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Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drought stress of crops is an important factor affecting their yield and sustainable agricultural development. Accurate diagnosis of crop drought stress is the basis for improving water resource utilization. Imaging technology can quickly, automatically, non-destructively, accurately acquire and analyze the phenotype characteristics of crops, providing a powerful new tool for crop science research. This paper focuses on the review of phenotype imaging analysis techniques for crop drought stress diagnosis. First, the single imaging technique for crop drought stress diagnosis was introduced, and then we introduced the fusion imaging technique for crop drought stress diagnosis. In the aspect of single imaging technology introduction, firstly, the principles of six phenotype imaging techniques including RGB imaging, 3D imaging, near-infrared imaging, hyperspectral imaging, chlorophyll fluorescence imaging and thermal imaging are introduced in this paper, and then we introduced research progress of the single imaging technology in crop drought stress phenotype analysis, beside the research achievements in crop drought stress in recent years were further summarized. Finally, the imaging technology was summarized and prospected at the end of each imaging technology introduction. In the fusion imaging technology of crop drought stress, this paper first summarized the research results of the automatic comprehensive phenotype imaging analysis platform of crop drought stress in recent years, and then summarized and analyzed the research of different fusion imaging methods, analyzed their advantages and disadvantages, and prospected the future research direction of fusion imaging technology in the end. With the horizontal and vertical comparative analysis of the research results of a single imaging technology, we found that RGB imaging technology has the lowest application cost and the most extensive application range, but the lowest accuracy. The 3D imaging solves the problem of crop occlusion in RGB imaging, improves the accuracy, and is widely used in high-throughput phenotype extraction platforms. The information on crop phenotype parameters can be obtained by near-infrared spectroscopy, chlorophyll fluorescence and hyperspectral imaging in a fast and non-destructive way. Among them, the application scope of NIR imaging is limited due to its limited ability to obtain phenotype information and relatively high cost. Chlorophyll fluorescence and hyperspectral imaging are better at obtaining physiological and biochemical parameters of crops, and are widely used in the fusion of imaging methods. Thermal images obtained by infrared thermal imaging are often used to obtain crop physiological parameters, and are also combined with visible light images for phenotype extraction. In the meantime, the method of obtaining crop phenotype by using the fusion of multiple imaging technologies has the advantages of different imaging technologies, which can effectively avoid the defects of a single imaging technology and make up for the deficiencies of obtaining single imaging phenotype parameters, so as to reflect the crop growth status more accurately and efficiently. More accurate feature extraction can be achieved by integrating various image information obtained by various imaging technologies and using artificial intelligence methods for integrated image processing. The use of fusion imaging technology to obtain crop phenotypes will be one of the important directions of crop drought stress phenotypes extraction in the future. Finally, according to the current development situation, future research on phenotypic imaging technology for crop drought stress diagnosis prospects, including the development of new devices and the combination of new artificial intelligence algorithms. [ABSTRACT FROM AUTHOR]

Remote sensing image change detection is an important research in the field of remote sensing, which aims to use remote sensing technology and image processing methods to identify the patterns and trends of surface cover changes. In order to gain a deeper understanding of the current development of this area and the technical methods used, a large amount of information and literatures are summarized and analyzed to provide a more comprehensive review of remote sensing image change detection methods. Firstly, the concept and processing flow of change detection are introduced. Then the classification system of change detection methods is summarized from six angles, followed by a review of their development history. Subsequently, the principles and characteristics of various types of change detection methods are outlined, their advantages and disadvantages are briefly analyzed, and the real-world application value of change detection on remotely sensed images is discussed from six aspects. Some problems and shortcomings in the area are briefly analyzed, and some possible ways to improve these problems are proposed, while the obstacles that may be encountered in the practical application of these methods are also predicted. Finally, the change detection methods are summarized, and the future development direction is prospected, in order to better understand the research status and development trend of remote sensing image change detection methods, and provide reference for further research. [ABSTRACT FROM AUTHOR]

