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This research paper introduces a novel approach in the realm of digital preservation and inheritance of Intangible Cultural Heritage (ICH) through a Customized 3D Convolutional Neural Network (CNN). The core of this study lies in the development of an advanced image processing algorithm tailored to accurately recognize, categorize, and archive diverse forms of ICH, which include traditional performances, ceremonies, oral traditions, and crafts. Utilizing a volumetric 3D CNN, this paper demonstrates how complex ICH elements can be effectively captured and analyzed, overcoming the limitations of traditional 2D image processing methods. The network is trained on a comprehensive dataset of ICH imagery, ensuring sensitivity to the subtle nuances and dynamic nature of these cultural expressions. This paper highlights the algorithm's capability in not only safeguarding the visual aspects of ICH but also in providing an interactive, digital medium for education and cultural dissemination. The proposed method shows significant promise in aiding the efforts of cultural preservationists and educators, offering a technologically advanced pathway for the protection and inheritance of the world's rich, yet vulnerable, cultural heritage. This study sets a precedent in the interdisciplinary field of cultural heritage conservation, digital technology, and artificial intelligence, providing a scalable and effective solution for global ICH preservation initiatives. [ABSTRACT FROM AUTHOR]

Recent years have seen increased interest in object detection‐based applications for fire detection in digital images and videos from edge devices. The environment's complexity and variability often lead to interference from factors such as fire and smoke characteristics, background noise, and camera settings like angle, sharpness, and exposure, which hampers the effectiveness of fire detection applications. Limited picture data for fire and smoke scenes further challenges model accuracy and robustness, resulting in high false detection and leakage rates. To address the need for efficient detection and adaptability to various environments, this paper focuses on (1) proposing a cloud‐edge collaborative architecture for real‐time fire and smoke detection, incorporating an iterative transfer learning strategy based on user feedback to enhance adaptability; (2) improving the detection capabilities of the base model YOLOv8 by enhancing the data augmentation method and introducing the coordinate attention mechanism to improve global feature extraction. The improved algorithm shows a 2‐point accuracy increase. After three iterations of transfer learning in the production environment, accuracy improves from 93.3% to 96.4%, and mAP0.5:0.95 increases by nearly 5 points. This program effectively addresses false detection issues in fire and smoke detection systems, demonstrating practical applicability. [ABSTRACT FROM AUTHOR]

In new energy battery manufacturing, machine vision is widely used in automated assembly scenarios for key parts. To improve the accuracy and real‐time multi‐target positioning recognition of flexible thin parts, this paper proposes a multi‐level feature fusion template matching algorithm based on the Gaussian pyramid. Firstly, the algorithm constructs a Gaussian pyramid by multi‐scale image construction. Secondly, considering the image features of each layer of the pyramid, this paper uses the grey‐based Fast Normalized Matching algorithm to obtain coarse positioning coordinates on the upper layer, and the improved Linemod‐2D algorithm is applied to the bottom layer image to get accurate positioning coordinates. Finally, the positioning coordinates returned from each layer are fused to obtain the final positioning coordinate. The experimental results show that the proposed algorithm achieves excellent performance in nickel plate positioning and recognition. It exhibits satisfactory performance in nickel sheet localization and recognition. In terms of angular error, repeat accuracy, and matching speed, it competes favourably with Halcon, VisionMaster, and SCISmart. Its positioning error closely approximates that of Halcon, effectively meeting the practical production demands for high‐speed feeding and high‐precision positioning. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a mobile application, built using Flutter, that leverages object detection to enhance the lives of visually impaired individuals. The application addresses a crucial challenge faced by this community: the lack of real-time information about their surroundings. A solution is proposed that utilizes pre-trained machine learning models, potentially through TensorFlow Lite for on-device processing, to identify objects within the user's field of view as captured by the smartphone camera. The application goes beyond simple object recognition; detected objects are translated into natural language descriptions through text-to-speech functionality, providing crucial auditory cues about the environment. This real-time information stream empowers users to navigate their surroundings with greater confidence and independence. Accessibility is a core principle of this paper. The user interface will be designed for compatibility with screen readers, ensuring seamless interaction for users who rely on assistive technologies. Haptic feedback mechanisms will be incorporated to provide non-visual cues and enhance the user experience. The ultimate goal of this paper is to create a user-friendly and informative application that empowers visually impaired individuals to gain greater independence in their daily lives. The application has the potential to improve spatial awareness, foster a sense of security, and promote overall inclusion within society. [ABSTRACT FROM AUTHOR]

Edge devices are increasingly utilized for defect detection in power line inspection, necessitating algorithms that optimize model size and accuracy. This study introduces a lightweight detection method using the Optimized Feature Network with MobileOne‐FPN‐NASHead (OFN network) OFN network and distillation technique. The OFN incorporates a lightweight backbone, neural network search, re‐parametrization, and a feature pyramid to create a compact yet effective detection network. To enhance feature learning, a heterogeneous distillation approach is applied, leveraging a modified YOLOv8 as a teacher network. This modification includes an explicit visual centre for improved global and local information extraction, crucial for dense target detection in power line inspections. Additionally, the Minimize the points distance IoU (MPDloU) loss function is used to improve localization accuracy over the the Complete‐Intersection Over Union (CIoU) loss. Experimental results show a 1.1% mean Average Precision (mAP) increase for the enhanced YOLOv8 and a 70.2% mAP for the OFN network with 18.95 GFLOPs and 343 FPS, achieving a commendable balance between model efficiency and detection performance. The research underscores the viability of the OFN for edge computing in power line defect detection, highlighting the potential of innovative algorithmic structures in this application. [ABSTRACT FROM AUTHOR]

