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Few-shot segmentation (FSS) is a cutting-edge technology that can meet requirements using a small workload. With the development of China Aerospace Engineering, FSS plays a fundamental role in astronaut working scene (AWS) intelligent parsing. Although mainstream FSS methods have made considerable breakthroughs in natural data, they are not suitable for AWSs. AWSs are characterized by a similar foreground (FG) and background (BG), indistinguishable categories, and the strong influence of light, all of which place higher demands on FSS methods. We design a pixel-wise and class-wise network (PCNet) to match support and query features using pixel-wise and class-wise semantic cues. Specifically, PCNet extracts pixel-wise semantic information at each layer of the backbone using novel cross-attention. Dense prototypes are further utilized to extract class-wise semantic cues as a supplement. In addition, the deep prototype is distilled in reverse to the shallow layer to improve its quality. Furthermore, we customize a dataset for AWSs and conduct abundant experiments. The results indicate that PCNet outperforms the published best method by 4.34% and 5.15% in accuracy under one-shot and five-shot settings, respectively. Moreover, PCNet compares favorably with the traditional semantic segmentation model under the 13-shot setting. [ABSTRACT FROM AUTHOR]

Simple Summary: In the sorting process of Chinese softshell turtles, it is necessary to classify them based on their weight and accurately identify their plastron and carapace. This process requires heavy manual labor and complex mechanical processing methods. To improve processing efficiency and reduce costs, this article introduces machine vision technology, and a new image processing method is proposed. This method can estimate the weight of Chinese softshell turtles and accurately locate the positions of their plastron and carapace. The automation level of aquaculture can be greatly enhanced, and hardware costs can be reduced through software optimization through this approach. With the rapid development of the turtle breeding industry in China, the demand for automated turtle sorting is increasing. The automatic sorting of Chinese softshell turtles mainly consists of three parts: visual recognition, weight prediction, and individual sorting. This paper focuses on two aspects, i.e., visual recognition and weight prediction, and a novel method for the object detection and weight prediction of Chinese softshell turtles is proposed. In the individual sorting process, computer vision technology is used to estimate the weight of Chinese softshell turtles and classify them by weight. For the visual recognition of the body parts of Chinese softshell turtles, a color space model is proposed in this paper to separate the turtles from the background effectively. By applying multiple linear regression analysis for modeling, the relationship between the weight and morphological parameters of Chinese softshell turtles is obtained, which can be used to estimate the weight of turtles well. An improved deep learning object detection network is used to extract the features of the plastron and carapace of the Chinese softshell turtles, achieving excellent detection results. The mAP of the improved network reached 96.23%, which can meet the requirements for the accurate identification of the body parts of Chinese softshell turtles. [ABSTRACT FROM AUTHOR]

The number of excavated tunnels is increasing day by day, and the corresponding engineering scale is also getting increasing. Safe, efficient, and economically beneficial tunnel construction methods are indispensable in the process of crossing mountains and steep ridges in the southwest region. However, behind the improvement of transportation infrastructure in Southwest China is the support provided by the rapid development of blasting industry engineering technology in China. In the process of tunnel construction using the drilling and blasting method, in addition to blasting vibration disasters the phenomenon of overbreak and underbreak caused by blasting construction is a prominent problem. This phenomenon not only affects the safety and stability of the tunnel excavation but also seriously increases the construction cost. Based on a short mountain hard rock tunnel project in southwest China, this paper studies the effect of blasting construction on the blasting vibration of adjacent structures and the influence of tunnel contour forming quality. Through the monitoring and analysis of in situ blasting vibration, the Sadowski formula is used to study the attenuation law of blasting vibration velocity in different tunnel sites, which provides a theoretical basis for tunnel blasting vibration control. This article compares the use of overbreak and underbreak value with the traditional method to determine the degree of overbreak and underbreak. It introduces the analysis of contour section fractal dimension value and uses fractal theory in the Python image processing module to accurately and quantitatively describe the problems of tunnel overbreak and underbreak. The feasibility and accuracy of this method have been verified, by combining the total station and 3D laser scanner results of overbreak and underbreak measurements of the Brenner Base Tunnel and a short hard rock tunnel in a mountainous area of southwestern China. The blasting scheme was optimized from the aspects of cut hole form, detonator interval time, and peripheral hole charge structure, and the rationality of the optimized scheme was verified according to the on-site blasting experiments. It has a profound influence on strengthening the protection of adjacent tunnel structures and improving the economic benefit of mountain highway projects. [ABSTRACT FROM AUTHOR]

Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as a nascent approach. To address this, we design the LG-SWC-R3 model based on an attention mechanism to leverage its powerful learning capabilities. To enhance efficiency, we propose a simple yet effective encoder–decoder architecture (PVP-Transformer-ED) designed on the principle of eliminating redundant spatial information from images. This architecture involves masking a high proportion of soil images and predicting the original image from the unmasked area to aid the PVP-Transformer-ED in understanding the spatial information correlation of the soil image. Subsequently, we fine-tune the SWC recognition model on the pre-trained encoder of the PVP-Transformer-ED. Extensive experimental results demonstrate the excellent performance of our designed model (R2 = 0.950, RMSE = 1.351%, MAPE = 0.081, MAE = 1.369%), surpassing traditional models. Although this method involves processing only a small fraction of original image pixels (approximately 25%), which may impact model performance, it significantly reduces training time while maintaining model error within an acceptable range. Our study provides valuable references and insights for the popularization and application of image-based SWC recognition methods. [ABSTRACT FROM AUTHOR]

The creation of lightscapes in colleges and universities are of great significance in enhancing the perception of the campus environment, improving physical and mental health, and shaping humanistic connotations. This research aims to examine lightscape perceptions and impacts of different campuses. At Chongqing University A and B Campuses, lightscape walking experiments, subjective questionnaires, objective luminance measurements, and HDR picture processing were used to examine lightscape perception and factors. The relevance and differences in the perception of circadian lightscapes in the two campuses were analyzed using SPSS software. The study found: (1) natural lightscapes such as sky light, cloud shadow, and lake water reflection were the most popular during the daytime, while artificial lightscapes, such as decorative lighting of buildings, were positively evaluated at night; (2) the frequency of visits by a crowd directly impacts the ambiance of the environmental area; (3) males showed strong emotional awareness and social interaction skills in daytime, leading to increased social activity and stronger emotional responses, but no differences in nighttime; (4) optimal nighttime luminance enhances the overall perception satisfaction of the illumination; (5) the amount and arrangement of outdoor space, vegetation, minor landscape design, and service facilities all affect the perception of circadian lightscapes. In conclusion, design concepts and proposals of landscapes were suggested to optimize college and university lightscapes. [ABSTRACT FROM AUTHOR]

Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km2 nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution of crabs in the pond. However, this method is inefficient and lacks automation. To address the problem of efficient and rapid detection of crabs via automated systems based on machine vision in low-brightness underwater environments, a two-step color correction and improved dark channel prior underwater image processing approach for crab detection is proposed in this paper. Firstly, the parameters of the dark channel prior are optimized with guided filtering and quadtrees to solve the problems of blurred underwater images and artificial lighting. Then, the gray world assumption, the perfect reflection assumption, and a strong channel to compensate for the weak channel are applied to improve the pixels of red and blue channels, correct the color of the defogged image, optimize the visual effect of the image, and enrich the image information. Finally, ShuffleNetV2 is applied to optimize the target detection model to improve the model detection speed and real-time performance. The experimental results show that the proposed method has a detection rate of 90.78% and an average confidence level of 0.75. Compared with the improved YOLOv5s detection results of the original image, the detection rate of the proposed method is increased by 21.41%, and the average confidence level is increased by 47.06%, which meets a good standard. This approach could effectively build an underwater crab distribution map and provide scientific guidance for crab farming. [ABSTRACT FROM AUTHOR]

Gravity surveys constitute an important method for investigating the Earth's interior based on density contrasts related to Earth material differentials. Because lithology depends on the environment and the period of formation, there are generally clear boundaries between rocks with different lithologies. Inversions with convex functions for approximating the L 0 norm are used to detect boundaries in reconstructed models. Optimizations can easily be found because of the convex transformations; however, the volume of the reconstructed model depends on the weighting parameter and the density constraint rather than the model sparsity. To determine and adapt the modelling size, a novel non-convex framework for gravity inversion is proposed. The proposed optimization aims to directly reduce the L 0 norm of the density matrix. An improved iterative hard thresholding algorithm is developed to linearly reduce the L 0 penalty during the inner iteration. Accordingly, it is possible to determine the modelling scale during the iteration and achieve an expected scale for the reconstructed model. Both simple and complex model experiments demonstrate that the proposed method efficiently reconstructs models. In addition, granites formed during the Yanshanian and Indosinian periods in the Nanling region, China, are reconstructed according to the modelling size evaluated in agreement with the magnetotelluric profile and density statistics of rock samples. The known ores occur at the contact zones between the sedimentary rocks and the reconstructed Yanshanian granites. The ore-forming bodies, periods, and processes are identified, providing guidance for further deep resource exploration in the study area. [ABSTRACT FROM AUTHOR]

Background: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. Methods: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. Results: Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. Conclusions: Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing. [ABSTRACT FROM AUTHOR]

Coastline changes in estuarine areas can result from a combination of natural processes such as erosion, sedimentation, and sea-level rise, as well as human activities, including urbanization and infrastructure development. These changes have the potential to affect the local environment, including submarine groundwater discharge, wetlands, and navigation routes. The Yalu River Estuary (YRE), situated on the border between China and North Korea, has been experiencing significant changes in its coastline over recent years. This study aims to investigate the coastline dynamics in the YRE from 2000 to 2020. The study employs Landsat 5/7/8 satellite data and proposes a modified Normalized Difference Water Index (NDWI) to accurately delineate the coast boundary, particularly in areas with extensive tidal flats like the YRE. The research findings indicate that from 2010 to 2020, significant changes occurred in the YRE shoreline, with erosion being the dominant trend. Human activities and alternations in hydrological conditions are important factors affecting the YRE coastline changes, contributing to the formation of distinctive spatiotemporal patterns. An extreme flooding event in the year 2010 also altered the inner estuarine coastline in the YRE, indicating the impact of strong natural drivers. Findings from this study provide a comprehensive understanding of the evolving coastal environment, considering natural and anthropogenic drivers, and highlight the importance of continuous monitoring in a region of ecological and geopolitical significance. [ABSTRACT FROM AUTHOR]

Heat flow is a geothermal parameter for indicating the heat source distribution and evaluating geothermal reservoirs. Only 1,230 heat flow points are distributed unevenly in China, mainly concentrated in high‐temperature geothermal and southeast regions. The Songliao Basin is a potential geothermal field in China. Still, only 20 measurement points are known, making evaluating the geothermal genetic mechanism difficult. Sparse data interpolation using deep learning methods is highly accurate and widely used in fields such as image processing. In this work, we propose a deep neural network for predicting heat flow in the Songliao Basin. More than 4,000 global heat flows and 23 geological and geophysical parameters are used as reference constraints for training. The uncertainty error of the prediction is estimated based on the correlation and distance‐based generalized sensitivity analysis. The results show that the maximum heat flow is 85 mW/m2, the average is 67.1 mW/m2, and the error with the measured data is 10.64%. The previous geophysical and geological interpretation results indicate that the heat flow is higher in the west and lower in the east, with high anomalies in the central region, which may be related to the uplift of the deep mantle and the depression of the shallow low‐velocity sedimentary layer. Some high‐temperature melt bodies are in the deep layers, forming the current potential geothermal field. The measured data validates that the DNN is an effective method for predicting regional‐scale heat flow, providing reliable heat source information for evaluating geothermal resources. Plain Language Summary: Geothermal has received extensive global attention as a new renewable energy resource. Geothermal heat flow (GHF) is crucial among various indicators for assessing potential geothermal fields, with over 20,000 measurement points worldwide. However, due to the expensive cost of drilling measurements, the spatial distribution of heat flow points could be more balanced, posing challenges to geothermal exploration and investigation. Traditional heat flow prediction methods rely on interpolation based on sparse measurement points to estimate the regional heat flow distribution. However, spatial variations in the measurement points often affect the prediction results, leading to lower accuracy. Therefore, this study proposes a deep learning approach to establish the complex relationship between heat flow and geological/geophysical features, aiming to estimate the heat flow distribution in the target area. The proposed method has been successfully applied to heat flow prediction in the Songliao Basin, accompanied by an uncertainty analysis technique that provides confidence intervals for the prediction results. These findings offer direct evidence for the geothermal formation mechanisms and thermal evolution laws in the Songliao Basin. Key Points: We propose a deep neural network (DNN) method for predicting terrestrial heat flowCorrelation and Distance‐based Generalized Sensitivity Analysis (DGSA) methods are used to evaluate the uncertainty of DNN predicted heat flow resultsThe prediction of terrestrial heat flow in the Songliao Basin provides direct evidence for deep heat source channels and geothermal genesis mechanisms [ABSTRACT FROM AUTHOR]

