Accurate estimation on the state of health(SOH)is essential for ensuring the safe and reliable operation of batteries.Traditional assessment methods primarily focus on electrical attributes for capacity decay,often ov...Accurate estimation on the state of health(SOH)is essential for ensuring the safe and reliable operation of batteries.Traditional assessment methods primarily focus on electrical attributes for capacity decay,often overlooking the impact of thermal distribution on battery aging.However,thermal effect is a critical factor for degradation process and associated risks throughout their service life.In this paper,we introduce a novel deep learning framework specially designed to estimate the capacity and thermal risks of lithium-ion batteries(LIBs).This model consists of two main components that leverage computer vision technology.One predicts battery capacity by integrating the advantages of thermal and electrical features using a temporal pattern attention(TPA)mechanism,while the other assesses thermal risk by incorporating temperature variation to provide early warnings of potential hazards.An infrared camera is deployed to record temperature evolution of LIBs during the electrochemical process.The thermal heterogeneities are recorded by infrared camera,and the corresponding temperature evolutions are extracted as representative features for analysis.The proposed model demonstrates high accuracy and stability,with an average root mean square error(RMSE)of 0.67% for capacity estimation and accuracy exceeding 93.9% for risk prediction,underscoring the importance of integrating spatial temperature distribution into battery health assessments.This work offers valuable insights for the development of intelligent and robust battery management systems.展开更多
Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples ca...Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.展开更多
This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities.A comparable filter is used to improve the visual quality of the...This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities.A comparable filter is used to improve the visual quality of the photographs by reducing truncations in the existing images.Furthermore,the collected images undergo processing using histogram gradients and a flexible threshold value that may be adjusted in specific situations.Thus,it is possible to reduce the occurrence of overlapping circumstances in collective picture characteristics by substituting grey-scale photos with colorized factors.The proposed method offers additional robust feature representations by imposing a limiting factor to reduce overall scattering values.This is achieved by visualizing a graphical function.Moreover,to derive valuable insights from a series of photos,both the separation and in-version processes are conducted.This involves analyzing comparison results across four different scenarios.The results of the comparative analysis show that the proposed method effectively reduces the difficulties associated with time and space to 1 s and 3%,respectively.In contrast,the existing strategy exhibits higher complexities of 3 s and 9.1%,respectively.展开更多
To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is deve...To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.展开更多
The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear...The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.展开更多
With the development of technology and the progress of life,more and more people,regardless of entertainment,learning,or work,cannot do without computer desks and cannot put down their mobile phones.Due to prolonged s...With the development of technology and the progress of life,more and more people,regardless of entertainment,learning,or work,cannot do without computer desks and cannot put down their mobile phones.Due to prolonged sitting and often neglecting the importance of posture,incorrect posture can often lead to health problems such as hunchback,lumbar muscle strain,and shoulder and neck pain over time.To address this issue,we designed a computer vision-based human body posture detection system.The system utilizes YOLOv8 technology to accurately locate key points of the human body skeleton,and then analyzes the coordinate positions and depth information of these key points to establish a criterion for distinguishing different postures.With the assistance of an SVM classifier,the system achieves an average recognition rate of 95%.Finally,we successfully deployed the posture detection system on Raspberry Pi hardware and conducted extensive testing.The test results demonstrate that the system can effectively detect various postures and provide real-time reminders to users to correct poor posture,demonstrating good practicality and stability.展开更多
6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is...6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.展开更多
Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traf...Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.展开更多
Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board f...Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board field-programmable gate array(FPGA)with a core frequency of 100 MHz,our system facilitates reading and writing operations across 16 channels,performing discrete incremental proportional-integral-derivative(PID)calculations within 3.4 microseconds.Building upon this foundation,gradient and extremum algorithms are further integrated,incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm.This integration enhances the real-time performance and significantly increases the accuracy of atom tracking.Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite(HOPG)surface under room temperature atmospheric conditions.Through applying computer vision and image processing algorithms,atom tracking can be used when scanning a large area.The techniques primarily consist of two algorithms:the region of interest(ROI)-based feature matching algorithm,which achieves 97.92%accuracy,and the feature description-based matching algorithm,with an impressive 99.99%accuracy.Both implementation approaches have been tested for scanner drift measurements,and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.展开更多
Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease ...Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.展开更多
Excessive inactivity in farm animals can be an early indication of illness.Traditional way for detecting excessive inactivity in pigs relies on manual inspection which can be laborious and especially time-consuming.Th...Excessive inactivity in farm animals can be an early indication of illness.Traditional way for detecting excessive inactivity in pigs relies on manual inspection which can be laborious and especially time-consuming.This paper proposed a computer vision system that could detect inactivity of individual pigs housed in group pens which is potential in alarming the farmer of the animals concerned.The system recorded sequential depth images for the animals in a pen and implemented a proposed image processing and logic analysis scheme named as‘DepInact’to keep track of the inactive time of group-housed individual pigs over time.To verify the robustness and accuracy of the developed system,a total of 656 pairs of corresponding depth data and color images,consecutively taken 4 s apart from each other,were attained.The verification process involved manually identifying all pigs using the color images captured.The results of identification of all pigs that were inactive for more than the preset period of time by DepInact were compared to those by manual inspection through the color images captured.An accuracy of 85.7%was achieved using the verification data,thus demonstrating that the developed system is a viable alternative to manual detection of inactivity of group-housed pigs.Nevertheless,more research is still needed to improve the accuracy of the developed system.展开更多
As global oil exploration ventures into deeper and more complex territories,drilling bit wear and damage have emerged as significant constraints on drilling efficiency and safety.Despite the publication of official bi...As global oil exploration ventures into deeper and more complex territories,drilling bit wear and damage have emerged as significant constraints on drilling efficiency and safety.Despite the publication of official bit wear evaluation standards by the International Association of Drill Contractors(IADC),the current lack of quantitative and scientific evaluation techniques means that bit wear assessments rely heavily on engineers'experience.Consequently,forming a standardized database of drilling bit information to underpin the mechanisms of bit wear and facilitate optimal design remains challenging.Therefore,an efficient and quantitative evaluation of bit wear is crucial for optimizing bit performance and improving penetration efficiency.This paper introduces an automatic standard workflow for the quantitative evaluation of bit wear and the design of a comprehensive bit information database.Initially,a method for acquiring images of worn bits at the drilling site was developed.Subsequently,the wear classification and grading models based on computer vision were established to determine bit status.The wear classification model focuses on the positioning and classification of bit cutters,while the wear grading model quantifies the extent of bit wear.After that,the automatic evaluation method of the bit wear is realized.Additionally,bit wear evaluation software was designed,integrating all necessary functions to assess bit wear in accordance with IADC standards.Finally,a drilling bit database was created by integrating bit wear data,logging data,mud-logging data,and basic drilling bit data.This workflow represents a novel approach to collecting and analyzing drilling bit information at drilling sites.It holds potential to facilitate the creation of a large-scale information database for the entire lifecycle of drilling bits,marking the inception of intelligent analysis,design,and manufacture of drilling bits,thereby enhancing performance in challenging drilling conditions.展开更多
Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related factors.With the current development in information technologies,computer vision h...Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related factors.With the current development in information technologies,computer vision has become a promising tool in the real-time automation of poultry monitoring systems due to its non-intrusive and non-invasive properties,and its ability to present a wide range of information.Hence,it can be applied to monitor several bio-processes and bio-responses.This review summarizes the current advances in poultrymonitoring techniques based on computer vision systems,i.e.,conventional machine learning-based and deep learning-based systems.A detailed presentation on the machine learning-based system was presented,i.e.,pre-processing,segmentation,feature extraction,feature selection,and dimension reduction,and modeling.Similarly,deep learning approaches in poultry monitoring were also presented.Lastly,the challenges and possible solutions presented by researches in poultry monitoring,such as variable illumination conditions,occlusion problems,and lack of augmented and labeled poultry datasets,were discussed.展开更多