In order to address the issue of time-consuming, labor-intensive traditional industrial tomato sorting, this paper proposes a high-precision tomato recognition strategy and fast automatic grasping system. Firstly, the Swin Transformer module is integrated into YOLOv10 to reduce the resolution of each layer by half and double the number of channels, improving recognition accuracy. Then, the Simple Attention Module (SimAM) and the Efficient Multi-Scale Attention (EMA) attention mechanisms are added to achieve complete integration of features, and the Bi-level Routing Attention (BiFormer) is introduced for dynamic sparse attention and resource allocation. Finally, a lightweight detection head is added to YOLOv10 to improve the accuracy of tiny target detection. To complement the recognition system, a single-vertex and multi-crease (SVMC) origami soft gripper is employed for rapid adaptive grasping of identified objects through bistable deformation. This innovative system enables quick and accurate tomato grasping post-identification, showcasing significant potential for application in fruit and vegetable sorting operations. [ABSTRACT FROM AUTHOR]

Curing modulation is one of the important processes in tobacco production, so it is crucial to recognize tobacco flue-curing states effectively and accurately. This study created a dataset of the complete tobacco flue-curing process in a bulk curing barn environment and proposed a lightweight recognition model based on a feature skip connections module. Firstly, the image data was enhanced using a color correction matrix, which was used to recover the true color of the tobacco leaf in order to reduce the misidentification of adjacent states. Secondly, the convolutional neural network model proposed in this paper introduced Spatially Separable convolution to enhance the extraction of tobacco leaf texture features. Then, the standard convolution in Short-Term Dense Concatenate (STDC) was replaced with Depthwise Separable Convolutional blocks with different expansion rates to reduce the number of model parameters and FLOPs (Floating Point Operations Per Second). Finally, the Tobacco Flue-Curing State Recognition Network (TFSNet) was constructed by combining the SimAm attention mechanism. The experimental results showed that the model accuracy was improved by 1.63 percentage points after the color correction process. The recognition accuracy of TFSNet for the seven states of tobacco flue-curing was as high as 98.71%. The number of params and the FLOPs of the TFSNet model were 203,058 and 172.39 M, which were 98.18% and 90.55% lower than that of the ResNet18 model, respectively. The size of the model was 0.78 mb, and the time consumed per frame was only 21 ms. Compared with the mainstream model, TFSNet significantly improved the detection speed while maintaining high accuracy, and it provided effective technical support for the intelligentization of the tobacco flue-curing process. [ABSTRACT FROM AUTHOR]

Due to the difficulty in distinguishing transparent and white foreign fibers from seed cotton in RGB images and in order to improve the recognition ability of deep learning (DL) algorithms for white, transparent, and multi-class mixed foreign fibers with different sizes in seed cotton, this paper proposes a method of combining hyperspectral imaging technology with a multi-module joint hierarchical residue network (MJHResNet). Firstly, a series of preprocessing methods are performed on the hyperspectral image (HSI) to reduce the interference of noise. Secondly, a double-hierarchical residual (DHR) structure is designed, which can not only obtain multi-scale information, but also avoid gradient vanishing to some extent. After that, a squeeze-and-excitation network (SENet) is integrated to reduce redundant information, improve the expression of model features, and improve the accuracy of foreign fiber identification in seed cotton. Finally, by analyzing the experimental results with advanced classifiers, this method has significant advantages. The average accuracy is 98.71% and the overall accuracy is 99.28%. This method has great potential for application in the field of foreign fiber identification in seed cotton. [ABSTRACT FROM AUTHOR]

Visual defect recognition techniques based on deep learning models are crucial for modern industrial quality inspection. The backbone, serving as the primary feature extraction component of the defect recognition model, has not been thoroughly exploited. High‐performance vision transformer (ViT) is less adopted due to high computational complexity and limitations of computational resources and storage hardware in industrial scenarios. This paper presents LSA‐Former, a lightweight transformer architectural backbone that integrates the benefits of convolution and ViT. LSA‐Former proposes a novel self‐attention with linear computational complexity, enabling it to capture local and global semantic features with fewer parameters. LSA‐Former is pre‐trained on ImageNet‐1K and surpasses state‐of‐the‐art methods. LSA‐Former is employed as the backbone for various detectors, evaluated specifically on the PCB defect detection task. The proposed method reduces at least 18M parameters and exceeds the baseline by more than 2.2 mAP. [ABSTRACT FROM AUTHOR]

In this research paper, we delve into the classification of Thai cuisine images. Despite Thailand's renowned reputation for its multicultural culinary landscape, there is a noticeable gap in dedicated studies on Thai food classification. This paper seeks to fill that void by applying deep learning methodologies, specifically Convolutional Neural Networks (CNNs), to the identification of Thai cuisine. Thai cuisine, shaped by regional and intra-regional variations, serves as a powerful cultural representation for the nation. The study employs image recognition through CNN and integrates transfer learning to enhance classification performance. The collaborative learning process between CNN and transfer learning contributes to achieving a noteworthy accuracy rate of 84%. While previous research has often overlooked the specificity of Thai cuisine, our aim is to shed light on the potential of deep classification networks, offering an engaging illustration for both researchers and food enthusiasts alike contributing to the broader field of food image classification. [ABSTRACT FROM AUTHOR]

To effectively address common issues such as cattle being obscured by fences and images prone to colour shifts and high brightness in a ranch setting, this paper proposes an algorithm for counting cows based on multi‐scale perception and image correlation. The algorithm first adjusts the model output scale to enhance cattle detection under current conditions. It incor‐porates efficient Partial Convolution (PConv) to replace 3 × 3 convolutions in the Neck segment of the YOLOv7 network, boost‐ing computational speed and reducing complexity. To streamline feature fusion, Dynamic Head (DyHead) unifies multiple at‐tentional operations in the Neck segment, enhancing efficiency. Additionally, it introduces a novel bounding box similarity metric Minimum Point DioU (MPDIoU) based on minimum point distance, encompassing factors from existing loss functions, while simplifying computations. Experimental results demonstrate the algorithm significantly improves detection, achieving 98.8% accuracy, 99.0% recall, and a 92.1% mAP value. Compared with mainstream SOTA models, Precision increases by 0.4%, Recall by 2.0%, and mAP value by 2.2%. Model size decreases by 23.9%, parameter count by 23.0%, and computational load by 6.1%. the algorithm shows improvements across all indices, meeting the challenge of real‐time cattle counting in ranches under complex conditions. [ABSTRACT FROM AUTHOR]

