Computer Science ›› 2024, Vol. 51 ›› Issue (10): 261-275.doi: 10.11896/jsjkx.230800158
• Computer Graphics & Multimedia • Previous Articles Next Articles
HONG Jingshan1, ZHU Yingdan2, SONG Kangkang2, LYU Dongxi2, CHEN Mingda2, HU Haigen1
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| [1]PENG T G,HE Y H,SUN C.Identification Technology of Dif-ferential Thickness Printing Line in the Surface Detection System of Tinned Strip Steel [J].Baosteel Technology,2015(2):3. [2]D'ORAZIO T,LEO M,DISTANTE A,et al.Automatic ultrasonic inspection for internal defect detection in composite materials [J].NDT & E International,2008,41(2):145-154. [3]STAUDE A,BARTSCHER M,EHRIG K,et al.Quantification of the capability of micro-CT to detect defects in castings using a new test piece and a voxel-based comparison method [J].NDT &E International,2011,44(6):531-536. [4]SODSAI K,NOIPITAK M,SAE-TANG W.Detection of Corrosion under Coated Surface by Eddy Current Testing Method[C]//2019 7th International Electrical Engineering Congress(iEECON).IEEE,2019. [5]YANG C L,ZHANG X Q.Overview of Material Defect Detection Applications Based on Machine Vision and Deep Learning [J].Material Introduction,2022(16):1-19. [6]LUO D L,CAI Y X,YANG Z H,et al.A review of deep lear-ning methods for industrial defect detection [J].Chinese Science:Information Science,2022,52(6):1002-1039. [7]BULUSU S,KAILKHURA B,LI B,et al.Anomalous Example Detection in Deep Learning:A Survey [J].arXiv.2003.06979,2020. [8]TANG J,WANG Z,ZHANG H,et al.A lightweight surface defect detection framework combined with dual-domain attention mechanism [J].Expert Systems with Applications,2024,238:121726. [9]CUI L,JIANG X,XU M,et al.SDDNet:A fast and accuratenetwork for surface defect detection [J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-13. [10]WANG B,WANG M,YANG J,et al.YOLOv5-CD:Strip steel surface defect detection method based on coordinate attention and a decoupled head [J].Measurement:Sensors,2023,30:100909. [11]XU H.FCD-YOLO:Improved YOLOv5 Based on DecoupledHead and Attention Mechanism for Defect Detection on Printed Circuit Board [C]//Proceedings of the 2022 2nd International Conference on Networking Systems of AI(INSAI).IEEE,2022:7-11. [12]CHEN C,SHEN F,DAI C.SwinTD:Transformer-based detection network for foreign objects in the cut section of tobacco packets [J].Measurement,2023,216:112953. [13]ZHAO L Y,WU Y Q.Research progress in surface defect detection methods based on machine vision [J].Journal of Instrumentation,2022,43(1):198-219. [14]MI C F,LU K,WANG W Y,et al.Research progress on surface defect detection of strip steel based on machine vision [J].Journal of Anhui University of Technology(Natural Science Edition),2022,39(2):180-188. [15]JIN Y G,ZHANG T,YANG Y N,et al.A review of product defect detection methods based on deep learning [J].Journal of Dalian University for Nationalities,2020,22(5):420-427. [16]ZHAO K L,JIN X L,WANG Y Z.A Review of Small Sample Learning Research [J].Journal of Software Science,2021,32(2):349-369. [17]SUNG F,YANG Y,ZHANG L,et al.Learning to Compare:Relation Network for Few-Shot Learning [C]//CVPR2018.2018. [18]KOCH G,ZEMEL R,SALAKHUTDINOV R.Siamese neuralnetworks for one-shot image recognition[C]//ICML Deep Learning Workshop.2015. [19]VINYALS O,BLUNDELL C,LILLICRAP T,et al.MatchingNetworks for One Shot Learning [J／OL].Matching Networks for One Shot Learning,2016,29.https://proceedings.neurips.cc/paper/2016/hash/90e1357833654983612fb05e3ec9148c-Abstract.html. [20]SNELL J,SWERSKY K,ZEMEL R S.Prototypical Networksfor Few-shot Learning [J／OL].Advances in Neural Information Processing Systems,2017,30.https://proceedings.neurips.cc/paper_files/paper/2017/hash/cb8da6767461f2812ae4290eac7cbc42-Abstract.html. [21]MAO J,XU G,HE L,et al.Attention-relation network for mobile phone screen defect classification via a few samples [J].Di-gital Communications and Networks,2024,10(4):1113-1120. [22]KNAAK C,VON QUERFURTH B E,HOLLATZ S,et al.Defect localization during laser microwelding of battery connectors using long exposure imaging and few-shot learning [J].Procedia CIRP,2022,111:790-795. [23]WU K,TAN J,LIU C.Cross-domain few-shot learning ap-proach for lithium-ion