With the development of machine vision technology, the workpiece image registration is widely used in the applications of machine grasping and defect detection. However, the different viewpoints of the workpiece are easy to cause the difficulty of image matching, and it becomes an urgent problem to be solved and studied. Considering the unique contour shape of the workpiece and the matching problem caused by multiple viewpoints, a workpiece image registration method based on contour curvature was proposed. Firstly, in contour smoothing processing, to address the issue of poor performance in polygon fitting Douglas-Pucker (DP) algorithm, the sliding window averaging method was used to further process the contours, and the smoothness of the contours was effectively improved. Then, in the resampling of contour curves, the time serialization was performed on the set of sampling points, and an effective resampling method was designed using the sampling interval to realize arbitrary values of resampling points and keep the density of contour points. When establishing a fully affine curvature library, a two-step matching strategy was designed to effectively improve matching accuracy and efficiency. Finally, for the curvature curve matching algorithm, shape context (SC) was introduced into dynamic time warping (DTW) distance calculation to solve the problems of DTW algorithm. The research results show that this method can effectively perform affine transformations on template workpiece images, achieving a match with the target workpiece images. Meanwhile, in the three comparative experiments of the matching algorithm, the transformed images obtained through the SC-DTW matching algorithm has only 4. 8%, 2. 65%, and 3. 92% differences from the target workpiece images. This is significantly less compared to the differences of 6. 83%, 4. 03%, 6. 34% for DTW, and 7. 45%, 6. 39%, 7. 31% for normalized cross correlation (NCC). This smaller discrepancy indicates that the algorithm has better registration effects and can more effectively calculate the similarity between two curves. Consequently, it can obtain the optimal curvature curve sample and achieve accurate workpiece image registration. [ABSTRACT FROM AUTHOR]

To overcome the common issues of color distortion and low contrast in underwater image processing, this study developed an innovative underwater image enhancement technique based on a lightweight non-linear activation free network. The core feature of this technique is the use of multiple cascaded non-linear activation free modules without traditional activation functions,significantly enhancing the flow of information and the efficiency of feature extraction. Additionally, the model integrates an innovative layer attention mechanism, which effectively identifies and optimizes feature dependencies between different layers, enhancing the expression of key information through dynamic adjustment of feature weights. To comprehensively evaluate the performance of the proposed method, detailed experiments were conducted on the Large Scale Underwater Image (LSUI) dataset. Compared with leading models such as FUnIE-GAN and Shallow-UWnet,our model demonstrated a significant advantage in structural similarity index (SSIM),with improvements of 8. 17% and 4. 13% respectively,markedly enhancing the color accuracy and detail retention of the images. Furthermore,the parameter count of our model was significantly reduced, decreasing by 98% and 50% respectively, greatly enhancing the model′s practicality and deployment capabilities in environments with limited computational resources. The results of this study confirm the effectiveness of this enhancement technique in addressing key visual challenges in underwater imaging and also demonstrate its potential for application in extreme visual environments. By introducing this lightweight and efficient image enhancement approach,new pathways have been opened for the further development and innovation of underwater image processing technologies,laying a solid foundation for the widespread deployment of underwater vision systems in practical applications. [ABSTRACT FROM AUTHOR]

The estimation of multiple matching models between wide baseline or large angle images is a quite challenging task in image processing. The existing algorithms can be used to estimate multiple matching models and their inliers between images well, but their results are prone to matching pairs mis-classification issues. In order to accurately estimate the multiple matching models and allocate matching pairs, this paper proposed an image multi-matching model estimation algorithm based on the aggregation of matching points of the same model (AMPSM). Firstly, for improve the proportion of correct matching pairs, it filtered out incorrect matching pairs based on the distribution characteristics of correct matching points in the neighboring region. Furthermore, based on the different matching model degrees to which the matching pairs belong, searched for the suspected intersection matching pairs of multiple models, that was interference matching pairs. Meantime, for reducing the impact of interference matching pairs on the accuracy of matching classification, they were removed. Afterwards, for improve the clustering degree of matching points with the co-model, the position was dynamically moved based on the distance between the points within the same model and the center of gravity of the point set during the sampling process. Finally, classifying clustered matching points by Mean Shift to obtain a multi matching model. And the proposed method was compared with classical framework based algorithms RANSAC, PROSAC, MAGSAC++, GMS, AdaLAM, PEARL, MTC, Sequential RANSAC, and deep learning based algorithms SuperGlue, OANet, CLCNet, CONSAC, etc. Results indicate over 30% increase in the inlier rate, 8.39% reduction in the mis-classification rate of multi model estimation. It is concluded that the new algorithm has significant advantages in incorrect matches filtering and multi-model estimation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coal gangue recognition is an important part of the coal intelligent industry, and the coal gangue recognition technology based on machine vision is the current research focus and hotspot, but there are some problems and deficiencies in the existing technology. Firstly, the influence of external factors on coal gangue image acquisition was revealed. Secondly, the common methods of coal gangue image processing were expounded and the shortcomings of each method was pointed out. Finally, the current recognition and classification technology of coal gangue based on machine learning was summarized. Based on the above research results, the further research directions of the current machine vision recognition methods for coal gangue are proposed, namely, building highquality coal gangue datasets, researching efficient image segmentation methods, selecting the most representative coal gangue features, and optimizing the deep learning network model structure. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problems of IspecHyper multi-rotor UAV hyperspectral imaging system, such as lack of data processing supporting software, large error in collecting multi-band hyperspectral data, lack of coordinates, and inability to automatically stitch. Taking the citrus planting base of Jiaolong Village, Taiping Town, Wuming District as the research area, this paper studies the hyperspectral image processing method of IspecHyper multi-rotor UAV. Firstly, the IspecHyper-VM200 imaging system was used to obtain high-definition photos and multi-band hyperspectral image data of the study area. Secondly, using high-definition photos as data sources, high-resolution UAV orthophotos were formed by PXI4D Mapper software preprocessing and ENVI software image geometric correction. Finally, the ENVI software was used to cut the distorted boundary data of multi-band hyperspectral images, and the geometric correction was completed based on the UAV orthophoto image, and then the hyperspectral reflectance image product was formed through image mosaic and spectral conversion calculation. The experimental results show that the technical method formed in this paper can effectively solve the problems of hyperspectral image processing of IspecHyper multi-rotor UAV, and provide technical reference for hyperspectral image processing of UAV. [ABSTRACT FROM AUTHOR]