Objective:We propose a solution that is backed by cloud computing,combines a series of AI neural networks of computer vision;is capable of detecting,highlighting,and locating breast lesions from a live ultrasound vide...Objective:We propose a solution that is backed by cloud computing,combines a series of AI neural networks of computer vision;is capable of detecting,highlighting,and locating breast lesions from a live ultrasound video feed,provides BI-RADS categorizations;and has reliable sensitivity and specificity.Multiple deep-learning models were trained on more than 300,000 breast ultrasound images to achieve object detection and regions of interest classification.The main objective of this study was to determine whether the performance of our Al-powered solution was comparable to that of ultrasound radiologists.Methods:The noninferiority evaluation was conducted by comparing the examination results of the same screening women between our AI-powered solution and ultrasound radiologists with over 10 years of experience.The study lasted for one and a half years and was carried out in the Duanzhou District Women and Children's Hospital,Zhaoqing,China.1,133 females between 20 and 70 years old were selected through convenience sampling.Results:The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 93.03%,94.90%,90.71%,92.68%,and 93.48%,respectively.The area under the curve(AUC)for all positives was 0.91569 and the AUC for all negatives was 0.90461.The comparison indicated that the overall performance of the AI system was comparable to that of ultrasound radiologists.Conclusion:This innovative AI-powered ultrasound solution is cost-effective and user-friendly,and could be applied to massive breast cancer screening.展开更多
This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as o...This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.展开更多
Computer vision and its application in the precision control system arediscussed. In the process of fabricating, the accuracy of the products should be controlledreasonably and completely. The precision should be kept...Computer vision and its application in the precision control system arediscussed. In the process of fabricating, the accuracy of the products should be controlledreasonably and completely. The precision should be kept and adjusted according to the information offeedback got from the measurement on-line or out-line in different procedures. Computer vision isone of the useful methods to do this. Computer vision and the image manipulation are presented, andbased on this, a n-dimensional vector to appraise on precision of machining is given.展开更多
Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materia...Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materials,which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees.This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material.The type of material of interest is metal sheet,whose shape is simple,a large rectangular shape,yet difficult to detect.The use of computer vision technology can reduce the costs incurred fromthe loss of high-value materials,eliminate repetitive work requirements for skilled labor,and reduce human error.A computer vision system is proposed and tested on a metal sheet picking process formultiple metal sheet stacks in the storage area by using one video camera.Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83%and a recall of 100%.展开更多
In the period of Industries 4.0,cyber-physical systems(CPSs)were a major study area.Such systems frequently occur in manufacturing processes and people’s everyday lives,and they communicate intensely among physical e...In the period of Industries 4.0,cyber-physical systems(CPSs)were a major study area.Such systems frequently occur in manufacturing processes and people’s everyday lives,and they communicate intensely among physical elements and lead to inconsistency.Due to the magnitude and importance of the systems they support,the cyber quantum models must function effectively.In this paper,an image-processing-based anomalous mobility detecting approach is suggested that may be added to systems at any time.The expense of glitches,failures or destroyed products is decreased when anomalous activities are detected and unplanned scenarios are avoided.The presently offered techniques are not well suited to these operations,which necessitate information systems for issue treatment and classification at a degree of complexity that is distinct from technology.To overcome such challenges in industrial cyber-physical systems,the Image Processing aided Computer Vision Technology for Fault Detection System(IM-CVFD)is proposed in this research.The Uncertainty Management technique is introduced in addition to achieving optimum knowledge in terms of latency and effectiveness.A thorough simulation was performed in an appropriate processing facility.The study results suggest that the IM-CVFD has a high performance,low error frequency,low energy consumption,and low delay with a strategy that provides.In comparison to traditional approaches,the IM-CVFD produces a more efficient outcome.展开更多