battery surface defects classification using an improved siamese network [J].IEEE Sensors Journal,2022,22(12):11847-11856. [24]GENG H,YANG F,ZENG X,et al.When wafer failure pattern classification meets few-shot learning and self-supervised lear-ning[C]//Proceedings of the 2021 IEEE/ACM International Conference on Computer Aided Design(ICCAD).IEEE,2021:1-8. [25]YU N,CHEN H,XU Q,et al.Wafer map defect recognition with few shot learning based on hybrid self-attention mechanism and prototype network[C]//Proceedings of the 2021 China Automation Congress(CAC).IEEE,2021:4128-4134. [26]CUI Z,WANG Q,GUO J,et al.Few-shot classification of façade defects based on extensible classifier and contrastive learning [J].Automation in Construction,2022,141:104381. [27]GUO Y,FAN Y,XIANG Z,et al.Zero-sample surface defect detection and classification based on semantic feedback neural network [J].arXiv:2106.07959,2021. [28]YUN S,HAN D,CHUN S,et al.CutMix:Regularization Strategy to Train Strong Classifiers With Localizable Features[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:6023-6032. [29]ZHANG H,CISSE M,DAUPHIN Y N,et al.mixup:Beyondempirical risk minimization [J].arXiv:1710.09412,2017. [30]LI C L,SOHN K,YOON J,et al.CutPaste:Self-SupervisedLearning for Anomaly Detection and Localization [EB/OL].http://openaccess.thecvf.com/content/CVPR2021/html/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.html. [31]GHIASI G,CUI Y,SRINIVAS A,et al.Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation[C]//Computer Vision and Pattern Recognition.IEEE,2021:2918-2928. [32]REED S,AKATA Z,YAN X,et al.Generative adversarial text to image synthesis[C]//International Conference on Machine Learning.PMLR,2016:1060-1069. [33]SONG J,KONG K,PARK Y I,et al.AnoSeg:Anomaly segmentation network using self-supervised learning[J].arXiv:2110.03396,2021. [34]CHIU M C,CHEN T M.Applying data augmentation and mask R-CNN-based instance segmentation method for mixed-type wafer maps defect patterns classification [J].IEEE Transactions on Semiconductor Manufacturing,2021,34(4):455-463. [35]WANG G,RAO Z,SUN H,et al.A belt tearing detection me-thod of YOLOv4-BELT for multi-source interference environment [J].Measurement,2022,189:110469. [36]NIU S,LI B,WANG X,et al.Defect image sample generationwith GAN for improving defect recognition [J].IEEE Transactions on Automation Science and Engineering,2020,17(3):1611-1622. [37]LI D,XIE W,WANG B,et al.Data augmentation and layered deformable mask R-CNN-based detection of wood defects [J].IEEE Access,2021,9:108162-108174. [38]ZHU J Y,PARK T,ISOLA P,et al.Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2223-2232. [39]YAO M H,SHEN Y Q,LIU J M.Unsupervised Domain Adaptation Method for Surface Defects of Workpieces [J].Small Micro Computer Systems,2021,42(6):1256-1261. [40]ZHANG J H.Research on fabric defect detection and classification methods based on unsupervised segmentation and ELM [D].Kunming:Kunming University of Science and Technology,2018. [41]KIM M,JO H,RA M,et al.Weakly-Supervised Defect Segmentation on Periodic Textures Using CycleGAN [J].IEEE Access,2020,8:176202-176216. [42]MEI S,YANG H,YIN Z.An unsupervised-learning-based approach for automated defect inspection on textured surfaces [J].IEEE Transactions on Instrumentation and Measurement,2018,67(6):1266-1277. [43]XIA D,YU Z,DENG X.A Real-time Unsupervised Two-stage Framework for Fabric Defect Detection[C]//proceedings of the 2021 3rd International Academic Exchange Conference on Science and Technology Innovation(IAECST).IEEE,2021:535-538. [44]LIU K,LI A,WEN X,et al.Steel Surface Defect DetectionUsing GAN and One-Class Classifier[C]//2019 25th Interna-tional Conference on Automation and Computing(ICAC).IEEE,2019:1-6. [45]PARK H,NOH J,HAM B.Learning Memory-guided Normality for Anomaly Detection [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:14372-14381. [46]MUJEEB A,DAI W,ERDT M,et al.Unsupervised Surface Defect Detection Using Deep Autoencoders and Data Augmentation[C]//2018 International Conference on Cyberworlds(CW).IEEE,2018:391-398. [47]ZHOU L,LING X,ZHU S,et al.An Self-supervised Learning &Self-attention Based Method for Defects Classification on PCB Surface