Recently, as the types of imported food and the design of their packaging become more complex and diverse, digital recognition technologies such as barcodes, QR (quick response) codes, and OCR (optical character recognition) are attracting attention in order to quickly and easily check safety information (e.g., food ingredient information and recalls). However, consumers are still exposed to inaccurate and inconvenient situations because legacy technologies require dedicated terminals or include information other than safety information. In this paper, we propose a deep learning-based packaging recognition system which can easily and accurately determine food safety information with a single image captured through a smartphone camera. The detection algorithm learned a total of 100 kinds of product images and optimized YOLOv7 to secure an accuracy of over 95%. In addition, a new SUS (system usability scale)-based questionnaire was designed and conducted on 71 consumers to evaluate the usability of the system from the individual consumer's perspective. The questionnaire consisted of three categories, namely convenience, accuracy, and usefulness, and each received a score of at least 77, which confirms that the proposed system has excellent overall usability. Moreover, in terms of task completion rate and task completion time, the proposed system is superior when it compared to existing QR code- or Internet-based recognition systems. These results demonstrate that the proposed system provides consumers with more convenient and accurate information while also confirming the sustainability of smart food consumption. [ABSTRACT FROM AUTHOR]

In small-current grounding systems, the fault current during a single-phase grounding fault is small, and the transient process is complex, leading to challenges in line selection accuracy and reliability. This paper proposes a single-phase grounding line selection method based on transfer learning using the ResNet-50 model. Zero-sequence voltage and current waveforms at the fault moment are preprocessed and combined to generate training data for the model. Simulation results using PSCAD demonstrate that the proposed method achieves 99.77% validation accuracy, even under noisy conditions. These results confirm the method's feasibility and reliability in identifying grounding faults in various conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Anhui Agricultural Sciences is the property of Journal of Anhui Agricultural Sciences 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.)

In pursuit of enhancing public safety and addressing challenges in driver behavior recognition, an intelligent recognition and detection method of driver behavior based on ResNet (IRDMDB-ResNet) is proposed. The approach aims to identify instances of distracted driving resulting from abnormal behavior. Three models (IRDMDB-1, IRDMDB-2, and IRDMDB-3) are presented to implement this method, which is adapted to a deep learning behavior recognition in driving scenarios. Firstly, this study utilizes two well-tested real datasets: Driver Drowsiness Dataset and The State Farm. These datasets undergo preprocessing to meet the input requirements of the model. Secondly, a lightweight convolutional neural network model has been designed to extract features, aiding the warning system in delivering precise information and minimizing traffic collisions to the maximum extent possible. Finally, the model is evaluated based on the confusion metrics, accuracy, precision, recall, and F1-score criterion. As a result, the IRDMDB-3 model proposed in this paper can recognize and detect driver behavior effectively and stably. And it achieves 99.79% of accuracy in the classification of distracted drivers looking elsewhere in The State Farm dataset. Similarly, the detection at Driver Drowsiness Dataset is 99.68%. This advancement represents a significant improvement in traffic safety, showcasing adaptability to diverse behaviors and remarkable recognition and detection capabilities. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Cereals, Oils & Foods is the property of Science & Technology of Cereals, Oils & Foods 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 Mine Automation is the property of Industry & Mine Automation 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 contemporary society, rich in mineral resources, efficiently and accurately identifying and classifying minerals has become a prominent issue. Recent advancements in artificial intelligence, particularly breakthroughs in deep learning, have offered new solutions for intelligent mineral recognition. This paper introduces a deep-learning-based object detection model for intelligent mineral identification, specifically employing the YOLOv8 algorithm. The model was developed with a focus on seven common minerals: biotite, quartz, chalcocite, silicon malachite, malachite, white mica, and pyrite. During the training phase, the model learned to accurately recognize and classify these minerals by analyzing and annotating a large dataset of mineral images. After 258 rounds of training, a stable model was obtained with high performance on key indicators such as Precision, Recall, mAP50, and mAP50–95, with values stable at 0.91766, 0.89827, 0.94300, and 0.91696, respectively. In the testing phase, using samples provided by the Geological and Mineral Museum at the School of Earth Sciences and Engineering, Sun Yat-sen University, the model successfully identified all test samples, with 83% of them having a confidence level exceeding 87%. Despite some potential misclassifications, the results of this study contribute valuable insights and practical experience to the development of intelligent mineral recognition technologies. [ABSTRACT FROM AUTHOR]

Research on social psychology has revealed the existence of an affective mechanism in a human group, which is the group members spread their emotions to one another, the emotions of the group members form the group emotion, and the group emotion as a powerful force shapes the group members’ emotions. Current group emotion recognition methods focus on how the emotions of the group members form the group-level emotion but rarely take into account how the group emotion feeds back to the group members instantaneously. This paper proposes a new graph convolutional network architecture to characterize this unique affective mechanism for group emotion recognition. We regard the group members as the nodes of the graph and introduce a pseudo node into the graph to represent the role of the group. This paper uses graph convolutional networks to model the emotional interactions within the group from a static image and constructs an effective emotional representation at the group level for recognition. Experiment results on three widely used datasets for group emotion recognition show that our proposed method achieved superior performance in terms of recognition accuracy compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