【Objective】Traditional methods for detecting potato pollen viability rely on visual counting, which can be inefficient and inaccurate. In this study, a method for quickly detecting pollen viability was proposed based on PaddlePaddle deep learning framework by comparing different models.【Method】First, the pollens were stained with 2,3,5-triphenyltetrazolium chloride（TTC）and imaged using a microscope. The images were annotated by Photoshop（PS）. Viable and total pollens were labeled respectively, then the label images were converted into single-channel images. Three models, SegFormer, U-Net and DeepLabV3, were used for training to distinguish viable pollens and total pollens. Finally, a Python OpenCV program was used to count the pollen number and calculate pollen viability.【Result】Compared with other models, SegFormer demonstrated the best performance in various evaluation indexes of the two datasets. Compared with manual recognition, the OpenCV program enabled fast and batch counting with less error.【Conclusion】Potato pollen viability can be detected quickly and accurately by image processing technology. This method was used to quickly identify the pollen viability of 200 F2 individuals, providing a soild foundation for the collection of pollen viability in potato. [ABSTRACT FROM AUTHOR]

Objectives: The circular coded marker is widely used in close-range photogrammetry, and has excellent characteristics that are independent of translation, rotation, and scaling. The robust recognition and decoding of the circular coded markers are of great significance to the subsequent 3D reconstruction. Methods: Aiming at the identification of coded marks in complex environments such as cluttered backgrounds and uneven lighting, a two-step contour screening strategy is proposed. First, coarse screening is performed based on the contour length and roundness, and then fine screening is performed based on statistical analysis on gray-scale distribution. Results: The experimental results show that when the shooting angle is less than 75° and the size of the code point is greater than 30 pixels, the proposed method can guarantee high accuracy in recognition and decoding. In a complex environment, the recognition accuracy and decoding accuracy are 99.53% and 100%, respectively, and the false recognition rate is 0%. Conclusions: The proposed method can effectively reduce the false recognition rate and have a good recognition effect in a complex environment. [ABSTRACT FROM AUTHOR]

The evaluation of carotid ultrasound image plaque requires a large number of experienced clinicians, and the ultrasound image has the characteristics of blurred boundary and strong noise interference, making the evaluation of plaques time-consuming and laborious. Therefore, a fully automated carotid plaque segmentation method is urgently needed to solve the problem of manpower scarcity. This study proposes a deep neural network model based on Swin-Transformer (Shifted-Windows Transformer) block for the automatic segmentation of carotid plaques. Based on the U-Net(U-Convolutional Network) architecture, the encoding part uses three convolutional blocks for image down-sampling to obtain feature images of different resolution sizes, and then adds six pairs of two consecutive Swin-Transformer blocks for more refined feature extraction. The decoding part up-samples the refined features output by the Swin-Transformer module step by step, and jump-joints them with the feature maps of each resolution level in the encoding part, respectively. The comparison experiments based on the data set of Tong Ren Hospital show that the Dice index of the proposed deep neural network model reaches 0.814 2, which is higher than that of other comparison networks. The results demonstrate that the proposed model can effectively extract the features of carotid ultrasound image plaques and achieve automated and high-precision plaque segmentation. [ABSTRACT FROM AUTHOR]