Aiming at concrete tasks of information fusion in computer pan vision (CPV) system, information fusion methods are studied thoroughly. Some research progresses are presented. Recognizing of vision testing object is re...Aiming at concrete tasks of information fusion in computer pan vision (CPV) system, information fusion methods are studied thoroughly. Some research progresses are presented. Recognizing of vision testing object is realized by fusing vision information and non vision auxiliary information, which contain recognition of material defects, intelligent robot’s autonomous recognition for parts and computer to defect image understanding and recognition automatically.展开更多
In the vision transformer(ViT)architecture,image data are transformed into sequential data for processing,which may result in the loss of spatial positional information.While the self-attention mechanism enhances the ...In the vision transformer(ViT)architecture,image data are transformed into sequential data for processing,which may result in the loss of spatial positional information.While the self-attention mechanism enhances the capacity of ViT to capture global features,it compromises the preservation of fine-grained local feature information.To address these challenges,we propose a spatial positional enhancement module and a wavelet transform enhancement module tailored for ViT models.These modules aim to reduce spatial positional information loss during the patch embedding process and enhance the model’s feature extraction capabilities.The spatial positional enhancement module reinforces spatial information in sequential data through convolutional operations and multi-scale feature extraction.Meanwhile,the wavelet transform enhancement module utilizes the multi-scale analysis and frequency decomposition to improve the ViT’s understanding of global and local image structures.This enhancement also improves the ViT’s ability to process complex structures and intricate image details.Experiments on CIFAR-10,CIFAR-100 and ImageNet-1k datasets are done to compare the proposed method with advanced classification methods.The results show that the proposed model achieves a higher classification accuracy,confirming its effectiveness and competitive advantage.展开更多
基金financial support of the Fundamental Research Funds for the Central Universities(SCU2023HGXY)Special Research Funds for Intelligent Battery Cell Multidimensional Signal Sensing Technology Project from Huawei Technologies Co.Ltd.(24H1117)。
文摘Accurate estimation on the state of health(SOH)is essential for ensuring the safe and reliable operation of batteries.Traditional assessment methods primarily focus on electrical attributes for capacity decay,often overlooking the impact of thermal distribution on battery aging.However,thermal effect is a critical factor for degradation process and associated risks throughout their service life.In this paper,we introduce a novel deep learning framework specially designed to estimate the capacity and thermal risks of lithium-ion batteries(LIBs).This model consists of two main components that leverage computer vision technology.One predicts battery capacity by integrating the advantages of thermal and electrical features using a temporal pattern attention(TPA)mechanism,while the other assesses thermal risk by incorporating temperature variation to provide early warnings of potential hazards.An infrared camera is deployed to record temperature evolution of LIBs during the electrochemical process.The thermal heterogeneities are recorded by infrared camera,and the corresponding temperature evolutions are extracted as representative features for analysis.The proposed model demonstrates high accuracy and stability,with an average root mean square error(RMSE)of 0.67% for capacity estimation and accuracy exceeding 93.9% for risk prediction,underscoring the importance of integrating spatial temperature distribution into battery health assessments.This work offers valuable insights for the development of intelligent and robust battery management systems.
基金Supported by the National Natural Science Foundation of China(U1903214,62372339,62371350,61876135)the Ministry of Education Industry University Cooperative Education Project(202102246004,220800006041043,202002142012)the Fundamental Research Funds for the Central Universities(2042023kf1033)。
文摘Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.
基金financially supported by Ongoing Research Funding Program(ORF-2025-846),King Saud University,Riyadh,Saudi Arabia.
文摘This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities.A comparable filter is used to improve the visual quality of the photographs by reducing truncations in the existing images.Furthermore,the collected images undergo processing using histogram gradients and a flexible threshold value that may be adjusted in specific situations.Thus,it is possible to reduce the occurrence of overlapping circumstances in collective picture characteristics by substituting grey-scale photos with colorized factors.The proposed method offers additional robust feature representations by imposing a limiting factor to reduce overall scattering values.This is achieved by visualizing a graphical function.Moreover,to derive valuable insights from a series of photos,both the separation and in-version processes are conducted.This involves analyzing comparison results across four different scenarios.The results of the comparative analysis show that the proposed method effectively reduces the difficulties associated with time and space to 1 s and 3%,respectively.In contrast,the existing strategy exhibits higher complexities of 3 s and 9.1%,respectively.