Various types of foreign fibres may be mixed in the planting, transportation, and production processes of cotton, which not only cause equipment to be out of control, but also leads to a decrease in the quality of cotton textile products and economic losses. The machine vision based detection method for cotton foreign fibres is widely used. Based on existing related research, we construct a classification dataset for cotton foreign fibres in practical scenarios, named the CF2113‐10 dataset. The authors design a basic foreign fibre classification network called CottonNet that balances performance and efficiency. The classification accuracy on the validation set reached 94.2%. In order to enhance the high‐level feature extraction ability, this paper improves the feature fusion method of residual networks and proposes CottonNet‐Res, which improves the classification accuracy to 95.1%. Finally, a classification model based on feature difference fitting, CottonNet‐Fusion, is proposed to address the classification problem of foreign fibre images sampled in complex environments. The classification accuracy of foreign fibre images sampled in ordinary scenes has improved to 97.4%, while the images sampled in complex environments maintain an accuracy of 90.3%. [ABSTRACT FROM AUTHOR]

Accurate classification of substation equipment images remains challenging due to various factors such as unexpected illumination, viewing angles, scale variations, shadows, surface contaminants, and different elements sharing similar appearances. This paper presents a novel two‐stage substation equipment classification method based on dual‐scale attention. Leveraging the region proposal technique from Faster‐regions with CNN features (RCNN), the input images are initially decomposed into multiple scales to capture latent features. A dual‐scale attention module is introduced to enhance the precision of feature extraction. Furthermore, a two‐stage network is proposed to address the challenge of classifying closely similar substation equipment. A multi‐layer perceptron performs a coarse classification to categorize the equipment into broad categories. Then, a lightweight classifier is employed for fine‐grained subclassification, further distinguishing equipment within the same broad category. To mitigate the issue of limited training data, a specialized dataset is collected and annotated for the substation equipment classification. Experimental results demonstrate that the proposed method achieves remarkable accuracy, recall, and F1‐score surpassing 0.91, outperforming mainstream approaches in terms of recall and F1 scores. Ablation experiments further validate the significant contributions of both the dual‐scale attention and the two‐stage classification module in improving the overall performance of the classification network. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of an improved Generative Adversarial Network (GAN) specifically designed for generating color portraits from sketches. The construction of the system involves using a GPU (Graphics Processing Unit) computing host as the primary unit for model training. The tasks that require high-performance calculations are handed over to the GPU host, while the user host only needs to perform simple image processing and use the model trained by the GPU host to generate images. This arrangement reduces the computer specification requirements for the user. This paper will conduct a comparative analysis of various types of generative networks which will serve as a reference point for the development of the proposed Generative Adversarial Network. The application part of the paper focuses on the practical implementation and utilization of the developed Generative Adversarial Network for the generation of multi-skin tone portraits. By constructing a face dataset specifically designed to incorporate information about ethnicity and skin color, this approach can overcome a limitation associated with traditional generation networks, which typically generate only a single skin color. [ABSTRACT FROM AUTHOR]

In order to understand the application of frost measurement of air source heat pump heat exchanger, a research on frost measurement of air source heat pump heat exchanger based on image recognition processing technology is put forward. In this paper, the scheme design of defrosting control system based on image processing technology is introduced, and the frost degree coefficient of outdoor heat exchanger fin surface is introduced to characterize the frost degree of fin surface. Secondly, the experimental environment room was built, and the conventional defrosting control method of the existing heat pump unit was tested on the spot. It was found that in the air temperature of -6~0 °C and the air relative humidity of 86~92%, the unit appeared the phenomenon of "defrosting in time" and "defrosting without frost". Finally, the change of frost layer on the surface of the outdoor heat exchanger fin during defrosting is analyzed, and the coefficient of frost degree on the surface of the fin is calculated and analyzed. At the beginning of defrosting, due to the large degree of frost, the P-value of the frost degree coefficient on the surface of the fin is close to 1, when the defrosting is carried out for 125 seconds, the P-value of the frost degree coefficient on the surface of the fin decreases sharply to 0.09, when the defrosting is completed, the P-value of the frost degree coefficient on the surface of the outdoor heat exchanger is maintained at about 0.04. The defrosting control method of air source heat pump based on image processing technology is proposed in this paper, and its technical feasibility and effect are verified by experimental tests, which lays a foundation for future popularization and application. [ABSTRACT FROM AUTHOR]

Since deep low‐frequency tremors are considered to be associated with large earthquakes that occur adjacently on the same subducting plate interface, it is important to investigate tremors that occurred before the establishment of modern seismograph networks such as the High Sensitivity Seismograph Network (Hi‐net). We propose a deep‐learning solution to detect evidence of tremors in scanned images of paper seismogram records from over 50 years ago. In this study, we fine‐tuned a convolutional neural network (CNN) based on the Residual Network, which was pre‐trained using images of synthetic waveforms from our previous study, using a data set comprised of images generated from real seismic data recorded digitally by Hi‐net to facilitate a supervised analysis. The fine‐tuned CNN was able to predict the presence or absence of tremors in the Hi‐net images with an accuracy of 98.64%. Gradient‐weighted Class Activation Mapping heatmaps created to visualize model predictions indicated that the CNN's ability to detect tremors is not degraded by the presence of teleseisms. Once validated using the Hi‐net images, the CNN was applied to paper seismograms recorded from 1966 to 1977 at the Kumano observatory in southwest Japan, operated by Earthquake Research Institute, The University of Tokyo. The CNN showed potential for detecting tremors in scanned images of paper seismogram records from the past, facilitating downstream tasks such as the creation of new tremor catalogs. However, further training using an augmented data set to control for variables such as inconsistent plotting pen thickness is required to develop a universally applicable model. Plain Language Summary: In 2002, deep low‐frequency tremors, which periodically occur with much smaller amplitudes and longer durations than common earthquakes, were discovered owing to the establishment of dense seismic arrays in Japan. These tremors are considered to be associated with megathrust earthquakes because they occur in deeper regions of plate boundaries compared with common earthquakes. Investigating tremors that occurred before the establishment of dense seismic arrays is important since megathrust earthquakes recur on intervals of 100–200 years, and digital records from modern seismic arrays cover only a fraction of that time. In this study, we developed a convolutional neural network based on the Residual Network (ResNet) that extracts tremors from images of waveforms recorded more than 50 years ago, when seismometers drew waveforms directly on drum‐rolled paper using a pen. The proposed ResNet, which was trained on seismogram images generated from synthetic waveforms and real data recorded digitally by modern seismic arrays, was applied to scanned images of paper seismograms recorded from 1966 to 1977 at an observatory in southwest Japan. A list of tremors was successfully obtained; however, further training using data that accounts for variables such as inconsistent plotting pen thickness is required to develop a universally applicable model. Key Points: Convolutional neural network model for detection of deep low‐frequency tremors from seismogram images is proposedThe model trained with seismogram images converted from real seismic data successfully detects tremorsThe detection performances of the trained model for the paper records at the Kumano observatory in southwest Japan are discussed [ABSTRACT FROM AUTHOR]