In order to realize the accurate evaluation of queue posture and action, aiming at the problem that it is difficult to accurately measure the three-dimensional human posture characteristics such as kick height in the training scene, this paper proposed a queue three-dimensional posture feature detection method based on binocular information. The method is divided into two stages; the 2D pose estimation stage and binocular stereo matching stage. In order to improve the accuracy of 2D human pose detection, it designed a 2D pose estimation model based on the improved HRNet network. Firstly, it introduced spatial channel attention in the back-bone network to enhance feature extraction capabilities. The feature fusion layer used an adaptive spatial feature fusion module to fuse multi-scale features of the network. Secondly, it used the unbiased data processing method to encode and decode the heat map to reduce the statistical error of the data. Finally, it adopted a coarse-to-fine multi-stage supervision method during model training to improve the detection accuracy of key points. Based on the 2D pose estimation model, it used the standard correlation matching function to achieve binocular stereo matching, and then obtained the 3D human pose through coordinate transformation. The experimental results show that the improved pose estimation network has better accuracy, the accuracy of the COCO dataset reaches 77.1%, and the detection accuracy of the self-made queue training dataset reaches 86.3%, which are respectively 2.2% and 3.1% higher than the original network. In the kick height experiment of the three-dimensional human body posture, the average relative error measured by the proposed method is 2.5%, which fully verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The reproductive performance of sows can play a critical role in animal breeding, particularly in the efficiency and effectiveness of selection. However, manual recordings of piglet births and their survival rates cannot fully meet the large-scale production in recent years. The high precision is often required to capture more nuanced data, such as the intervals between births. The advanced technologies can be expected to enhance both the accuracy and efficiency of animal breeding programs. In this study, a lightweight network was developed to rapidly and accurately monitor the sow birthing in real time. Specifically, essential birthing metrics were engineered to analyze, such as the number of piglets born and the precise intervals between each birth. The lightweight network was tailored for the real-time monitoring of sow birthing activities. The critical birthing parameters were obtained to significantly enhance the efficiency and accuracy of breeding programs. Initially, the efficacy of different monitoring views—specifically, single versus double-column views—were evaluated on the accuracy of the improved model. A single-column view was significantly improved to accurately monitor the birthing events. The real-time decisionmaking and direct implications were obtained from the breeding outcomes. Advanced video processing techniques were incorporated, such as horizontal and vertical flipping. Some challenges were remained on the dynamic changes in the sow posture and varying camera perspectives during monitoring. Moreover, different lighting conditions were adapted to capture the inherent motion blur of active piglets during birth. Color jittering and Gaussian blur were then employed to significantly enhance the robustness of the model. The reliable performance was obtained under diverse operational conditions. Further advancements were achieved through a comparative analysis of classification networks. The results revealed that ResNet50 was greatly contributed to the recognition accuracy. MobileNetV3-S was performed the best with the compact model size and superior processing speed of 505.14 frames per second, indicating the optimal operational efficiency. Furthermore, MobileNetV3-S was refined to apply with the masked generative distillation—a sophisticated technique that was effectively enhanced the network's ability to capture and interpret essential birthing features. ResNet50 was utilized as the teacher model in the practical application, while MobileNetV3-S as the student model. The training was conducted using masked generative distillation followed by dependency graph pruning. The tests were carried out on a DELL OptiPlex microcomputer. An impressive detection speed of 83.10 frames per second was achieved with a test accuracy in a single-column field of view of 91.48%. Although there was a slight decrease in the accuracy of 0.98 percentage points, the detection speed was improved by 67.13 frames per second. This improved model was then deployed at the edge for testing. The better performance was achieved in the managed farrowing intervals with a detection error of just 0.31 seconds and the duration of piglet birth events with a mere 0.02-second error. Highly efficient and exceptionally precise real-time monitoring was obtained to promote the management practices of breeding activities in complex farm environments. In conclusion, the advanced computational techniques were integrated for the transformative potential to the monitoring of sow birthing. Real-time data was acquired to combine the image processing and machine learning. Some standards can be offered for the accuracy and efficiency in livestock management. The reproductive dynamics can greatly contribute to the sustainable and scientifically-informed animal husbandry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the fluorescence hyperspectral images of plastic packaging surfaces in the wavelength range of 421–700 nm were collected using fluorescence hyperspectral imaging (F-HSI) technique with ultraviolet (UV) light at 365 nm as the excitation light source. The characteristic fluorescence spectra of Escherichia coli contamination on the surface of various plastic packaging materials (polyethylene (PE), polypropylene (PP), and polyamide (PA)) were determined and the detection limit for the biofilm of E. coli was established. The results showed that the characteristic fluorescence wavelengths of E. coli on PE, PP and PA surfaces were 550, 543, and 540 nm, respectively. Based on the processed single-band images at these characteristic wavelengths, E. coli inoculation points on the surface of the three plastic packaging materials could be accurately detected, and the detection rates were all greater than 98%. The detection limits of E. coli biofilm on PE, PP and PA surfaces were 1.39, 1.39 and 1.59 (lg (CFU/cm2)), respectively. The research results provide a powerful technical means and theoretical support for microbiological detection of packaged foods and food safety monitoring.

