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Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques
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作者 Raveena Selvanarayanan Surendran Rajendran Youseef Alotaibi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期759-782,共24页
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
关键词 computer vision coffee berry disease colletotrichum kahawae XG boost shapley additive explanations
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A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images
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作者 Yuejie Li Shijun Li 《Computers, Materials & Continua》 SCIE EI 2024年第3期3041-3070,共30页
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
关键词 6G technology blockchain end-to-end recognition Chinese characters natural scene computer vision algorithms convolutional neural network
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A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts
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作者 Zia Ullah Lin Qi +2 位作者 E.J.Solteiro Pires Arsénio Reis Ricardo Rodrigues Nunes 《Computers, Materials & Continua》 SCIE EI 2024年第8期2387-2421,共35页
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
关键词 computer vision end-of-line visual inspection of antenna parts machine learning algorithms image processing techniques deep learning models
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Clinical Application of Preliminary Breast Cancer Screening for Dense Breasts Using Real-Time AI-Powered Ultrasound with Deep-Learning Computer Vision
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作者 Zhenzhong Zhou Xueqin Xie +3 位作者 Zongjin Yang Zhongxiong Feng Xiaoling Zheng Qian Huang 《Journal of Clinical and Nursing Research》 2024年第6期36-47,共12页
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
关键词 Breast cancer screening ULTRASOUND Lesion detection BI-RADS Deep learning computer vision Cloud computing
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Computer Vision-Based Human Body Posture Correction System
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作者 Yangsen QIU Yukun WANG +2 位作者 Yuchen WU Xinyi QIANG Yunzuo ZHANG 《Mechanical Engineering Science》 2024年第1期1-7,共7页
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
关键词 computer vision human posture deep learning image processing
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Review of Fabric Defect Detection Based on Computer Vision 被引量:3
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作者 朱润虎 辛斌杰 +1 位作者 邓娜 范明珠 《Journal of Donghua University(English Edition)》 CAS 2023年第1期18-26,共9页
In textile inspection field,the fabric defect refers to the destruction of the texture structure on the fabric surface.The technology of computer vision makes it possible to detect defects automatically.Firstly,the ov... In textile inspection field,the fabric defect refers to the destruction of the texture structure on the fabric surface.The technology of computer vision makes it possible to detect defects automatically.Firstly,the overall structure of the fabric defect detection system is introduced and some mature detection systems are studied.Then the fabric detection methods are summarized,including structural methods,statistical methods,frequency domain methods,model methods and deep learning methods.In addition,the evaluation criteria of automatic detection algorithms are discussed and the characteristics of various algorithms are analyzed.Finally,the research status of this field is discussed,and the future development trend is predicted. 展开更多
关键词 computer vision fabric defect detection algorithm evaluation textile inspection
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Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda(Lepidoptera:Noctuidae)based on computer vision 被引量:1
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作者 LÜChun-yang GE Shi-shuai +4 位作者 HE Wei ZHANG Hao-wen YANG Xian-ming CHU Bo WU Kong-ming 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2023年第7期2173-2187,共15页
Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The ... Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The present monitoring and early warning strategies for the fall army worm(FAW)mainly focus on adult population density,but lack an information technology platform for precisely forecasting the reproductive dynamics of the adults.In this study,to identify the developmental status of the adults,we first utilized female ovarian images to extract and screen five features combined with the support vector machine(SVM)classifier and employed male testes images to obtain the testis circular features.Then,we established models for the relationship between oviposition dynamics and the developmental time of adult reproductive organs using laboratory tests.The results show that the accuracy of female ovary development stage determination reached 91%.The mean standard error(MSE)between the actual and predicted values of the ovarian developmental time was 0.2431,and the mean error rate between the actual and predicted values of the daily oviposition quantity was 12.38%.The error rate for the recognition of testis diameter was 3.25%,and the predicted and actual values of the testis developmental time in males had an MSE of 0.7734.A WeChat applet for identifying the reproductive developmental state and predicting reproduction of S.frugiperda was developed by integrating the above research results,and it is now available for use by anyone involved in plant protection.This study developed an automated method for accurately forecasting the reproductive dynamics of S.frugiperda populations,which can be helpful for the construction of a population monitoring and early warning system for use by both professional experts and local people at the county level. 展开更多