Although various automatic or semi-automatic recognition algorithms have been proposed for tiny part recognition, most of them are limited to expert knowledge base-based target recognition techniques, which have high false detection rates, low recognition accuracy and low efficiency, which largely limit the quality as well as efficiency of tiny part assembly. Therefore, this paper proposes a precision part image preprocessing method based on histogram equalization algorithm and an improved convolutional neural network (i.e. Region Proposal Network(RPN), Visual Geometry Group(VGG)) model for precision recognition of tiny parts. Firstly, the image is restricted to adaptive histogram equalization for the problem of poor contrast between part features and the image background. Second, a custom central loss function is added to the recommended frame extraction RPN network to reduce problems such as excessive intra-class spacing during classification. Finally, the local response normalization function is added after the nonlinear activation function and pooling layer in the VGG network, and the original activation function is replaced by the Relu function to overcome the problems such as high nonlinearity and serious overfitting of the original model. Experiments show that the improved VGG model achieves 95.8% accuracy in precision part recognition and has a faster recognition speed than most existing convolutional networks trained on the same test set. [ABSTRACT FROM AUTHOR]

Background: There is a large amount of occlusion in dairy farms and changes in daytime and nighttime lighting seriously affect the accuracy of traditional dairy cow identification. Methods: This paper proposed a method to dairy cow identification in dairy farm. Firstly, Resnet50 was used to extract the pattern features of dairy cows and this paper fused the fourth and fifth scale features with semantic information. Secondly, on the basis of the triplet loss, it added label smoothing loss instead of softmax cross-entropy loss to further avoid over-fitting and reduced the intra-class distance with the center loss, which improved the mean average precision (mAP) by 8.9% compared with the initial Re-identification (Reid) and the joint optimized distance calculation formula was improved the single distance measurement in the triplet loss, using two distance measurement methods to effectively improve the recognition accuracy. Finally, it was combined with pruning operation to reduce the redundant parameters of the model. Result: The experiment proves that the identification mAP of dairy cows reaches 81.2%, which is an improvement 10.9% compared to the baseline model mAP, providing a strong technical support for the subsequent dairy cow identification and target tracking system. [ABSTRACT FROM AUTHOR]

Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. SNNs trained with Spike Timing-Dependent Plasticity (STDP) benefit from gradient-free and unsupervised local learning, which can be easily implemented on ultra-low-power neuromorphic hardware. However, classification tasks cannot be performed solely with unsupervised STDP. In this paper, we propose Stabilized Supervised STDP (S2-STDP), a supervised STDP learning rule to train the classification layer of an SNN equipped with unsupervised STDP for feature extraction. S2-STDP integrates error-modulated weight updates that align neuron spikes with desired timestamps derived from the average firing time within the layer. Then, we introduce a training architecture called Paired Competing Neurons (PCN) to further enhance the learning capabilities of our classification layer trained with S2-STDP. PCN associates each class with paired neurons and encourages neuron specialization toward target or non-target samples through intra-class competition. We evaluate our methods on image recognition datasets, including MNIST, Fashion-MNIST, and CIFAR10. Results show that our methods outperform state-of-the-art supervised STDP learning rules, for comparable architectures and numbers of neurons. Further analysis demonstrates that the use of PCN enhances the performance of S2-STDP, regardless of the hyperparameter set and without introducing any additional hyperparameters. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of a convolutional neural network based on multi-valued neurons (CNNMVN) and a fully connected multilayer neural network based on multi-valued neurons (MLMVN), employed here as a convolutional neural network in the frequency domain. We begin by providing an overview of the fundamental concepts underlying CNNMVN, focusing on the organization of convolutional layers and the CNNMVN learning algorithm. The error backpropagation rule for this network is justified and presented in detail. Subsequently, we consider how MLMVN can be used as a convolutional neural network in the frequency domain. It is shown that each neuron in the first hidden layer of MLMVN may work as a frequency-domain convolutional kernel, utilizing the Convolution Theorem. Essentially, these neurons create Fourier transforms of the feature maps that would have resulted from the convolutions in the spatial domain performed in regular convolutional neural networks. Furthermore, we discuss optimization techniques for both networks and compare the resulting convolutions to explore which features they extract from images. Finally, we present experimental results showing that both approaches can achieve high accuracy in image recognition. [ABSTRACT FROM AUTHOR]

Froth flotation is a widespread and important method for mineral separation, significantly influencing the purity and quality of extracted minerals. Traditionally, workers need to control chemical dosages by observing the visual characteristics of flotation froth, but this requires considerable experience and operational skills. This paper designs a deep ensemble learning-based sensor for flotation froth image recognition to monitor actual flotation froth working conditions, so as to assist operators in facilitating chemical dosage adjustments and achieve the industrial goals of promoting concentrate grade and mineral recovery. In our approach, training and validation data on flotation froth images are partitioned in K-fold cross validation, and deep neural network (DNN) based learners are generated through pre-trained DNN models in image-enhanced training data, in order to improve their generalization and robustness. Then, a membership function utilizing the performance information of the DNN-based learners during the validation is proposed to improve the recognition accuracy of the DNN-based learners. Subsequently, a technique for order preference by similarity to an ideal solution (TOPSIS) based on the F1 score is proposed to select the most probable working condition of flotation froth images through a decision matrix composed of the DNN-based learners' predictions via a membership function, which is adopted to optimize the combination process of deep ensemble learning. The effectiveness and superiority of the designed sensor are verified in a real industrial gold–antimony froth flotation application. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology 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.)