Images[C]//Proceedings of the 2021 2nd International Conference on Electronics,Communications and Information Technology(CECIT).IEEE,2021:229-234. [48]DOSOVITSKIY A,BEYER L,KOLESNIKOV A,et al.Animage is worth 16x16 words:Transformers for image recognition at scale [J].arXiv:2010.11929,2020. [49]ZHOU Q,WANG H,TANG Y,et al.Defect Detection Method Based on Knowledge Distillation [J].IEEE Access,2023,11:35866-35873. [50]XU R,HAO R,HUANG B.Efficient surface defect detectionusing self-supervised learning strategy and segmentation network [J].Advanced Engineering Informatics,2022,52:101566. [51]KAHNG H,KIM S B.Self-supervised representation learningfor wafer bin map defect pattern classification [J].IEEE Tran-sactions on Semiconductor Manufacturing,2020,34(1):74-86. [52]KIMURA D,CHAUDHURY S,NARITA M,et al.Adversarial Discriminative Attention for Robust Anomaly Detection[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.2020:2172-2181. [53]ZHANG G,PAN Y,ZHANG L.Semi-supervised learning with GAN for automatic defect detection from images [J].Automation in Construction,2021,128:103764. [54]ODENA A.Semi-Supervised Learning with Generative Adversarial Networks [J].arXiv:1606.01583,2016. [55]WANG W,SU C.Semi-supervised semantic segmentation network for surface crack detection [J].Automation in Construction,2021,128:103786. [56]LIU J,SONG K,FENG M,et al.Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection [J].Optics and Lasers in Engineering,2021,136:106324. [57]DI H,KE X,PENG Z,et al.Surface defect classification of steels with a new semi-supervised learning method [J].Optics and Lasers in Engineering,2019,117:40-48. [58]EVO I.Semi-supervised neural network training method forfast-moving object detection[C]//Symposium on Neural Network Applications in Electrical Engineering.2018:1-6. [59]ZHOU B,KHOSLA A,LAPEDRIZA A,et al.Learning DeepFeatures for Discriminative Localization [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:2921-2929. [60]SELVARAJU R R,COGSWELL M,DAS A,et al.Grad-CAM:Visual Explanations from Deep Networks via Gradient-Based Localization[C]//IEEE International Conference on Computer Vision.IEEE,2017:618-626. [61]CHATTOPADHYAY A,SARKAR A,HOWLADER P,et al.Grad-CAM++:Generalized Gradient-based Visual Explanations for Deep Convolutional Networks [C]//2018 IEEE Winter Conference on Applications of Computer Vision(WACV).IEEE,2018:839-847. [62]ZHANG D,SONG K,XU J,et al.An image-level weakly supervised segmentation method for No-service rail surface defect with size prior [J].Mechanical Systems and Signal Processing,2022,165:108334. [63]JIANG X Y.Weak supervised PCB carbon oil defect detectionbased on neural networks [D].Hangzhou:Zhejiang University,2020. [64]SUN K,XIAO B,LIU D,et al.Deep high-resolution representation learning for human pose estimation [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:5693-5703. [65]HE X,WU L,SONG F,et al.Research on Fabric Defect Detection Based on Deep Fusion DenseNet-SSD Network[C]//Proceedings of the 2020 International Conference on Wireless Communication and Sensor Networks.2020:60-64. [66]LIU Z,HUO Z,LI C,et al.DLSE-Net:A robust weakly supervised network for fabric defect detection [J].Displays,2021,68:102008. [67]VENKATARAMANAN S,PENG K C,SINGH R V,et al.Attention Guided Anomaly Localization in Images [C]//European Conference on Computer Vision.Cham:Springer International Publishing,2020:485-503. [68]KINGMA D P,WELLING M.Auto-Encoding Variational Bayes [J].arXiv:1312.6114,2013. [69]LIU T,ZHANG W,YAN S.A novel image enhancement algorithm based on stationary wavelet transform for infrared thermography to the de-bonding defect in solid rocket motors [J].Mechanical Systems and Signal Processing,2015,62:366-380. [70]LI J,WANG J,LI Z,et al.A novel algorithm of IC defect imagesenhancement based on histogram equalization and IHS transform [C]//2013 International Conference on Anti-Counterfeiting,Security and Identification(ASID).IEEE,2013:1-5. [71]ZOU G,LI T,LI G,et al.A visual detection method of tile surface defects based on spatial-frequency domain image enhancement and region growing[C]//2019 Chinese Automation Congress(CAC).IEEE,2019:1631-1636. [72]CHENG L,KERSEMANS M.Dual-IRT-GAN:A