Since low-light images are of poor quality and carry noise, resulting in insufficient contrast and brightness, unclear details in the image, and the problem of high cost of obtaining paired low-light image datasets, a no-reference low-light image enhancement method for feature map enhancement is proposed under the influence of the Mach band effect of biological vision. Firstly, the model uses the enhanced filter block (EFB) to enhance the feature of images and feature maps, which can suppress noise and enhance the feature details to improve the ability of the network to learn features. Then, skip connection is combined with efficient spatial attention (ESA) module to extract global context information and local regional features by fusing enhanced shallow features and deep features, which effectively retains image color information, avoids detail loss, and improves the generalization ability of the network. Finally, the pixel estimation curve is used to adjust the dynamic range of the pixels in the low-light image and enhance the brightness of the low-light image. Experimental results show that the PSNR, SSIM, LPIPS and NIQE of the image processed by the algorithm reach 17.709 dB, 0.657, 0.239 and 3.486 respectively. Compared with the existing mainstream algorithms, it can better achieve the purpose of image enhancement, and effectively improves the image brightness and detail information, while maintaining the naturalness of the image.

With the rapid development and application of computer technology and machine vision technology, the exploration of unexploded submunition search technology based on “artificial intelligence +” model has received extensive attention. However, due to the danger of unexploded submunitions and the particularity of military applications, data set construction is a bottleneck problem that needs to be solved urgently. Based on this, the paper discusses the construction methods and processes of real physical image data sets and three-dimensional reconstruction data sets using physical images. It focuses on the analysis of the key technologies and their advantages and disadvantages in the construction process of the two data sets. A multi-camera is used to collect the target image and geographic coordinate information, and then the deep learning algorithm is used to extract the target feature, generate the three-dimensional point cloud and fuse the three-dimensional image. The experimental results show that the three-dimensional data set constructed by this method can effectively solve the problem of insufficient data volume of the existing data set of unexploded submunitions. Finally, the future development direction of the data set construction method is prospected.

Integrated PET/MR is one of cutting-edge technologies in functional and molecular imaging. A review of the current development status of integrated PET/MR products can provide inspiration and promote the development of related fields. This study introduced the technical characteristics and research and development difficulties of integrated PET/MR products from both hardware and software aspects, summarized the publication of English and Chinese papers related to the clinical application of PET/MR products from 2008 to 2022, analysed the differences and current status of clinical application of integrated PET/MR products at home and abroad, and pointed out the development status and direction of integrated PET/MR products in China. Finally, the development of integrated PET/MR products was discussed in terms of technology, clinical application prospects, and market strategies.

To enhance image clarity and address the difficulties in feature extraction and incomplete haze removal in traditional image dehazing processes, a multi-feature fusion based generative adversarial dehazing network is proposed.The network adopts a generative adversarial approach and consists of a generator and a discriminator.The generator utilizes an encoderdecoder structure, and extracts haze-related feature maps from multiple receptive fields by a multi-feature extraction fusion (MFEF) block.The discriminator uses a series of convolutional calculations to analyze the feature differences between the generated images and the clear images, guiding the generator to output move realistic dehazing images.The experimental images show that the proposed method can effectively eliminate haze interference while preserving the original color tone of the image to the greatest extent possible.The experimental results demonstrate that the dehazed images produced by our algorithm have improved peak signal-to-noise ratio and structural similarity with an average of 2.588 dB and 2.66% respectively, compared with existing methods.