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.)

The extraction of navigation lines is a crucial aspect in the field autopilot system for intelligent agricultural equipment. Given that soybean seedlings are small, and straw can be found in certain Northeast China soybean fields, accurately obtaining feature points and extracting navigation lines during the soybean seedling stage poses numerous challenges. To solve the above problems, this paper proposes a method of extracting navigation lines based on the average coordinate feature points of pixel points in the bean seedling belt according to the calculation of the average coordinate. In this study, the soybean seedling was chosen as the research subject, and the Hue, Saturation, Value (HSV) colour model was employed in conjunction with the maximum interclass variance (OTSU) method for RGB image segmentation. To extract soybean seedling bands, a novel approach of framing binarised image contours by drawing external rectangles and calculating average coordinates of white pixel points as feature points was proposed. The feature points were normalised, and then the improved adaptive DBSCAN clustering method was used to cluster the feature points. The least squares method was used to fit the centre line of the crops and the navigation line, and the results showed that the average distance deviation and the average angle deviation of the proposed algorithm were 7.38 and 0.32. The fitted navigation line achieved an accuracy of 96.77%, meeting the requirements for extracting navigation lines in intelligent agricultural machinery equipment for soybean inter-row cultivation. This provides a theoretical foundation for realising automatic driving of intelligent agricultural machinery in the field. [ABSTRACT FROM AUTHOR]

Chemical molecular structures are a direct and convenient means of expressing chemical knowledge, playing a vital role in academic communication. In chemistry, hand drawing is a common task for students and researchers. If we can convert hand-drawn chemical molecular structures into machine-readable formats, like SMILES encoding, computers can efficiently process and analyze these structures, significantly enhancing the efficiency of chemical research. Furthermore, with the progress of educational technology, automated grading is gaining popularity. When machines automatically recognize chemical molecular structures and assess the correctness of the drawings, it offers great convenience to teachers. We created ChemReco, a tool designed to identify chemical molecular structures involving three atoms: C, H, and O, providing convenience for chemical researchers. Currently, there are limited studies on hand-drawn chemical molecular structures. Therefore, the primary focus of this paper is constructing datasets. We propose a synthetic image method to rapidly generate images resembling hand-drawn chemical molecular structures, enhancing dataset acquisition efficiency. Regarding model selection, the hand-drawn chemical molecule structural recognition model developed in this article achieves a final recognition accuracy of 96.90%. This model employs the encoder-decoder architecture of EfficientNet + Transformer, demonstrating superior performance compared to other encoder-decoder combinations. [ABSTRACT FROM AUTHOR]

The hydrological monitoring of flow data is important for flood prevention and modern river management. However, traditional contact methods are increasingly struggling to meet the requirements of simplicity, accuracy, and continuity. The video-based river discharge measurement is a technique to monitor flow velocity without contacting the water body by using the image-recognition algorithms, which has been verified to have the advantages of full coverage and full automation compared with the traditional contact technique. In order to provide a timely summary of the available results and to inform further research and applications, this paper reviews and synthesizes the literature on the general implementation routes of the video-based river discharge measurement technique and the principles and advances of today's popular image-recognition algorithms for velocity detection. Then, it discusses the challenges of image-recognition algorithms in terms of image acquisition conditions, parameter uncertainties, and complex meteorological and water environments. It is concluded that the performance of this technique can be improved by enhancing the robustness and accuracy of video-based discharge measurement algorithms, minimizing weather effects, and improving computational efficiency. Finally, future development directions for further perfecting this technique are outlined. [ABSTRACT FROM AUTHOR]

The success of supervised models for emotion recognition on images heavily depends on the availability of images properly annotated. Although millions of images are presently available, only a few are annotated with reliable emotional information. Current emotion recognition solutions either use large amounts of weakly-labeled web images, which often contain noise that is unrelated to the emotions of the image, or transfer learning, which usually results in performance losses. Thus, it would be desirable to know which images would be useful to be annotated to avoid an extensive annotation effort. In this paper, we propose a novel approach based on active learning to choose which images are more relevant to be annotated. Our approach dynamically combines multiple active learning strategies and learns the best ones (without prior knowledge of the best ones). Experiments using nine benchmark datasets revealed that: (i) active learning allows to reduce the annotation effort, while reaching or surpassing the performance of a supervised baseline with as little as 3% to 18% of the baseline's training set, in classification tasks; (ii) our online combination of multiple strategies converges to the performance of the best individual strategies, while avoiding the experimentation overhead needed to identify them. [ABSTRACT FROM AUTHOR]