Accurate near-surface velocity models are necessary for land seismic imaging. First-arrival traveltime tomography (FTT) routinely used for estimating near-surface velocity models may fail in geological complex areas. Supervised deep learning (SDL) is capable of building accurate velocity models, based on tens of thousands of velocity model-shot gathers training pairs. It takes lots of time and memory space, which may be unaffordable for practical applications. We propose integrating the FTT and SDL to build near-surface velocity models. During the neural network training, the FTT-inverted models rather than the original seismic data are used as the network inputs and corresponding true models are the outputs. The FTT-inverted and true models are the same physical quantities and with the same dimensions. Their relationship is less non-linear than that between shot gathers and true models. Thus, the neural network of the proposed method can be trained well using only a small number of training samples, dramatically reducing the time and memory costs. Numerical tests demonstrate the feasibility and effectiveness of the proposed method. We applied the proposed method to a land data set obtained in mountainous areas in the west of China and obtained satisfactory near-surface velocity models and stacking images. [ABSTRACT FROM AUTHOR]

To address the phenomenon of color shift and low contrast in underwater images caused by wavelength- and distance-related attenuation and scattering when light propagates in water, we propose a method based on an attention mechanism and adversarial autoencoder for enhancing underwater images. Firstly, the pixel and channel attention mechanisms are utilized to extract rich discriminative image information from multiple color spaces. Secondly, the above image information and the original image reverse medium transmittance map are feature-fused by a feature fusion module to enhance the network response to the image quality degradation region. Finally, the encoder learning is guided by the adversarial mechanism of the adversarial autoencoder, and the hidden space of the autoencoder is continuously approached to the hidden space of the pre-trained model. The results of the experimental images acquired from the Beihai Bay area of China on the HYSY-163 platform show that the average value of the Natural Image Quality Evaluator is reduced by 27.8%, the average value of the Underwater Color Image Quality Evaluation is improved by 28.8%, and the average values of the Structural Similarity and Peak Signal-to-Noise Ratio are improved by 35.7% and 42.8%, respectively, compared with the unprocessed real underwater images, and the enhanced underwater images have improved clarity and more realistic colors. In summary, our network can effectively improve the visibility of underwater target objects, especially the quality of images of submarine pipelines and marine organisms, and is expected to be applied in the future with underwater robots for pile legs of offshore wellhead platforms and large ship bottom sea life cleaning. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the capabilities of the inexpensive theodolite created by the authors, compared with modern commercial instruments, equipped with a Leica TPS1203 robotic total station with built-in GoPro Hero6 camera image sensors. This theodolite was tested in the laboratory where simulated dynamic displacements were determined. The results of the experimental tests showed that the displacement errors and differences between the simulated displacements of the test machine and those detected by the theodolite were in the range of Δ = − 0. 1 5 mm to +0.13 mm depending on the simulated amplitude. The theodolite was further used in the field for static and dynamic tests of the Wanzhou Railway Bridge, China. Determination of the dynamic motions of the bridge and the results of calculating the natural frequencies from the measurement data are presented. During loading tests of the bridge, the frequencies were also determined by accelerometers and these data were used as a reference to assess the accuracy of the theodolite and its suitability for dynamic tests. An original algorithm was developed in the MATLAB software environment to process the recorded videos, i.e. image processing to determine displacements and natural frequencies. It is demonstrated that the position of the theodolite at a distance of 28.5 m from the bridge provides the correspondence of one pixel of 0.333 mm, which provides a high level of accuracy when determining the dynamic vertical displacements of a moving target. It is established that the proposed theodolite will cost less than 5 000 euros. The results obtained are relevant for use in civil engineering for monitoring the amplitude vibrations of structures. [ABSTRACT FROM AUTHOR]

As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat's yield performance effectively. Nevertheless, it is still challenging to conduct large-scale phenotyping to analyse canopy-level wheat spikes and relevant performance traits, in the field and in an automated manner. Here, we present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones. The system includes the YOLACT-Plot model for plot segmentation, an optimised YOLOv7 model for quantifying the spike number per m² (SNpM²) trait, and performance-related trait analysis using spectral and texture features at the canopy level. Besides using our labelled dataset for model training, we also employed the Global Wheat Head Detection dataset to incorporate varietal features into the DL models, facilitating us to perform reliable yield-based analysis from hundreds of varieties selected from main wheat production regions in China. Finally, we employed the SNpM² and performance traits to develop a yield classification model using the Extreme Gradient Boosting (XGBoost) ensemble and obtained significant positive correlations between the computational analysis results and manual scoring, indicating the reliability of CropQuant-Air. To ensure that our work could reach wider researchers, we created a graphical user interface for CropQuant-Air, so that non-expert users could readily use our work. We believe that our work represents valuable advances in yield-based field phenotyping and phenotypic analysis, providing useful and reliable toolkits to enable breeders, researchers, growers, and farmers to assess crop-yield performance in a costeffective approach. [ABSTRACT FROM AUTHOR]

Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. [ABSTRACT FROM AUTHOR]

In 1982, a water-soluble aminoplastic resin embedding method for electron microscopy (EM) was developed to study lipids. It involves a silylation technique for processing cured resins by converting them from hydrophilic to hydrophobic status, facilitating ultra-thin sectioning. The technique offers better preservation of lipovitellin material in frog (Rana amurensis) yolk platelets, and it shows membrane ultrastructure better than traditional epoxy embedding methods. An early (1982) pseudo-colored TEM image processing technique applied to the micrographs obtained by EM of silylated aminoplastic thin sections revealed the lipovitellin molecules, in relation to the biosynthesis of new membranes in the first cleavage of the egg, with remarkable clarity and explains the lipovitellin molecules in relation to the biosynthesis of new membranes. Because the original article was written in Chinese and published in China before its open policy, many scientists may not be aware of the publication. Thus, in this short review, the essential components of the procedure are presented in English to help guide investigators who might be interested in the use of water-soluble embedding methods for lipid research. [ABSTRACT FROM AUTHOR]

Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement. [ABSTRACT FROM AUTHOR]

The ecological environment in the karst region of southwest China is fragile, and vegetation degradation is considered the main cause of this phenomenon. Vegetation growth is affected by preferential flow due to a large number of fissures and macropores in the soil. Therefore, the preferential flow and preferential paths in local typical vegetation need to be studied. The distribution and developmental characteristics of preferential flow and preferential paths in three typical local stands were obtained by conducting field staining tracer experiments and using image processing techniques. The results showed that there was preferential flow movement in the soil moisture of the local understorey and that there was a high degree of preferential flow development and high variability. The degree of preferential flow in several typical forests can be expressed, from highest to lowest, in the following order: secondary forests, Cupressus duclouxiana Hickel (Hickel., 1814) forest, and Pinus massoniana (Lamb., 1803) forest. The preferential paths in the soil were mainly distributed above 15 cm, and the number of preferential paths with a small influence radius was much greater than that with a large influence radius. The preferential paths with an influence radius of 2.5~5 mm accounted for the largest proportion. The total number of preferential paths in the P. massoniana forest was small. The preferential paths in the secondary and C. duclouxiana Hickel forests were similar in number, but the secondary forests had more preferential paths with a large influence radius. This study investigated the characteristics of preferential flow and preferential paths in the typical forests of the karst region in southwest China, and it can provide data and research directions for the study of hydrological processes in local forest vegetation. [ABSTRACT FROM AUTHOR]