[Objective] To solve the problem of poor manual detection accuracy of traditional grain and chaff separator and improve production efficiency. [Methods] An image detection method based on machine vision was proposed, which realized the feature recognition and separation of grain rough through multi-stage progressive fusion of different image algorithms. The acquired images were selected in the ROI region and enhanced by Retinex algorithm. The Otsu algorithm was used to segment the image, and then the median filtering wwas combined with morphology to remove the image noise. The improved Canny algorithm was used to detect edge features of binary images, and the position information of the contour of the valley rough image was extracted by combining the Hough transform. Finally, the state estimation of the position information was performed by using the Kalman filter, and the best predicted value of the separated position was obtained, while the position offset error was reduced. [Results] The average detection error of the system was 3.14 mm, a decrease of 1.82 mm compared to before filtering, and the average standard deviation of filtering error was 0.8 mm. [Conclusion] This method can effectively detect the grain rough feature information and improve the separation accuracy.

Based on the literature in recent years, the application and development of machine vision in the detection of food bottle and can packaging defects were introduced, and the hardware structure and detection process of the detection system were explained. The research results based on image processing technology and deep learning method in the field of packaging defect detection of food bottles and cans were reviewed and analyzed. The research also summarized the advantages and disadvantages of classification network model and target detection network model both technologies and prospected their future development, and provide a reference for the innovative development of intelligent packaging inspection.

The aflatoxin produced by peanut mildew is highly carcinogenic, and it seriously affects food safety. In order to accurately and quickly identify moldy peanuts, this project proposes a detection method for moldy peanuts based on machine vision. Firstly, the peanut image was double-sided filtering and noise reduction, and then the image was converted to HSV space. The moldy peanut was recognized and detected by superimposing the mold color range extracted in H and S space and the open processing results of V space. The experimental results showed that the recognition accuracy of this method for moldy peanuts reached 95.3%, and the processing time for a single frame of peanut image was 0.6 seconds. Compared with other algorithms, this method had the advantages of fast speed and high accuracy, which can meet the real-time detection of moldy peanuts. At the same time, the grading processing of peanut mold is also more practical.

This is an article in the field of process mineralogy. In order to solve the problem of color spot impurity sorting in high-purity mica, a sorting method of mica containing impurities based on image processing is proposed in this article. According to the state of color spot impurities of mica under different light sources, the influence of image illumination environment is studied. On this basis, the processing methods such as Gaussian filtering and Canny edge detection are studied. Finally, the image recognition and discrimination of mica ores are realized accurately, and good results are obtained in the recognition of three different types of mica ores.

In the process of water treatment, the coagulation process is a complex system characterized by significant lag and non-linearity. To address its characteristics and based on the current research status of dosing control, a forward-cascade intelligent dosing system based on floc image processing and fuzzy logic control was developed. Through the acquisition and processing of water sample images, key parameters of floc characteristics were extracted, and the equivalent diameters were calculated to establish a dosing model to determine the coagulant dosage. Subsequently, automatic adjustment and precise dosing of coagulants were achieved through fuzzy PID control and feedforward compensation. System monitoring and performance analysis showed that compared with traditional manual dosing, the intelligent dosing system reduced the standard deviation of the sedimentation effluent turbidity by 27%, with the relative error controlled within 10% of the set value. The dosage of coagulants was reduced by 11.04%. The intelligent dosing system demonstrated significant advantages in coagulant dosage and treatment effects, which showed promising feasibility and potential for dissemination.

Objective In order to solve the problems of low efficiency and poor precision of traditional APM (automated people mover system), it is necessary to study the APM technology for track misalignment detection. Method The line laser sensor is used to obtain the track data, and track misalignment is identified and measured based on multi-dimensional image processing and point cloud processing technologies. Track contour data is firstly visualized, and image processing is then used to identify and locate track misalignment. Meanwhile, dynamic sequence is fused to determine whether the track status recognized by image recognition algorithm is consistent in a continuous period of time, thus solving the interference of single track section misjudgment and improving the accuracy of misalignment identification and positioning.After accurate identification and positioning of the misalignment, the track point cloud data obtained by scanning is processed by filtering and plane fitting to obtain the misalignment value. Result & Conclusion The test results from the actual line of Shanghai Metro Pujiang Line show that the multi-dimensional image processing technology and point cloud processing technology can realize accurate identification and positioning of track misalignment with high precision and good repeatability, meeting the requirements of track inspection.