Incomplete handwritten Dongba character often appears in heritage documents and its recognition is significant for heritage and philology. However, all previous methods always suppose that a complete Dongba character is used as input, and thus fail to achieve satisfactory performance when applied to incomplete Dongba character recognition. In this paper, an end-to-end network (DB2RNet) is proposed for incomplete handwritten Dongba character image recognition by multiscale feature restoration. Specifically, we first develop datasets that contain different levels of incomplete Dongba characters. A restoration module is proposed to restore the input incomplete Dongba character, and then a recognition module is employed to recognize Dongba character. By introducing an inter-module residual connection between the restoration module and recognition module, the DB2RNet can strengthen feature information transmission and boost the recognition performance. In addition, novel multiscale feature blocks are introduced, which can provide more effective texture and contextual information transmission for Dongba character image restoration, and thus yielding better restoration effects and better recognition results. Extensive experiments are conducted on Dongba character, Chinese character and Oracle character datasets and validate the effectiveness, superiority and robustness of our methods. Experiments results demonstrate that our proposed DB2RNet achieves competitive Dongba character restoration and recognition performance and outperforms the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The surface quality of steel strip is a critical indicator of the quality of hot‐rolled strip, so accurate inspection of its surface is essential. However, the complex texture of steel strip surface defects makes the detection challenging. Here, a multi‐scale feature fusion and attention based YOLOv5 (MFFA‐YOLOv5) model is proposed. Specifically, the bottom layer features are up‐sampled and fused not only with the middle layer features, but also with the top layer features, so that the model better captures the surface texture information of the steel strip. Secondly, an improved attention mechanism module is introduced to deal with the global and local information of the steel strip surface by introducing down‐sampling and up‐sampling paths based on Convolutional Block Attention Module (CBAM). Meanwhile, a self‐attention mechanism path is added to improve the capability of feature representation. Experimental results on the NEU‐DET dataset show that the MFFA‐YOLOv5 model significantly outperforms other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Copyright of South China Fisheries Science is the property of South China Fisheries 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.)

Perception systems in autonomous vehicles need to accurately detect and classify objects within their surrounding environments. Numerous types of sensors are deployed on these vehicles, and the combination of such multimodal data streams can significantly boost performance. The authors introduce a novel sensor fusion framework using deep convolutional neural networks. The framework employs both camera and LiDAR sensors in a multimodal, multiview configuration. The authors leverage both data types by introducing two new innovative fusion mechanisms: element‐wise multiplication and multimodal factorised bilinear pooling. The methods improve the bird's eye view moderate average precision score by +4.97% and +8.35% on the KITTI dataset when compared to traditional fusion operators like element‐wise addition and feature map concatenation. An in‐depth analysis of key design choices impacting performance, such as data augmentation, multi‐task learning, and convolutional architecture design is offered. The study aims to pave the way for the development of more robust multimodal machine vision systems. The authors conclude the paper with qualitative results, discussing both successful and problematic cases, along with potential ways to mitigate the latter. [ABSTRACT FROM AUTHOR]

Conventional few-shot learning (FSL) mainly focuses on knowledge transfer from a single source dataset to a recognition scenario with only a few training samples available but still similar to the source domain. In this paper, we consider a more practical FSL setting where multiple semantically different datasets are available to address a wide range of FSL tasks, especially for some recognition scenarios beyond natural images, such as remote sensing and medical imagery. It can be referred to as multi-source cross-domain FSL. To tackle the problem, we propose a two-stage learning scheme, termed learning and adapting multi-source representations (LAMR). In the first stage, we propose a multi-head network to obtain efficient multi-domain representations, where all source domains share the same backbone except for the last parallel projection layers for domain specialization. We train the representations in a multi-task setting where each in-domain classification task is taken by a cosine classifier. In the second stage, considering that instance discrimination and class discrimination are crucial for robust recognition, we propose two contrastive objectives for adapting the pre-trained representations to be task-specialized on the few-shot data. Careful ablation studies verify that LAMR significantly improves representation transferability, showing consistent performance boosts. We also extend LAMR to single-source FSL by introducing a dataset-splitting strategy that equally splits one source dataset into sub-domains. The empirical results show that LAMR can achieve SOTA performance on the BSCD-FSL benchmark and competitive performance on mini-ImageNet, highlighting its versatility and effectiveness for FSL of both natural and specific imaging. [ABSTRACT FROM AUTHOR]

In addressing the challenge of ships being prone to occlusion in multi‐target situations during ship target detection, leading to missed and false detections, this paper proposes an enhanced ship detection algorithm for YOLOv7‐tiny. The proposed method incorporates several key modifications. Firstly, it introduces the Convolutional Block Attention Module in the Backbone section of the original model, emphasizing position information while attending to channel features to enhance the network's ability to extract crucial information. Secondly, it replaces standard convolution with GSConv convolution in the Neck section, preserving detailed information and reducing computational load. Subsequently, the lightweight operator Content‐Aware ReAssembly of Features is employed to replace the original nearest‐neighbour interpolation, mitigating the loss of feature information during the up‐sampling process. Finally, the localization loss function, SIOU Loss, is utilized to calculate loss, expedite training convergence, and enhance detection accuracy. The research results indicate that the precision of the improved model is 91.2%, mAP@0.5 is 94.5%, and the F1‐score is 90.7%. These values are 3.7%, 5.5%, and 4.2% higher than those of the original YOLOv7‐tiny model, respectively. The improved model effectively enhances detection accuracy. Additionally, the improved model achieves an FPS of 145.4, meeting real‐time requirements. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-perspective adaptive examination cheating behavior detection method to meet the demand for automated monitoring throughout the entire process in paperless online exams. Unlike current dual-perspective cheating behavior detection methods, we expand the monitoring field of view by using three cameras with different perspectives: the overhead perspective, the horizontal perspective, and the face perspective. This effectively covers areas where cheating may occur. An adaptive cheating behavior detection system based on three perspectives is proposed, including a gaze direction recognition model based on Swin Transformer, a cheating tool detection model based on Lightweight-YOLOv5-Coordinate Attention, and a cheating behavior determination model based on Multilayer Perceptron. To reduce computational complexity and ensure efficient processing while expanding the monitoring field of view, the system uses the results of the gaze direction recognition model to adaptively select the cheating behavior detection model from different perspectives, reducing the three-perspective system to dual-perspective. In online simulation tests, our method achieves cheating behavior determination at 35 frames per second, with an average recognition rate of 95%. It has good real-time performance, accuracy, and a large monitoring range. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & 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.)