In the operation of railway vehicles, the quality of bogies directly affects the operation quality and driving safety. Wheel set is one of the most important components in bogie, so the maintenance of wheel set is very important. For a long time, the detection of train wheel sets in China is still in the stage of manual measurement with backward technology and low efficiency. A new automatic detection method of wheel flange tread based on fuzzy neural network image processing technology is proposed in this paper. This method can accurately detect the defects of wheel flange tread. It collects the original image of the tested wheel set through the digital camera, inputs it into the computer, through certain calculation and processing, and compares it with the model established based on fuzzy neural network, so as to detect the defects of wheel flange and tread. First, the research status of wheel tread defect detection is summarized. Second, the basic principles of digital image technology are studied, the image processing models are confirmed, and the image processing method based on fuzzy neural network is established. Finally, eight wheel set treads are selected to carry out defect detection, and the analysis results show that the proposed method can obtain the better inspection precision. [ABSTRACT FROM AUTHOR]

Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could provide sufficient temporal and spatial information for lake management. These are especially critical for lake-rich regions where in situ monitoring data are scarce. This study demonstrated the implementation of an atmospheric correction algorithm (ACOLITE algorithm) in conjunction with the Google Earth Engine platform to generate remote-sensing reflectance products of specific points efficiently. The study also evaluated the performance of an algorithm for inverting lake SDs in Yunnan Plateau lakes, which is one of the five lake districts in China, since there is a lack of in situ data for most of the lakes in the region. The in situ data from four lakes with large SD ranges and imagery from Landsat Operational Land Imager were used to train and evaluate the performance of two algorithms: an empirical algorithm (stepwise regression) and machine learning (support vector machines and multi-layer perception). The results revealed that the retrieval accuracy of models with bands and band ratio combinations could be substantially improved compared with models with a single band or band combinations. A negative correlation was also observed between the temporal match between observations and the model accuracy. This study found that the MLP model with sufficient training data was more suitable for transparency estimation of lakes belonging to the dataset; the SVM model was more suitable for transparency prediction outside the training set, regardless of the adequacy of the training data. This study provides a reference for monitoring lakes within the Yunnan region using remote sensing. [ABSTRACT FROM AUTHOR]

The Earth observation system heavily relies on sophisticated remotely sensed satellites, an important means to obtain global high-precision geospatial products and an important strategic area for the world's major scientific and technological powers to develop. Although China's satellites currently have real-time or quasi-real-time observations with a high temporal resolution, there are still a lot of gaps between their positioning accuracy and the world's advanced level. This essay aims to study an efficient ground image processing technology and apply it to high-resolution satellite remote sensing images. The convolutional neural network is an efficient deep learning method for image recognition and feature extraction. In this essay, people use a convolutional neural network (CNN) to identify ground images, use a support vector machine (SVM) to classify and summarize images, and then use a Kalman filter for noise reduction, so as to obtain sophisticated remotely sensed images. In the experiment, 100 satellite remote sensing images in the GeoImageDB database were selected for the simulation test, the images were divided into 5 types, and their recognition accuracy, classification accuracy, image signal-to-noise ratio, and resolution were analyzed. The results show that the accuracy of CNN's recognition of different types of images is up to about 94%, and the lowest is about 85%. The accuracy of the SVM for image classification is above 80%, and the highest is about 95%. The SNR of the image after noise reduction is basically above 6.5, and some even reach above 8.0. The resolution of the image is basically above 800ppi, and the highest even reaches an ultra-high resolution of 1400ppi. Overall, the processed images are of high quality. This shows that this essay uses CNN for image recognition and then uses an SVM for classification, and finally, the method of denoising the image has certain feasibility and has achieved good results through experiments. [ABSTRACT FROM AUTHOR]

Corn is one of the main food crops in China, and its area ranks in the top three in the world. However, the corn leaf disease has seriously affected the yield and quality of corn. To quickly and accurately identify corn leaf diseases, taking timely and effective treatment to reduce the loss of corn yield. We proposed identifying corn leaf diseases using the Mobilenetv3 (CD-Mobilenetv3) model. Based on the Mobilenetv3 model, we replaced the model's cross-entropy loss function with a bias loss function to improve accuracy. Replaced the model's squeeze and excitation (SE) module with the efficient channel attention (ECA) module to reduce parameters. Introduced the cross-layer connections between Mobile modules to utilize features synthetically. Then we Introduced the dilated convolutions in the model to increase the receptive field. We integrated a hybrid open-source corn leaf disease dataset (CLDD). The test results on CLDD showed the accuracy reached 98.23%, the precision reached 98.26%, the recall reached 98.26%, and the F1 score reached 98.26%. The test results are improved compared to the classic deep learning (DL) models ResNet50, ResNet101, ShuffleNet_x2, VGG16, SqueezeNet, InceptionNetv3, etc. The loss value was 0.0285, and the parameters were lower than most contrasting models. The experimental results verified the validity of the CD-Mobilenetv3 model in the identification of corn leaf diseases. It provides adequate technical support for the timely control of corn leaf diseases. [ABSTRACT FROM AUTHOR]

This study investigated the visual harmony of an urban stream considering changes to the ratio of water to greenery on the riverbed. The Baxi stream, a third-order stream in Yongan City, Fujian Province, China was selected as the study site. The stream reach is disturbed by several hydraulic structures, such as restricted water flow by a vertical revetment and water level regulation by submerged dams. Images of the river were captured, and image processing was performed to change the proportion of water and greenery, and the proportions of various landscape elements in the image were calculated. Based on the statistical analysis of survey results, cognitive indicators (vividness and naturalness) associated with harmony and preference, and the relationship between harmony or preference and landscape elements, were established. Landscape elements included ratios of visible water (WR), visible greenery (GR), visible buildings, and visible infrastructure. The results demonstrated that visual preference, P, is positively correlated with harmony, H, vividness, V, and naturalness, N. In particular, H is almost consistent to P. The proportion of visible water and greenery had a significant impact on the H and P of the stream landscape. When the ratio of WR to GR was approximately 0.8, H was optimal, and the public's P was high. These results can be used to improve and enhance the visual landscape quality of this stream reach. The methodology proposed in this study could provide other study areas with a reference for how to obtain the best visual harmony or achieve public acceptance by changing the amount of visible water and/or greenery. [ABSTRACT FROM AUTHOR]