U-Net and its variants have showcased exceptional performance in the domain of breast medical image segmentation. By employing a fully convolutional network (FCN) structure for semantic segmentation, the symmetrical structure of U-Net offers remarkable flexibility and adaptability. It can be tailored to diverse image segmentation tasks and challenges by adjusting network depth and incorporating new modules, leaving a significant impact on subsequent network designs. This paper aims to delve into the application of U-shaped convolutional networks in breast medical image segmentation, categorizing and summarizing U-shaped convolutional networks used for this purpose in recent years. It outlines the widely used breast medical image datasets and evaluation metrics, discusses common data augmentation techniques, and provides a detailed introduction to the network structure of the U-Net model along with traditional segmentation methods for breast medical images. Furthermore, it summarizes the improvements made to the U-Net network structure for breast medical image segmentation, including modifications like residual structures, multi-scale features, dilation mechanisms, attention mechanisms, skip connection mechanisms, and integration with Transformers. Finally, it addresses the current challenges and problems encountered in breast medical image segmentation and offers insights into future research directions.

Fry counting can play a pivotal role in the aquaculture industry. However, manual counting cannot fully meet the large-scale production in recent years, due to the inefficient, low precision, and even damage to the fry. In this study, a new fry counting was proposed using improved YOLOv8 and multi-target tracking. The objects were taken as the grass carp fry with the body lengths ranging from 20 to 50 mm. Firstly, the test platform was designed, according to the test requirements. Eight fry videos were then captured to extract and screen 961 valid images as the initial dataset. Then augmentation operations were performed on the data, including motion blur, brightness adjustment, noise increase, and image flipping. 2285 images dataset was divided into the training set and testing set in the ratio of 9:1. P2 small layer of target detection was then added into the YOLOv8, due to the small fry targets and high detection speed. While the GAM (Global Attention Mechanism) was introduced in front of the detection head, and the Inner-SIoU (Inner-SCYLLA- Intersection over Union) loss function was used to accelerate the convergence speed of the model for the better recognition accuracy of small and overlapping targets. The YOLOv8 detector was trained with the following settings: initial learning rate of 0.01, moments of 0.9, weight decay of 0.0005, batch size of 16, and 300 training rounds. The image processing computer was a 12th Gen Intel(R) Core (TM) i7-12700H 2.70 GHz, with an operating system of 64-bit Windows 11 system with 24 G of system memory. After that, a tracking and counting approach was realized for fry counting using BoT-SORT multi-target tracking with motion feature matching, eight-element Kalman filter, and camera motion compensation and discarding the appearance feature matching module. The tracking of fry targets was realized for the fry targets. Finally, the performance of the improved model was also evaluated, in terms of merits and performance. The ablation tests showed that the improved model was significantly promoted the counting performance, compared with the pre-improved model; Comparative test results showed that the average counting accuracy (ACP), average absolute error (MAE), and root mean square error (RMSE) were 97.16%, 3.27, and 5.16, respectively. The indexes of improved model were better than those of YOLOv5+DeepSORT, YOLOv8+DeepSORT, YOLOv8+StrongSORT, YOLOv8+ByteTrack, and YOLOv8+BoT-SORT; The comparative tests showed that the counting performance was negatively correlated with the speed of the fry through the shooting area. Once the speed was too high, the tracker was caused the insufficient inference speed, leading to a decline in the counting accuracy. The number of small fries were found in the video with the higher speed and accuracy than before. The finding can lay the strong foundation for the rapid and accurate counting of fry on the biomass estimation in factory aquaculture. [ABSTRACT FROM AUTHOR]