基金The National Natural Science Foundation of China(No.52338011,52378291)Young Elite Scientists Sponsorship Program by CAST(No.2022-2024QNRC0101).
文摘To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.
文摘The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.
基金funded by the Science and Technology Project of Hebei Education Department (No.ZD2022100).
文摘With the development of technology and the progress of life,more and more people,regardless of entertainment,learning,or work,cannot do without computer desks and cannot put down their mobile phones.Due to prolonged sitting and often neglecting the importance of posture,incorrect posture can often lead to health problems such as hunchback,lumbar muscle strain,and shoulder and neck pain over time.To address this issue,we designed a computer vision-based human body posture detection system.The system utilizes YOLOv8 technology to accurately locate key points of the human body skeleton,and then analyzes the coordinate positions and depth information of these key points to establish a criterion for distinguishing different postures.With the assistance of an SVM classifier,the system achieves an average recognition rate of 95%.Finally,we successfully deployed the posture detection system on Raspberry Pi hardware and conducted extensive testing.The test results demonstrate that the system can effectively detect various postures and provide real-time reminders to users to correct poor posture,demonstrating good practicality and stability.
基金supported by the Inner Mongolia Natural Science Fund Project(2019MS06013)Ordos Science and Technology Plan Project(2022YY041)Hunan Enterprise Science and Technology Commissioner Program(2021GK5042).
文摘6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.
基金National Key R&D Program of China,Grant/Award Number:2022YFC3303600National Natural Science Foundation of China,Grant/Award Number:62077015Natural Science Foundation of Zhejiang Province,Grant/Award Number:LY23F020010。
文摘Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.
基金Project supported by the National Science Fund for Distinguished Young Scholars(Grant No.T2125014)the Special Fund for Research on National Major Research Instruments of the National Natural Science Foundation of China(Grant No.11927808)the CAS Key Technology Research and Development Team Project(Grant No.GJJSTD20200005)。
文摘Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board field-programmable gate array(FPGA)with a core frequency of 100 MHz,our system facilitates reading and writing operations across 16 channels,performing discrete incremental proportional-integral-derivative(PID)calculations within 3.4 microseconds.Building upon this foundation,gradient and extremum algorithms are further integrated,incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm.This integration enhances the real-time performance and significantly increases the accuracy of atom tracking.Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite(HOPG)surface under room temperature atmospheric conditions.Through applying computer vision and image processing algorithms,atom tracking can be used when scanning a large area.The techniques primarily consist of two algorithms:the region of interest(ROI)-based feature matching algorithm,which achieves 97.92%accuracy,and the feature description-based matching algorithm,with an impressive 99.99%accuracy.Both implementation approaches have been tested for scanner drift measurements,and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.
基金support from the Deanship for Research&Innovation,Ministry of Education in Saudi Arabia,under the Auspices of Project Number:IFP22UQU4281768DSR122.
文摘Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.
基金The current study was finally supported by National Key R&D Program of China(2018YFD0500700)and Natural Science Foundation of Hubei Province(2018CFB099).
文摘Excessive inactivity in farm animals can be an early indication of illness.Traditional way for detecting excessive inactivity in pigs relies on manual inspection which can be laborious and especially time-consuming.This paper proposed a computer vision system that could detect inactivity of individual pigs housed in group pens which is potential in alarming the farmer of the animals concerned.The system recorded sequential depth images for the animals in a pen and implemented a proposed image processing and logic analysis scheme named as‘DepInact’to keep track of the inactive time of group-housed individual pigs over time.To verify the robustness and accuracy of the developed system,a total of 656 pairs of corresponding depth data and color images,consecutively taken 4 s apart from each other,were attained.The verification process involved manually identifying all pigs using the color images captured.The results of identification of all pigs that were inactive for more than the preset period of time by DepInact were compared to those by manual inspection through the color images captured.An accuracy of 85.7%was achieved using the verification data,thus demonstrating that the developed system is a viable alternative to manual detection of inactivity of group-housed pigs.Nevertheless,more research is still needed to improve the accuracy of the developed system.