Unconventional shale reservoirs and typical fine-grained rocks exhibit complicated, oriented features at various scales. Due to the complex geometry, combination and arrangement of grains, as well as the substantial heterogeneity of shale, it is challenging to analyze the oriented structures of shale accurately. In this study, we propose a model that combines both multifractal and structural entropy theory to determine the oriented structures of shale. First, we perform FE–SEM experiments to specify the microstructural characteristics of shale. Next, the shape, size and orientation parameters of the grains and pores are identified via image processing. Then fractal dimensions of grain flatness, grain alignment and pore orientation are calculated and substituted into the structural entropy equation to obtain the structure-oriented entropy model. Lastly, the proposed model is applied to study the orientation characteristic of the Yan-Chang #7 Shale Formation in Ordos Basin, China. A total of 1470 SEM images of 20 shale samples is analyzed to calculate the structure-oriented entropy (SOE) of Yan-Chang #7 Shale, whose values range from 0.78 to 0.96. The grains exhibit directional arrangement (SOE ≥ 0.85) but are randomly distributed (SOE < 0.85). Calculations of samples with different compositions show that clay and organic matters are two major governing factors for the directivity of shale. The grain alignment pattern diagram analyses reveal three types of orientation structures: fusiform, spider-like and eggette-like. The proposed model can quantitatively evaluate the oriented structure of shale, which helps better understand the intrinsic characteristics of shale and thereby assists the successful exploitation of shale resources. [ABSTRACT FROM AUTHOR]

Dance sport is a competition of athletes for qualities such as speed, strength, endurance, flexibility, and coordination, requiring a high level of overall physical fitness. As China's sports fever continues to heat up, especially among students, the active nature of young people can lead to overenthusiasm for sports, dance, and other sports, which can lead to various physical discomforts during sports. Especially in the current sports habits of students, there is a great hidden danger of sports injuries. It is mainly due to excessive exercise, irregular exercise methods, and incomplete sports protective equipment. On this basis, this study proposes a model to further assess the health status of individuals and groups from the rate of energy consumption during exercise by building a real-time motion detection system. The development of sports health assessment systems, including the application of imaging techniques, is also analyzed. The experimental results in this article show that when the error threshold is set to 10%, the accuracy of the model is 85%, and the obtained results are ideal. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of tunnel construction in China, the length of tunnels has continued to increase, and the consequent tunnel disease detection has attracted more and more attention from maintenance departments. Among many diseases, lining cracks are the most common, which directly reflect the stress of the lining, which is very important for the study of tunnel diseases. In view of the current detection status and detection requirements, this article has carried out research work on a vehicle-mounted tunnel lining crack detection system based on image processing. Due to the grayscale difference between the cracks on the lining surface and the lining background, these differences lead to significant crack edge features and relatively stable detection. Therefore, this article designs an intelligent edge algorithm system for cracks on the lining surface to detect the edges of the image, extract the edges of cracks, and remove useless interference information in the lining background. The experiment proves that the paired sample t-test can find that after the experiment is over, the P value of different edge detection operators for global threshold segmentation is less than 0.05, which has a significant difference. The Canny, Deriche, and Lanser filters are relatively strong, and the extracted crack edge noise is relatively small. Finally, the parameter values of the crack image are calculated, and the calculated values of the crack parameters provide a scientific and reliable basis for tunnel safety evaluation. [ABSTRACT FROM AUTHOR]

GF-3 is the first C-band multipolarization synthetic aperture radar (SAR) satellite in China. The SAR system is equipped with an active phased array antenna to electronically generate various antenna beams. The accuracy of SAR antenna patterns is of main importance for precise SAR image processing. The traditional method for antenna pattern measurement is the near-field method but it is unable to satisfy the demand for fast measuring in-orbit. In order to measure the SAR antenna pattern quickly with high accuracy, we propose the internal calibration method. The internal calibration uses calibration signals which are routed along the nominal signal path in SAR systems, thus monitoring the gain and phase variations for each T/R channel is possible. This paper will focus on this method, including its principle, operation steps, and results, compared to the near-field method. The internal calibration method provides a valuable solution for SAR antenna pattern measurement. [ABSTRACT FROM AUTHOR]

The product design major was formally established in 2011 and has been an important part of Chinese art education since then. At the same time, it has laid a solid foundation for the development of China's higher education. Therefore, the product design major occupies an important position in China's higher education and plays an irreplaceable role in cultivating industrial design talents for the country. With the continuous development and progress of national education, the teaching of product design is also constantly innovating, in order to better improve the teaching effect of product design and cultivate more product design professionals for the country. The purpose of this paper is to study the teaching of product design major based on intelligent network teaching, establish an intelligent network teaching system, and carry out intelligent network teaching experiment of product design major based on this system. The experiment concluded that the overall satisfaction of the students majoring in product design on the intelligent network teaching reached 82%, and the intelligent network teaching mode also increased the outstanding performance rate of the students majoring in product design by 30%. [ABSTRACT FROM AUTHOR]

In the cold season, wine aids in maintaining body temperature and is advised for military officers. This paper proposes a study on the multimotor drive control method of the upper-retort-robot based on machine vision for wine brewing automation to meet the demand of military areas located in cold regions, as wine is recommended to keep soldiers' body temperatures normal in China's extremely cold regions. Based on machine vision, the target is converted into an image signal by an image pickup device and is sent to the image processing system. Pixel distribution, brightness, color, and other data are transformed to digital signals, and target attributes are retrieved to control the field equipment's operations. The Monte-Carlo approach is used to generate joint variables at random within each joint's fluctuation range. The positive aspects of kinematics model are utilized and the working space of the upper-retort-robot is calculated using multimotor drive control method. The multimotor drive compensates the harmonic ripple torque and establishes the fault-tolerant automatic control of the system to maintain quality of the liquor. The results of the experiments reveal that the robot arm can reach any place within the barrel's set range. To control the quality of the liquor, the robot will function in an automatic manner. The robot's transmission performance is capable of meeting the requirements for automated liquor quality control during the production of wine from grapes. The results show that the suggested multimotor drive control (MMDCM) approach is robust and viable in terms of robot transmission performance and dexterity. [ABSTRACT FROM AUTHOR]