关键词 Spodoptera frugiperda computer vision OVARY TESTIS WeChat applet
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Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture 被引量:1
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作者 R.Punithavathi A.Delphin Carolina Rani +4 位作者 K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2759-2774,共16页
Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital ... Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures. 展开更多
关键词 Precision agriculture smart farming weed detection computer vision deep learning
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Current applications of artificial intelligence-based computer vision in laparoscopic surgery 被引量:1
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作者 Kangwei Guo Haisu Tao +4 位作者 Yilin Zhu Baihong Li Chihua Fang Yinling Qian Jian Yang 《Laparoscopic, Endoscopic and Robotic Surgery》 2023年第3期91-96,共6页
Recent advances in artificial intelligence(AI)have sparked a surge in the application of computer vision(CV)in surgical video analysis.Laparoscopic surgery produces a large number of surgical videos,which provides a n... Recent advances in artificial intelligence(AI)have sparked a surge in the application of computer vision(CV)in surgical video analysis.Laparoscopic surgery produces a large number of surgical videos,which provides a new opportunity for improving of CV technology in laparoscopic surgery.AI-based CV techniques may leverage these surgical video data to develop real-time automated decision support tools and surgeon training systems,which shows a new direction in dealing with the shortcomings of laparoscopic surgery.The effectiveness of CV applications in surgical procedures is still under early evaluation,so it is necessary to discuss challenges and obstacles.The review introduced the commonly used deep learning algorithms in CV and described their usage in detail in four application scenes,including phase recognition,anatomy detection,instrument detection and action recognition in laparoscopic surgery.The currently described applications of CV in laparoscopic surgery are limited.Most of the current research focuses on the identification of workflow and anatomical structure,while the identification of instruments and surgical actions is still awaiting further breakthroughs.Future research on the use of CV in laparoscopic surgery should focus on applications in more scenarios,such as surgeon skill assessment and the development of more efficient models. 展开更多
关键词 Artificial intelligence computer vision Deep learning Laparoscopic surgery
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Measuring tree stem diameters and straightness with depth-image computer vision
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作者 Hoang Tran Keith Woeste +2 位作者 Bowen Li Akshat Verma Guofan Shao 《Journal of Forestry Research》 SCIE CAS CSCD 2023年第5期1395-1405,共11页
Current techniques of forest inventory rely on manual measurements and are slow and labor intensive.Recent developments in computer vision and depth sensing can produce accurate measurement data at significantly reduc... Current techniques of forest inventory rely on manual measurements and are slow and labor intensive.Recent developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor costs.We developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem straightness.Time use,mean absolute error(MAE),and root mean squared error(RMSE)metrics were used to compare the system against manual methods,and to compare the system against itself(reproducibility).Depth-derived diameter measurements of the stems at the heights of 0.3,1.4,and 2.7 m achieved RMSE of 1.7,1.5,and 2.7 cm,respectively.The ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained foresters.The ForSense system was also consistent,achieving sub-centimeter diameter difference with subsequent measures and less than 4%difference in straightness value between runs.This method of forest inventory,which is based on depth-image computer vision,is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion(SFM)photogrammetry and ground-based LiDAR or terrestrial laser scanning(TLS). 展开更多
关键词 Forest inventory Depth sensing computer vision Tree diameter Stem straightness Trunk volume
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Surface Characteristics Measurement Using Computer Vision:A Review
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作者 AbdulWahab Hashmi Harlal Singh Mali +2 位作者 Anoj Meena Mohammad Farukh Hashmi Neeraj Dhanraj Bokde 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第5期917-1005,共89页
Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured.For any components to execute their intended functions and operations,surface quality is considered e... Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured.For any components to execute their intended functions and operations,surface quality is considered equally significant to dimensional quality.Surface Roughness(Ra)is a widely recognized measure to evaluate and investigate the surface quality of machined parts.Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100%inspection and examination because of the time and efforts involved in performing the measurement.However,Machine vision has emerged as the innovative approach to executing the surface roughness measurement.It can provide economic,automated,quick,and reliable solutions.This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics,i.e.,surface roughness,waviness,flatness,surface texture,etc.,machine vision parameters.This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement. 展开更多