基金the National Key Research and Development Project(2019YFA0708300)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX 2020-03)+1 种基金the CNPC Science and Technology Innovation Fund(No.2022DO02-0308)the Distinguished Young Foundation of National Natural Science Foundation of China(No.52125401)for their financial support。
文摘As global oil exploration ventures into deeper and more complex territories,drilling bit wear and damage have emerged as significant constraints on drilling efficiency and safety.Despite the publication of official bit wear evaluation standards by the International Association of Drill Contractors(IADC),the current lack of quantitative and scientific evaluation techniques means that bit wear assessments rely heavily on engineers'experience.Consequently,forming a standardized database of drilling bit information to underpin the mechanisms of bit wear and facilitate optimal design remains challenging.Therefore,an efficient and quantitative evaluation of bit wear is crucial for optimizing bit performance and improving penetration efficiency.This paper introduces an automatic standard workflow for the quantitative evaluation of bit wear and the design of a comprehensive bit information database.Initially,a method for acquiring images of worn bits at the drilling site was developed.Subsequently,the wear classification and grading models based on computer vision were established to determine bit status.The wear classification model focuses on the positioning and classification of bit cutters,while the wear grading model quantifies the extent of bit wear.After that,the automatic evaluation method of the bit wear is realized.Additionally,bit wear evaluation software was designed,integrating all necessary functions to assess bit wear in accordance with IADC standards.Finally,a drilling bit database was created by integrating bit wear data,logging data,mud-logging data,and basic drilling bit data.This workflow represents a novel approach to collecting and analyzing drilling bit information at drilling sites.It holds potential to facilitate the creation of a large-scale information database for the entire lifecycle of drilling bits,marking the inception of intelligent analysis,design,and manufacture of drilling bits,thereby enhancing performance in challenging drilling conditions.
基金The project was funded by China National Key Research and Development Project(Grant No.2017YFD0701602-2).
文摘Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related factors.With the current development in information technologies,computer vision has become a promising tool in the real-time automation of poultry monitoring systems due to its non-intrusive and non-invasive properties,and its ability to present a wide range of information.Hence,it can be applied to monitor several bio-processes and bio-responses.This review summarizes the current advances in poultrymonitoring techniques based on computer vision systems,i.e.,conventional machine learning-based and deep learning-based systems.A detailed presentation on the machine learning-based system was presented,i.e.,pre-processing,segmentation,feature extraction,feature selection,and dimension reduction,and modeling.Similarly,deep learning approaches in poultry monitoring were also presented.Lastly,the challenges and possible solutions presented by researches in poultry monitoring,such as variable illumination conditions,occlusion problems,and lack of augmented and labeled poultry datasets,were discussed.
文摘Objective:We propose a solution that is backed by cloud computing,combines a series of AI neural networks of computer vision;is capable of detecting,highlighting,and locating breast lesions from a live ultrasound video feed,provides BI-RADS categorizations;and has reliable sensitivity and specificity.Multiple deep-learning models were trained on more than 300,000 breast ultrasound images to achieve object detection and regions of interest classification.The main objective of this study was to determine whether the performance of our Al-powered solution was comparable to that of ultrasound radiologists.Methods:The noninferiority evaluation was conducted by comparing the examination results of the same screening women between our AI-powered solution and ultrasound radiologists with over 10 years of experience.The study lasted for one and a half years and was carried out in the Duanzhou District Women and Children's Hospital,Zhaoqing,China.1,133 females between 20 and 70 years old were selected through convenience sampling.Results:The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 93.03%,94.90%,90.71%,92.68%,and 93.48%,respectively.The area under the curve(AUC)for all positives was 0.91569 and the AUC for all negatives was 0.90461.The comparison indicated that the overall performance of the AI system was comparable to that of ultrasound radiologists.Conclusion:This innovative AI-powered ultrasound solution is cost-effective and user-friendly,and could be applied to massive breast cancer screening.