A shale lithofacies scheme is commonly used to characterize source rock reservoirs of the Lower Cambrian Niutitang Formation. However, this classification ignores that individual components such as quartz may have different origins, potentially affecting reservoir quality. The main objective of this article is, therefore, to present a refined scheme for lithofacies and an image processing workflow for the detection of quartz types in the Niutitang Formation shales from the Jiumen outcrop in the Guizhou Province (Upper Yangtze Basin, SW China). In order to do so, a combination of bulk density, optical and scanning electron microscopy and image analysis was used. The shale lithology was macroscopically classified into seven major categories and nineteen subcategories. Subsequently, the shales were investigated at the microscopic level, mainly focusing on quartz types and microstructural variations. Afterwards, the workflow to calculate the weight per unit volume (1 cm3) of the quartz types was presented, i.e., firstly, by calculating the weight of mineral matter by subtraction of the measured weight of organic matter from the bulk shale; secondly, by calculating the weight of total quartz in bulk shale from the weight of mineral matter and its proportion calculated from X-ray diffraction data; thirdly, by calculating the weight of detrital quartz and non-detrital quartz with energy dispersive X-ray mapping, image processing and quartz density; finally, by calculating the weight of clay-sized quartz by subtracting of the weight of detrital and non-detrital quartz from the weight of the total quartz. The bulk quartz content was found to be dominated by clay-sized quartz, which may mainly control the mesopore volume available for gas storage and, hence, the shale gas reservoir development. [ABSTRACT FROM AUTHOR]

One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly (Bemisia tabaci) and thrips (Frankliniella occidentalis) are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one. To alleviate this situation, this study proposed an automated detection approach to meet the need for continuous monitoring of pests in greenhouse conditions. Candidate targets were firstly located using a spectral residual model and then different color features were extracted. Ultimately, Whitefly and thrips were identified using a support vector machine classifier with an accuracy of 93.9 and 89.9%, a true positive rate of 93.1 and 80.1%, and a false positive rate of 9.9 and 12.3%, respectively. Identification performance was further tested via comparison between manual and automatic counting with a coefficient of determination, R 2, of 0.9785 and 0.9582. The results show that the proposed method can provide a comparable performance with previous handcrafted feature-based methods, furthermore, it does not require the support of high-performance hardware compare with deep learning-based method. This study demonstrates the potential of developing a vision-based identification system to facilitate rapid gathering of information pertaining to numbers of small-sized pests in greenhouse agriculture and make a reliable estimation of overall population density. [ABSTRACT FROM AUTHOR]

Different satellite images have different positioning accuracy. For example, stereo satellite images have higher positioning accuracy than resource survey satellite images. In addition, for a large number of non stereo satellite images, due to the inability to build a strong triangulation model, it is impossible to carry out block adjustment alone to improve the image positioning accuracy. High precision and high resolution Orthophoto Images are the basis of resource investigation and monitoring and basic geographic information updating. For example, China's third national land survey, national geographic situation monitoring and other national projects require that the survey base map must reach the accuracy of 1:10000 scale, that is, the mean square plane error of points is less than 5m. For most satellite images, a certain number of ground control points need to be deployed to achieve this accuracy. Due to the difficulty of obtaining high-precision ground control points and DEM data in difficult areas, high-precision mapping has always been an unsolved problem, such as Western China. In addition, due to the limited coverage of a single satellite image, to realize the complete coverage of an image in a large area requires the joint application of multiple satellite images. In this paper, the high-precision collaborative geometric processing model and technical method of high-resolution multi-source remote sensing satellite images are proposed. The high-precision collaborative geometric processing of more than ten kinds of high-resolution domestic satellite images is completed by integrating multi-source observation data. An automatic construction method of large-scale block adjustment model of remote sensing images from domestic satellites based on multivariate generalized control network is proposed, including key technologies such as automatic optimization of optimal tie points under different modes, automatic matching of multi-node parallelization tie points, multi-level gross error elimination and so on, which realizes the automatic and stable construction of aerial triangulation model. The test shows that the positioning accuracy of satellite images is better than the accuracy requirements of 1:10000 scale without ground control points, which solves the problem of geometric positioning of 1:10000 scale accuracy in areas where it is difficult to obtain ground control points in the field of Western China. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

With the gradual improvement of the quality of life, taste, and ecological and environmental awareness of urban residents in China, the environmental landscape of residential areas has gradually become a hot spot. At present, the level of the residential environmental landscape has become a necessary means for real estate developers to publicize products and improve economic benefits. Although many residential areas have invested a high cost in constructing environmental landscapes, there are always some deficiencies and defects in the design and implementation of environmental landscapes in residential areas due to various reasons. Therefore, to ameliorate the low efficiency and high cost of manual processing of landscape images, a Fully Convolutional Network (FCN) model based on the traditional Convolutional Neural Network (CNN) is designed for semantic segmentation of landscape images to deal with the excessive amount of landscape elements in landscape image processing. The deconvolution method is utilized to realize pixel-level semantic segmentation. Besides, the image preprocessing method enhances the data to prevent overfitting from commonly occurring in FCN. Moreover, the model two-stage training method ameliorates long training time and complex convergence in deep learning. Finally, three upsampling network structures, i.e., FCN-32s, FCN-16s, and FCN-8s, are selected for a comparative experiment to determine the most suitable network. The experimental results demonstrate that the FCN-8s upsampling network structure is the most prominent; it attains a pixel accuracy of more than 90%, an average accuracy of 88%, and an average Image Understanding of 75%. The three values are the highest among the three upsampling structures, indicating that the FCN-8s can realize accurate landscape image processing. Besides, the recognition accuracy of FCN for landscape elements reaches 90%, 25% higher than that of CNN. This method is effective and accurate in classifying landscape elements, improves the classification accuracy intelligently, and significantly reduces the cost of landscape element classification, which is feasible. [ABSTRACT FROM AUTHOR]

The current era is an information age, and society is turning to the information age. The image processing technology is also widely used in various fields, and the technology of sports action recognition based on image processing technology can also be said to be appropriate. This article uses a spatial visual feature analysis algorithm to implement it. To implement this algorithm, a series of work such as image collection, feature extraction, and action recognition must be completed first and then implemented through texture functions and other related functions. This algorithm can be used to complete the image-based sports action recognition technology at the minimum time cost. This algorithm can help sportsmen better complete training and standardize movements to a certain extent. As for the development of China's current sports industry structure, it is also steadily improving. The people's love for sports is getting stronger and stronger, which also makes the development of China's sports industry still benefit a lot. [ABSTRACT FROM AUTHOR]