Recent research has revealed that subtle imperceptible perturbations can deceive well-trained neural network models, leading to inaccurate outcomes. These instances, known as adversarial examples, pose significant threats to the secure application of machine learning techniques in safety-critical systems. In this paper, we delve into the study of one-pixel attacks in deep neural networks, recently reported as a kind of adversarial examples. To identify such one-pixel attacks, most existing methodologies rely on the differential evolution method, which utilizes random selection from the current population to escape local optima. However, the differential evolution technique might waste search time and overlook good solutions if the number of iterations is insufficient. Hence, in this paper, we propose a gradient ascent with momentum approach to efficiently discover good solutions for the one-pixel attack problem. As our method takes a more direct route to the goal compared to existing methods relying on blind random walks, it can effectively identify one-pixel attacks. Our experiments conducted on popular CNNs demonstrate that, in comparison with existing methodologies, our technique can detect one-pixel attacks significantly faster. [ABSTRACT FROM AUTHOR]

Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditional post-processing algorithms to recognize braille character information. Experimental results demonstrate that this framework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille characters in complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dot detection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods, these represent improvements of 0.08% and 0.02%, respectively. [ABSTRACT FROM AUTHOR]

The YOLO series of algorithms have made substantial contributions to the detection of insulator defects in power transmission line operations. However, existing target detection algorithms for the small target detection and low‐quality insulator images encounter difficulties in effectively capturing relevant features, resulting in a higher probability of target loss. To identify and classify defects in the operational state of insulators, an improved YOLOv8 target identification algorithm called DGW‐YOLOv8 is proposed in this paper. The deformable attention backbone of the DGW‐YOLOv8 target identification algorithm is designed by adding the deformable ConvNets v2 module and the global attention mechanism. This addition reduces the feature loss caused by the network feature processing, enhances the sensitivity of the algorithm to small‐scale targets, and reduces the impact caused by the different global positions of the targets. Additionally, to address the problem of low quality of captured images, WIoU v3 is used to replace CIoU in the original YOLOv8 target identification algorithm to optimize the loss function, reduce the degrees of freedom, and improve the network robustness. Experimental results demonstrate that the enhanced YOLOv8 algorithm can achieve an improvement of 2.4% and 5.5% in mAP and mAP50‐95, respectively, compared with the original algorithm. [ABSTRACT FROM AUTHOR]

In real scenarios, accurate and real‐time detection of traffic signs is of great significance to the automatic driving system. To meet the requirements of detection accuracy and speed, a new traffic sign detection method based on YOLOv5 and Swin‐Transformer is proposed in this paper. First, based on the traditional Focus structure, a lightweight shallow information enhancement module is designed. Second, to enhance the channel weights of useful information, an adjustable channel attention mechanism is proposed. Additionally, a Cross Stage Partial module based on Swin‐Transformer is designed to capture contextual information around traffic signs, thereby improving the detection accuracy of small‐scale traffic signs. Finally, to better fuse deep semantic features and shallow detail features, an adaptive feature fusion method is proposed. To verify the superiority of the proposed method, experimental verification was carried out on TT100K and DFG traffic sign detection datasets, and their mAP, AP50 and FPS were (75.3, 94.8, 82) and (79.7, 85.9, 118), respectively. The experimental results show that the proposed traffic sign detection method has high accuracy and real‐time performance, and can meet the needs of traffic sign detection in the actual scene. [ABSTRACT FROM AUTHOR]

Accurately identifying all objects of interest inside the specified frame of reference is crucial for object identification techniques to allow machine vision to interpret images successfully. Theoretical frameworks from computer vision and deep learning have informed many potential solutions to this problem. However, current approaches frequently fail when faced with objects going through random geometric changes and continually show shortcomings when identifying small, dense objects. This research looks at state-of-the-art object detection methods, compares them, and then suggests a convolutional object detection network that has been tweaked to fix the problems with the existing methods. Comparing our research to existing approaches, we find that they perform better. We accomplish this by training deep convolutional networks to detect geometric transformations and adjusting the networks to handle multi-scaled features. The results achieved after the optimization of the You only look once (YOLO) V5 & V7 are stated in this paper. The experiments demonstrated that YOLO v8 achieved higher accuracy than the hyper-tuned YOLO V5 and V7, with an average Precision (mAP) score of 52.8% on the customized military dataset running at 60 Frames Per Second (FPS). Object detection using deep neural networks (DNNs) poses a significant challenge due to the high computational and power requirements, mainly when deployed on general-purpose platforms such as CPUs and GPUs. However, addressing these limitations becomes crucial for efficient edge computing tasks like object detection, where faster, smaller, and energy-efficient solutions are essential. To overcome these challenges, system-on-chip (SoC) designs emerge as a promising solution. [ABSTRACT FROM AUTHOR]

For years, researchers have been studying computer vision, i.e. the ability of artificial intelligence (AI) systems to perceive and interpret visual data like humans. This study is gaining increasing attention as researchers aim to develop tools that automate visual tasks and replicate human visual awareness. However, the interpretation of images is very complex due to the vast amount of multi-resolution information they contain, making the development of AI technologies for visual recognition particularly challenging. This article provides an overview of digital image processing, highlighting the main concepts and introducing key algorithms. These methods are designed to capture, process, and interpret digital images and enable the extraction of important data from real-world environments. We conduct rigorous image processing tests and compare AI-driven recognition systems with human analysis. The results show that computer vision technology significantly outperforms human observation in terms of accuracy and consistency. These results highlight the potential of computer vision to revolutionize various industries by automating complex visual tasks and offer promising future applications in areas such as healthcare, security, and manufacturing. The paper provides valuable insights into current advances in digital image processing and the role of AI in improving visual recognition capabilities, paving the way for further innovation in this area. [ABSTRACT FROM AUTHOR]