关键词 Machine vision surface roughness computer vision machining parameters surface characterization
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A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations
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作者 Mariusz Ostrowski Bartlomiej Blachowski +3 位作者 Bartosz Wójcik Mateusz Żarski Piotr Tauzowski Łukasz Jankowski 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2023年第1期1-17,共17页
Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points o... Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure.However,CV methods produce significantly more measurement errors.Thus,computer vision-based structural health monitoring(CVSHM)requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data.In this paper a complete CVSHM framework is proposed,and three damage assessment methods are tested.The first is the augmented inverse estimate(AIE),proposed by Peng et al.in 2021.This method is designed to work with highly contaminated measurement data,but it fails with a large noise provided by CV measurement.The second method,as proposed in this paper,is based on the AIE,but it introduces a weighting matrix that enhances the conditioning of the problem.The third method,also proposed in this paper,introduces additional constraints in the optimization process;these constraints ensure that the stiffness of structural elements can only decrease.Both proposed methods perform better than the original AIE.The latter of the two proposed methods gives the best results,and it is robust with respect to the selected coefficients,as required by the algorithm. 展开更多
关键词 computer vision structural health monitoring physics-based graphical models augmented inverse estimate model updating non-negative least square method
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A Computer Vision-Based System for Metal Sheet Pick Counting
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作者 Jirasak Ji Warut Pannakkong Jirachai Buddhakulsomsiri 《Computers, Materials & Continua》 SCIE EI 2023年第5期3643-3656,共14页
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
关键词 computer vision manual operation operation monitoring material counting
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Nondestructive Testing of Bridge Stay Cable Surface Defects Based on Computer Vision
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作者 Fengyu Xu Masoud Kalantari +1 位作者 Bangjian Li Xingsong Wang 《Computers, Materials & Continua》 SCIE EI 2023年第4期2209-2226,共18页
The automatically defect detection method using vision inspectionis a promising direction. In this paper, an efficient defect detection method fordetecting surface damage to cables on a cable-stayed bridge automatical... The automatically defect detection method using vision inspectionis a promising direction. In this paper, an efficient defect detection method fordetecting surface damage to cables on a cable-stayed bridge automatically isdeveloped. A mechanism design method for the protective layer of cables of abridge based on vision inspection and diameter measurement is proposed bycombining computer vision and diameter measurement techniques. A detectionsystem for the surface damages of cables is de-signed. Images of cablesurfaces are then enhanced and subjected to threshold segmentation by utilizingthe improved local grey contrast enhancement method and the improvedmaximum correlation method. Afterwards, the data obtained through diametermeasurement are mined by employing the moving average method. Imageenhancement, threshold segmentation, and diameter measurement methodsare separately validated experimentally. The experimental test results showthat the system delivers recall ratios for type-I and II surface defects of cablesreaching 80.4% and 85.2% respectively, which accurately detects bulges oncable surfaces. 展开更多
关键词 Defect detection computer vision bridge cable image enhancement
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Recognition of mortar pumpability via computer vision and deep learning
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作者 Hao-Zhe Feng Hong-Yang Yu +2 位作者 Wen-Yong Wang Wen-Xuan Wang Ming-Qian Du 《Journal of Electronic Science and Technology》 EI CAS CSCD 2023年第3期73-81,共9页
The mortar pumpability is essential in the construction industry,which requires much labor to estimate manually and always causes material waste.This paper proposes an effective method by combining a 3-dimensional con... The mortar pumpability is essential in the construction industry,which requires much labor to estimate manually and always causes material waste.This paper proposes an effective method by combining a 3-dimensional convolutional neural network(3D CNN)with a 2-dimensional convolutional long short-term memory network(ConvLSTM2D)to automatically classify the mortar pumpability.Experiment results show that the proposed model has an accuracy rate of 100%with a fast convergence speed,based on the dataset organized by collecting the corresponding mortar image sequences.This work demonstrates the feasibility of using computer vision and deep learning for mortar pumpability classification. 展开更多
关键词 Classification computer vision Deep learning PUMPABILITY 2-dimensional convolutional long short-term memory network (ConvLSTM2D) 3-dimensional convolutional neural network(3D CNN)
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A comprehensive survey of robust deep learning in computer vision