Different varieties of the same fruit tree can be classified as a fine granularity. Most them, only a single organ of fruit cannot fully extract the overall feature of the plant. It is also difficult to further improve the accuracy of recognition. Multiorgan features can be expected to recognize the fruit tree in current research. However, the inter-organ features can be interfered with each other in the multi-organ feature recognition. It is still challenging to accurately locate and distinguish different organ features. In this study, an organ segmentation (RSA-YOLOv8s) was proposed for the fruit and leaf using YOLOv8s target detection. The three parts of the model were improved, namely Backbone, Neck, and Head. A cross-stage local residual (Residual CSPLayer 2Conv, RC2) module was also designed in the Backbone part. The sensory field of each network layer was used to construct the hierarchical class residual links in each network layer. The dense and small target features were fully extracted after that. The Scale Spatial Pyramid Pooling (SSPP) module was designed in the Neck part. The spatial and scale information of the network were integrated to introduce 3D convolution. The higher-order and multi-scale features were extracted to detect the multi-scale targets. Furthermore, the Asymmetric decoupling detection head (ADDH) module was designed in the Head part. The regression of the model was realized to classify the features using an asymmetric structure. As such, the classification was focused on the center content, while the regression was the edge information. A total of 950 images of 17 fruit tree varieties were selected from the PlantCLEF2022 public dataset. The experimental results show that the precision rate, recall rate, F1 value and average precision of the RSA-YOLOv8s model were 83.2%, 87.9%, 85.5%, and 90.2%, respectively, in the recognition of fruit and leaf organ, which were 5.6, 6.6, 6.1 and 6.7 percentage points higher than those of the original, respectively; By contrast, the precision rate, recall rate, F1 value and mean precision were 88.2%, 91.7%, 89.9% and 94.3%, respectively, in the recognition of single organ, which were 6.4, 6.6, 6.5 and 7.2 percentage points higher than those of the original, respectively; The precision rate, recall rate, F1 value and the mean precision were 78.2%, 84.1%, 81.0% and 86.1%, respectively, in the recognition of leaf single organ, which were 4.8, 6.6, 5.6 and 6.2 percentage points higher than those of the original, respectively. In addition, the improved model was also applied into the 20 date varieties with a total of 3881 images. A recognition accuracy of 99.1% was achieved, which was 6.6% higher than that of the original model. The finding can provide a strong reference for the organ segmentation and variety recognition using multi-organ features, particularly for common fruit trees. [ABSTRACT FROM AUTHOR]

Automatic picking tea buds is ever increasing in the continuous expansion of famous brand tea market at present. However, manual picking cannot fully meet the needs of short-term concentrated harvesting in the large-scale tea production. Mechanical mass picking can also share the very short window period of famous brand tea. Therefore, it is the urgent demand to realize the intelligent and accurate picking of famous tea. Among them, visual identification has been confined to the small target of tea buds and the complex background under the natural environment in the field. Fortunately, Yolov5s-segment network model is suitable to identify tea buds, due to the easy deployment, simple integrated structure and real-time detection. In this study, a novel model was proposed to extract the contour and then locate the picking point of tea bud using improved Yolov5s-segment. The rapid recognition and accurate location of picking points were realized for the tea buds in large field scenes. Firstly, P2 micro-target detection layer was imported into the Neck and Head of the original network. The small targets detection of P3, P4 and P5 layers was achieved in the improved Yolov5s-segment network. Secondly, the contour features of tea bud were extracted in the natural lighting environment. CBAM (convolutional block attention module) was added into the end of Backbone of Yolov5s-segment network for the anti-interference of the improved model. Finally, the position coordinates of the bud stem were extracted, according to the bud contour. The picking points were located accurately for the single bud, one bud with one leaf, and one bud with two leaves. Furthermore, the micro target detection layer and CBAM attention mechanism were gradually added into the original Yolov5s-segment model. In ablation test, the CBAM attention mechanism was replaced with SE, ECA and SimAM. After that, the optimal model was compared with Yolov8s-segment and Yolact. Finally, the positioning test was carried out using the optimal model. The results showed that the best performance was achieved in the model with the micro-target detection layer and CBAM attention mechanism. Compared with the original Yolov5s-segment model, the accuracy, recall rate, F1 score, mean average precision mAP50 and mAP50-95 were improved by 7.0, 8.9, 8.1, 8.3 and 7.3 percentage points, respectively. In the comparison test, the mAP50 of the improved model increased by 4.2 and 9.5 percentage points, respectively, compared with the Yolov8s-segment and Yolact. The positioning test showed that the pixel coordinates of picking points were accurately obtained in the 0° and 45° shooting angles of camera under different states, indicating the high positioning accuracy. The improved model can be used to accurately extract the outline of single bud, one bud with one leaf and one bud with two leaves in large field scenes. The precise location of picking points can be realized at the same time. The findings can provide a theoretical basis for intelligent and rapid picking of famous tea. [ABSTRACT FROM AUTHOR]

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