Since the founding of the People's Republic of China, the advantages of logistics are neglected, and the scale operation and the welfare in the industry are difficult to achieve due to the influence of the economic system and social environment. Therefore, a new intelligent logistics distribution management system based on machine vision and visual sensor image processing technology is constructed to respond to the shortcomings of the traditional system, including slow efficiency, huge cost, complex data, and low degree of informatization. Through the analysis and research on the visual sensor image processing technology and the order processing, receipt management, distribution management, scheduling management, and return management that affect logistics distribution, a simulation experiment is used to verify that the visual sensor image processing technology is rigorous, intelligent, and efficient and has high precision. The intelligent logistics distribution management system can effectively solve the problems existing in the traditional logistics distribution management. The experimental results show that the visual sensor image processing technology can collect and analyze the target image and effectively track and monitor it in the logistics distribution process. The average distribution precision of the intelligent logistics distribution management system reaches more than 99.5%, which is greatly improved compared with 90% of the traditional logistics distribution. And it can greatly improve the distribution efficiency, which increases by about 26.5%. The study realizes the information management of the logistics system and automatically completes all the work according to the designed program, so that the real-time dynamic distribution can be transmitted to the urban logistics distribution at any time. [ABSTRACT FROM AUTHOR]

The TVPS146's digital video processor is used for digital video processing and image output on digital signal processing (DSP) ports. The following is a summary of the field programmable gate array (FPGA) features and programming principles. FPGA is designed by the ping-pong operation, including serial to parallel conversion design, pipeline operation, and data interface synchronization. A dance video screenshot analysis microsystem is composed of it and video dynamic image processing. Finally, according to the number of dance culture performance institutions based on the internet from 2014 to 2018, the development strategy of dance culture and creativity based on the internet is put forward. The results show that the image sharpness of the video screenshots processed by the system is improved, and the effect of enhanced processing is achieved compared to the unprocessed video screenshots. In addition, after the video screenshot processed by the system is compressed, some details are lost and the outline of the image is blurred. The combination of internet immediacy, mass, grassroots, and interactivity can accelerate the development of China's dance culture and creative industry and provide an effective practical foundation for innovation in the development of art and culture. [ABSTRACT FROM AUTHOR]

In order to increase the recycling of phosphogypsum waste, this study explored the feasibility of using phosphogypsum to replace some of the lime and aggregate in the lime-fly ash-crushed stone mixture which is a widely used road base material in China. For this purpose, compaction, compressive strength, composition structures, wetting-drying cycle tests, and shrinkage tests were carried out on the lime-fly ash-phosphogypsum-crushed stone composite to investigate its performance. The results indicate that lime-fly ash-crushed stone modified with phosphogypsum has the required strength of the road base material and favourable performances in environment (wetting-drying cycle) stability. The image processing analysis and shrinkage tests demonstrated that phosphogypsum can significantly improve the compactness and shrinkage performance of lime-fly ash-crushed stone mixture. A suitable content of phosphogypsum and a reasonable content of fine aggregate are conducive to improving the roadway engineering properties (i.e., decreasing shrinkage cracks and increasing compressive strength) of lime-fly ash-phosphogypsum-crushed stone composites. [ABSTRACT FROM AUTHOR]

In recent years, with the growth of China's economy and the development of the automobile manufacturing industry, the number of various vehicles has continuously increased, and the incidence of traffic accidents has also increased. Especially in traffic blind areas, right-turning areas of vehicles, etc., traffic accidents such as vehicle collisions are extremely easy to occur, which poses a serious threat to people's lives and property, and is extremely harmful. Therefore, related research on collision detection of people and vehicles has been traffic-safe and has received extensive attention from field researchers. At present, the research on human-vehicle collision detection is to detect human-vehicle collision accidents by tracking the track of vehicles and pedestrians, but there are problems such as poor tracking effect, low accuracy of collision discrimination and complex algorithms. Aiming at these problems, this paper studies the human-vehicle collision detection algorithm based on image processing. Through the image processing of traffic monitoring video, the vehicle and pedestrian contour information is extracted. Based on this, a mathematical model for collision detection is constructed to realize human-vehicle collision detection. The results show that the proposed method can effectively distinguish the collision between pedestrians and vehicles, and the algorithm for image processing is simpler than the traditional tracking algorithm, and the time is shorter. The results show that the image-based collision detection algorithm based on image processing can effectively and quickly identify the traffic accidents in which people and vehicles collide, and then can issue alarm signals in time, shortening the accident processing time and reducing the accident time. The possibility of a secondary accident has a high practicability in the detection of traffic accidents in which people and vehicles collide. [ABSTRACT FROM AUTHOR]

• The all-sky airglow imager and Digisonde data were used to investigate plasma bubbles. • Optical image processing was performed to obtain image products with high resolution. • Plasma bubbles appeared like long stripe and bifurcated vertically. • The distance between adjacent plasma bubbles was in the range of 100–400 km. • Plasma bubbles generally drifted eastward and the average drift velocity was 90 m/s. Ionospheric plasma bubbles and scintillation phenomena often occur at low latitudes in China. The all-sky airglow imager located at Fuke station in Hainan (19.5°N, 109.2°E) is part of the Meridian project in China. In this work, we used mainly the observation data obtained by the imager and a digisonde to study plasma bubbles. Optical image processing—including image enhancement, azimuth correction and image projection—was performed on the original airglow observation images, and clear high-resolution images of the bubbles were obtained. Based on these, the morphological features, spatial and temporal variations, spatial scale, drift velocity of the plasma bubbles in the F region of the ionosphere were investigated and analyzed. The plasma bubbles appeared as long, dark stripes on the bright airglow emission background. Their large-scale structures often bifurcated vertically, and were usually tilted westward with north-south extension along the magnetic field lines. The east-west scale of the plasma bubbles was usually greater than 200 km and these increased gradually with time. Moreover, the distance between the adjacent plasma bubbles also increased over time, and was in the 100–400 km range. Plasma bubbles generally drifted eastward with an average drift velocity of approximately 90 m/s. The occurrence and duration of the plasma bubbles were compared with digisonde observations, and it was shown that plasma bubbles lasted for nearly four hours. Additionally, the main characteristics of the plasma bubbles were obtained using both optical and radio observations and the study of which can be beneficial for monitoring and predicting ionospheric scintillations. [ABSTRACT FROM AUTHOR]