基金funded by Woosong University Academic Research 2024.
文摘This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.
文摘Computer vision and its application in the precision control system arediscussed. In the process of fabricating, the accuracy of the products should be controlledreasonably and completely. The precision should be kept and adjusted according to the information offeedback got from the measurement on-line or out-line in different procedures. Computer vision isone of the useful methods to do this. Computer vision and the image manipulation are presented, andbased on this, a n-dimensional vector to appraise on precision of machining is given.
基金This work was jointly supported by the Excellent Research Graduate Scholarship-EreG Scholarship Program Under the Memorandum of Understanding between Thammasat University and National Science and Technology Development Agency(NSTDA),Thailand[No.MOU-CO-2562-8675]the Center of Excellence in Logistics and Supply Chain System Engineering and Technology(COE LogEn)+1 种基金Sirindhorn International Institute of Technology(SIIT)Thammasat University,Thailand.
文摘Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materials,which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees.This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material.The type of material of interest is metal sheet,whose shape is simple,a large rectangular shape,yet difficult to detect.The use of computer vision technology can reduce the costs incurred fromthe loss of high-value materials,eliminate repetitive work requirements for skilled labor,and reduce human error.A computer vision system is proposed and tested on a metal sheet picking process formultiple metal sheet stacks in the storage area by using one video camera.Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83%and a recall of 100%.
文摘In the period of Industries 4.0,cyber-physical systems(CPSs)were a major study area.Such systems frequently occur in manufacturing processes and people’s everyday lives,and they communicate intensely among physical elements and lead to inconsistency.Due to the magnitude and importance of the systems they support,the cyber quantum models must function effectively.In this paper,an image-processing-based anomalous mobility detecting approach is suggested that may be added to systems at any time.The expense of glitches,failures or destroyed products is decreased when anomalous activities are detected and unplanned scenarios are avoided.The presently offered techniques are not well suited to these operations,which necessitate information systems for issue treatment and classification at a degree of complexity that is distinct from technology.To overcome such challenges in industrial cyber-physical systems,the Image Processing aided Computer Vision Technology for Fault Detection System(IM-CVFD)is proposed in this research.The Uncertainty Management technique is introduced in addition to achieving optimum knowledge in terms of latency and effectiveness.A thorough simulation was performed in an appropriate processing facility.The study results suggest that the IM-CVFD has a high performance,low error frequency,low energy consumption,and low delay with a strategy that provides.In comparison to traditional approaches,the IM-CVFD produces a more efficient outcome.
文摘Aiming at concrete tasks of information fusion in computer pan vision (CPV) system, information fusion methods are studied thoroughly. Some research progresses are presented. Recognizing of vision testing object is realized by fusing vision information and non vision auxiliary information, which contain recognition of material defects, intelligent robot’s autonomous recognition for parts and computer to defect image understanding and recognition automatically.
基金National Natural Science Foundation of China(No.62176052)。
文摘In the vision transformer(ViT)architecture,image data are transformed into sequential data for processing,which may result in the loss of spatial positional information.While the self-attention mechanism enhances the capacity of ViT to capture global features,it compromises the preservation of fine-grained local feature information.To address these challenges,we propose a spatial positional enhancement module and a wavelet transform enhancement module tailored for ViT models.These modules aim to reduce spatial positional information loss during the patch embedding process and enhance the model’s feature extraction capabilities.The spatial positional enhancement module reinforces spatial information in sequential data through convolutional operations and multi-scale feature extraction.Meanwhile,the wavelet transform enhancement module utilizes the multi-scale analysis and frequency decomposition to improve the ViT’s understanding of global and local image structures.This enhancement also improves the ViT’s ability to process complex structures and intricate image details.Experiments on CIFAR-10,CIFAR-100 and ImageNet-1k datasets are done to compare the proposed method with advanced classification methods.The results show that the proposed model achieves a higher classification accuracy,confirming its effectiveness and competitive advantage.