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作者 Jia Liu Yaochu Jin 《Journal of Automation and Intelligence》 2023年第4期175-195,共21页
Deep learning has presented remarkable progress in various tasks.Despite the excellent performance,deep learning models remain not robust,especially to well-designed adversarial examples,limiting deep learning models ... Deep learning has presented remarkable progress in various tasks.Despite the excellent performance,deep learning models remain not robust,especially to well-designed adversarial examples,limiting deep learning models employed in security-critical applications.Therefore,how to improve the robustness of deep learning has attracted increasing attention from researchers.This paper investigates the progress on the threat of deep learning and the techniques that can enhance the model robustness in computer vision.Unlike previous relevant survey papers summarizing adversarial attacks and defense technologies,this paper also provides an overview of the general robustness of deep learning.Besides,this survey elaborates on the current robustness evaluation approaches,which require further exploration.This paper also reviews the recent literature on making deep learning models resistant to adversarial examples from an architectural perspective,which was rarely mentioned in previous surveys.Finally,interesting directions for future research are listed based on the reviewed literature.This survey is hoped to serve as the basis for future research in this topical field. 展开更多
关键词 ROBUSTNESS Deep learning computer vision SURVEY Adversarial attack Adversarial defenses
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Computer vision technology in log volume inspection 被引量:3
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作者 汪亚明 黄文清 赵匀 《Journal of Forestry Research》 SCIE CAS CSCD 2002年第1期67-70,84,共4页
Log volume inspection is very important in forestry research and paper making engineering. This paper proposed a novel approach based on computer vision technology to cope with log volume inspection. The needed hardwa... Log volume inspection is very important in forestry research and paper making engineering. This paper proposed a novel approach based on computer vision technology to cope with log volume inspection. The needed hardware system was analyzed and the details of the inspection algorithms were given. A fuzzy entropy based on image enhancement algorithm was presented for enhancing the image of the cross-section of log. In many practical applications the cross-section is often partially invisible, and this is the major obstacle for correct inspection. To solve this problem, a robust Hausdorff distance method was proposed to recover the whole cross-section. Experiment results showed that this method was efficient. 展开更多
关键词 Log volume Automatic inspection computer vision Fuzzy entropy Hausdorff distance
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Review on the proceeding of automatic seedlings classification by computer vision 被引量:1
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作者 杨延竹 赵学增 +1 位作者 王伟杰 吴羡 《Journal of Forestry Research》 SCIE CAS CSCD 2002年第3期245-249,252,共5页
The classification of seedlings is important to ensure the viability of seedlings after transplantation and is acknowledged as a key factor in forestation and environmental improvement. Based on numerous papers on aut... The classification of seedlings is important to ensure the viability of seedlings after transplantation and is acknowledged as a key factor in forestation and environmental improvement. Based on numerous papers on automatic seedling classification (ASC), the seedling grading theory, traditional grading methods, the background and the proceeding of ASC techniques are described. The automation of the measurement of seedling morphological characteristics by photoelectric meters and computer vision is studied, and the automatic methods of the current grading systems are described respectively. And the further researches on ASC by computer vision are proposed. 展开更多
关键词 Seedlings classification AUTOMATION Morphological characteristic computer vision
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Application of Computer Vision Technique to Maize Variety Identification
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作者 孙钟雷 李宇 何伟 《Agricultural Science & Technology》 CAS 2013年第5期783-786,796,共5页
Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been su... Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted. 展开更多
关键词 Maize variety identification computer vision Image processing Feature extraction Pattern recognition
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Application of Computer Vision Technology in Agriculture 被引量:6
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作者 黄喜梅 毕建杰 +3 位作者 张楠 丁筱玲 李飞 侯发东 《Agricultural Science & Technology》 CAS 2017年第11期2158-2162,共5页
With the development of image processing technology and computer, computer vision technology has been widely used in the production of agriculture,and has made many important achievements. This paper reviews its-resea... With the development of image processing technology and computer, computer vision technology has been widely used in the production of agriculture,and has made many important achievements. This paper reviews its-research progress on diagnosis of agricultural products, water diagnosis, weed identification,product quality testing and grading, agricultural picking and sorting and other as- pects, and finally put forward its existing problems and prospects for the future. 展开更多
关键词 Image processing computer vision technology Agriculture production PROSPECT
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