China has been the world’s largest automotive manufacturing country since 2008. The automotive wheel industry in China has been growing steadily in pace with the automobile industry. Visual recognition system that automatically classifies wheel types is a key component in the wheel production line. Traditional recognition methods are mainly based on extracted feature matching. Their accuracy, robustness, and processing speed are often compromised considerably in actual production. To overcome this problem, we proposed a convolutional neural network approach to adaptively classify wheel types in actual production lines with a complex visual background. The essential steps to achieve wheel identification include image acquisition, image preprocessing, and classification. The image differencing algorithm and histogram technique are developed on acquired wheel images to remove track disturbances. The wheel images after image processing were organized into training and test sets. This approach improved the residual network model ResNet-18 and then evaluated this model based on the wheel test data. Experiments showed that this method can obtain an accuracy over 98% on nearly 70,000 wheel images and its single image processing time can reach millisecond level. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle is a typical field robot which can work in many unstructured environments like mines, forests, and even radiation areas. In our mine monitoring system built in a northeast province of China, special designed unmanned aerial vehicle is applied to take photos and perceive the environment. We select a series of image-based techniques to process aerial pictures to monitor the slope. The visual features are initially refined by histogram equalization. Then, the rocks and cracks can be detected by different digital image processing operators, like Canny, so as to assess displacements. Advanced semantic segmentation model, U-Net, is also selected to process the problem. Experimental results show that both Canny and U-Net can perceive the edges in pictures effectively, better than other operators. In addition, we model the inspection mission for mine slopes into a traveling salesman problem, then plan the path for unmanned aerial vehicle by swarm intelligence-based optimization. [ABSTRACT FROM AUTHOR]

Potholes are the most common form of distress on cement concrete pavements, which can compromise pavement safety and ridability. Thus, timely and accurate pothole detection is an important task in developing proper maintenance strategies and ensuring driving safety. This paper proposes a method of integrating the processing of grayscale and texture features. This method mainly combines industrial camera to realize rapid and accurate detection of pothole. Image processing techniques including texture filters, image grayscale, morphology, and extraction of the maximum connected domain are used synergistically to extract useful features from digital images. A machine learning model based on the library for support vector machine (LIBSVM) is constructed to distinguish potholes from longitudinal cracks, transverse cracks, and complex cracks. The method is validated using data collected from agricultural and pastoral areas of Inner Mongolia, China. The comprehensive experiments for recognition of potholes show that the recall, precision, and F1-Score achieved are 100%, 97.4%, and 98.7%, respectively. In addition, the overlap rate between the extracted pothole region and the original image is estimated. Images with an overlap rate greater than 90% accounted for 76.8% of the total image, and images with an overlap rate greater than 80% accounted for 94% of the total image. A comparison discloses that the proposed approach is superior to the existing method not only from the perspective of the accuracy of pothole detection but also from the perspective of the segmentation effect and processing efficiency. [ABSTRACT FROM AUTHOR]

In China, traditional techniques for measuring structural subsidence cannot keep pace with the rapid development of critical national infrastructure such as the growing network of highspeed railways. Traditional monitoring methods using leveling instruments are inefficient and time consuming when monitoring structures like bridges and tunnels. Thus, a fast, economical, and more accurate and precise way to survey building subsidence is urgently needed to address this problem. This paper introduces a new close-range photogrammetry technique that deploys a fixed camera with tilt compensator to measure changes in height over small areas. A barcode subsidence mark that can be identified automatically during digital image processing replaces the leveling points used in traditional methods. Four experiments at different locations verified that results from the new method were stable and consistent with total station measurements. This approach is simple, inexpensive, and produces accurate and precise results as our evaluation results show. [ABSTRACT FROM AUTHOR]

Global warming has caused severe regional water security risks, and from the theory of the hydrological cycle, the daily extreme temperature could also bring an impact on the streamflow volume, which could be even more important than the average temperature. Therefore, based on the level of the maximum or minimum temperature warming scenarios, a variety of meteorological datasets were selected to assess the asymmetric effect of increased daily extreme temperature on the streamflow from a multi-scale perspective by using the Variable Infiltration Capacity (VIC) model. Model simulations indicate that the streamflow experiences more significant changes in response to the maximum temperature than in response to the minimum temperature, and the relationships of streamflow with both the maximum and minimum temperatures show an upwards parabolic response function, but the response function varies with the type of warming. Additionally, the seasonal and monthly duration curves results show that the increases in both the maximum and minimum temperatures demonstrate a similar response that leads the proportion of the flood period streamflow to be increased (Tmin : 0.16–0.53%/°C; Tmax : 0.11–0.51%/°C). When the minimum temperature increases, the higher the original temperature in the region, the greater the proportion of the flood period streamflow increases. However, when the maximum temperature increases, the opposite effect occurs. • This paper included a numerical analysis of the sensitivity of streamflow to daily extreme temperature increases. • This paper discusses in detail the regional asymmetric effect of daily extreme temperature increase to streamflow. • A relatively complete picture of the hydrological processes caused by daily extreme temperature rise is presented. [ABSTRACT FROM AUTHOR]

Monitoring of subgrade settlement in railway and highway helps to maintain traffic safety and reduce infrastructure losses. When an excess subgrade settlement is detected, maintenances of the unqualified subgrade will be processed in time, and early warning of constructions will be processed to prevent the occurrence of safety accident. In this article, a noncontact, real-time, and unattended system based on optical method is introduced to monitor the subgrade settlement remotely. Several high-power infrared LED targets are mounted on the subgrade and they will move as the subgrade subsides. The movements of the LED targets are detected by an optical detection device based on the principle of optical imaging. The displacement data is remotely transmitted to a server for analysis via GPRS. The remote monitoring system has been applied to the monitoring of subgrade settlement of Zhensheng highway in Guizhou province of China in 2014. This technology helped to detect the subgrade settlement that was caused by the construction of Beiyinpo railway tunnel. Four test points on road surface were monitored at a sampling rate every 20 minutes. The effectiveness of the system has been verified by the practical application. [ABSTRACT FROM AUTHOR]

GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images. [ABSTRACT FROM AUTHOR]

The Southern Tianshan area is one of the most important gold belts identified by many world‐class, super large and large gold deposits such as Muruntau (Uzbekistan), Kumtor (Kyrgyzstan), and Jilau (Tajikistan). Some medium‐ to small‐scale gold deposits, such as Sawayaerdun and Bulong, have been discovered and reported in recent years at the China part of the belt. The study area, named the Wushitala area, is located in the eastern part of Southern Tianshan, and it has a strong potential for gold and other metallic mineral deposits. This study utilizes various image processing techniques, including false colour composite, band ratios, and matched filtering, to process Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and map the distribution of hydrothermal minerals (e.g., muscovite, kaolinite, chlorite, epidote, goethite, and malachite) related to the known deposits in the Wushitala area. The identified alteration zones are coincident with the known gold and copper deposits and field samples from the study area. The distribution of the alteration zones also indicates that the acid intrusions and regional structures play an important role in focusing the mineralizing fluids. The results show that ASTER data accompanied with image processing methods and reference spectra (e.g., JPL, lab, or field measured) could be an effective technique for mapping hydrothermal alteration zones in areas with no dominant vegetation cover. Due to the extensively distributed acid intrusions and structures along the Southern Tianshan Belt, the mineral prospecting methodology is suggested for application in similar geological settings in the belt. [ABSTRACT FROM AUTHOR]

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring. [ABSTRACT FROM AUTHOR]