defect-awaredeep adversarial network to perform super-resolution tasks in infrared thermographic inspection [J].Composites Part B:Engineering,2022,247:110309. [73]MIRZA M,OSINDERO S.Conditional Generative AdversarialNets [J].arXiv:1411.1784,2014. [74]ISOLA P,ZHU J Y,ZHOU T,et al.Image-to-Image Translation with Conditional Adversarial Networks [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1125-1134. [75]LEDIG C,THEIS L,HUSZAR F,et al.Photo-Realistic SingleImage Super-Resolution Using a Generative Adversarial Network [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4681-4690. [76]GAO J.Image super-resolution reconstruction based on deeplearning and its application in PCB welding quality detection [D].Xi'an:Xidian University. [77]QIN J,CHEN L,JEON S,et al.Progressive Interaction-Lear-ning Network for Lightweight Single-Image Super-Resolution in Industrial Applications [J].IEEE Transactions on Industrial Informatics,2022,19(2):2183-2191. [78]SHEN Z,DANG H,SUN M,et al.Application of Generating Adversarial Networks in High-light Removal of Wheel Hub Surface[C]//2019 International Conference on Advanced Mechatronic Systems(ICAMechS).2019:12-15. [79]YAN L,ZHENG W,WANG F Y,et al.Joint image-to-imagetranslation with denoising using enhanced generative adversarial networks [J].Signal Processing:Image Communication,2021,91:116072. [80]LU H.Research on Surface Defect Detection Method of Carbon Fiber Prepreg Based on Machine Vision [D].Jinan:Shandong University,2020. [81]ENGIN D,GEN A,EKENEL H K.Cycle-Dehaze:Enhanced CycleGAN for Single Image Dehazing[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).IEEE,2018:825-833. [82]ZHANG H,JIANG L,LI C.CS-ResNet:Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection [J].Expert Systems with Applications,2021,185:115673. [83]QU Z,MEI J,LIU L,et al.Crack detection of concrete pavement with cross-entropy loss function and improved VGG16 network model [J].IEEE Access,2020,8:54564-54573. [84]ZHANG Z,WEN G,CHEN S.Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding [J].Journal of Manufacturing Processes,2019,45:208-216. [85]MA L,XIE W,ZHANG Y.Blister Defect Detection Based onConvolutional Neural Network for Polymer Lithium-Ion Battery [J].Applied Sciences,2019,9(6):1085. [86]CHEN R,CAI D,HU X,et al.Defect Detection Method of Aluminum Profile Surface Using Deep Self-Attention Mechanism under Hybrid Noise Conditions [J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-9. [87]LIU Q,WANG C,LI Y,et al.A Fabric Defect Detection Method Based on Deep Learning [J].IEEE Access,2022,10:4284-4296. [88]BOCHKOVSKIY A,WANG C Y,LIAO H Y M.Yolov4:Optimal speed and accuracy of object detection [J].arXiv:2004.10934,2020. [89]HE K,ZHANG X,REN S,et al.Spatial Pyramid Pooling inDeep Convolutional Networks for Visual Recognition [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,37(9):1904-1916. [90]LUO H,XU G L.Rail surface defect detection based on image enhancement and deep learning [J].Journal of Railway Science and Engineering,2021,18(3):623-629. [91]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149. [92]WEI L,DRAGOMIR A,DUMITRU E,et al.SSD:Single Shot MultiBox Detector[C]//Computer Vision－ECCV 2016:14th European Conference,Amsterdam,The Netherlands,October 11－14,2016,Proceedings,Part I.Springer International Publishing.2016:21-37. [93]HUANG G,LIU Z,LAURENS V,et al.Densely ConnectedConvolutional Networks [C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2017:4700-4708. [94]XIONG Q,TANG S,LI Y,et al.Research on Surface Quality Monitoring of Workpiece Based on CornerNet[C]//Procee-dings of the 2021 7th Annual International Conference on Network and Information Systems for Computers(ICNISC).IEEE.2021:430-438. [95]LAW H,DENG J.CornerNet:Detecting Objects as Paired Keypoints[J].International Journal of Computer Vision,2020,128(3):642-656. [96]LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature Pyramid Networks for Object Detection [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125. [97]XU Y,LI D,XIE Q,et al.Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN [J].Measurement,2021,178:109316. [98]HE K,GKIOXARI G,PIOTR DOLLÁ R,et al.Mask R-CNN[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,42(2):386-397. [99]YANG Y J,TANG L L,ZHENG Y.Improving Full Convolutional Networks to Optimize Insulator Defect Detection [J].Mechanical Design and Manufacturing,2021,(3):177-1780. [100]LONG J,SHELHAMER E,DARRELL T.Fully Convolutional Networks for Semantic Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,39(4):640-651. [101]JIANG X,TIAN S,ZHU Z,et al.Decision Fusion Network with Perception Fine-tuning for Defect Classification [J].arXiv:2309.12630,2023. [102]LIN D,LI Y,PRASAD S,et al.CAM-UNET:Class Activation MAP Guided UNET with Feedback Refinement for Defect Segmentation[C]//2020 IEEE International Conference on Image Processing(ICIP).2020:2131-2135. [103]RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolu-tional Networks for Biomedical Image Segmentation [C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer International Publishing,2015:234-241. [104]LIN D,CHENG Y,LI Y,et al.MLSA-UNet:End-to-End Multi-Level Spatial Attention Guided UNet for Industrial Defect Segmentation [C]//2022 IEEE International Conference on Image Processing(ICIP).2022:441-445. [105]LIU Z,LIN Y,CAO Y,et al.Swin Transformer:Hierarchical Vision Transformer using Shifted Windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:10012-10022. [106]ZHAO J.Surface Defect Classification with Vision Transformer [C]//2022 3rd International Conference on Intelligent Design(ICID).2022:124-128. [107]ZHANG Z,GONG Z,HONG Q,et al.Swin-Transformer Based Classification for Rice Diseases Recognition [C]//2021 International Conference on Computer Information Science and Artificial Intelligence(CISAI).2021:153-156. [108]XIE X,LIU H,NA Z,et al.DPiT:Detecting Defects of Photovoltaic Solar Cells With Image Transformers [J].IEEE Access,2021,9:154292-154303. [109]CARION N,MASSA F,SYNNAEVE G,et al.End-to-End Object Detection with Transformers [C]//European Conference on Computer Vision.Cham:Springer International Publishing,2020:213-229. [110]ZHU X,SU W,LU L,et al.Deformable detr:Deformable transformers for end-to-end object detection[J].arXiv:2010.04159,2020. [111]LI F,ZHANG H,LIU S,et al.Dn-detr:Accelerate detr training by introducing query denoising[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:13619-13627. [112]CARON M,TOUVRON H,MISRA I,et al.Emerging properties in self-supervised vision transformers[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:9650-9660. [113]SONG D,LIU B,LI Y.Based on end-to-end object detection algorithm with transformers for detecting wafer maps[C]//2022 International Conference on Computer Network,Electronic and Automation(ICCNEA).IEEE,2022:297-302. [114]ZHANG K,LOU W,WANG J,et al.PA-DETR:End-to-endvisually indistinguishable bolt defects detection method based on transmission line knowledge reasoning[J].IEEE Transactions on Instrumentation and Measurement,2023,72:1-14. [115]DANG L M,WANG H,LI Y,et al.DefectTR:End-to-end defect detection for sewage networks using a transformer[J].Construction and Building Materials,2022,325:126584. [116]ZHANG H,XIONG W,ZHANG W,et al.Yarn-dyed Shirt Piece Defect Detection Based on U-shaped Swin Transformer Auto-encoder[C]//2022 IEEE 11th Data Driven Control and Learning Systems Conference(DDCLS).IEEE,2022:443-448. [117]ÜZEN H,TÜRKOĞLU M,YANIKOGLU B,et al.Swin-MFINet:Swin transformer based multi-feature integration network for detection of pixel-level surface defects[J].Expert Systems with Applications,2022,209:118269. [118]YEUNG C C,LAM K M.Attentive Boundary-Aware Fusion for Defect Semantic Segmentation Using Transformer [J].IEEE Transactions on Instrumentation and Measurement,2023,72:1-13. [119]GAO L,ZHANG J,YANG C,et al.Cas-VSwin transformer:A variant swin transformer for surface-defect detection [J].Computers in Industry,2022,140:103689. [120]WAN H,GAO L,YUAN Z,et al.A novel transformer modelfor surface damage detection and cognition of concrete bridges [J].Expert Systems with Applications,2023,213:119019. [121]WANG J,XU G,YAN F,et al.Defect transformer:An efficient hybrid transformer architecture for surface defect detection [J].Measurement,2023,211:112614. [122]HU Q,TANG B,JIANG L,et al.Rail Surface Defects DetectionBased on Yolo v5 Integrated with Transformer[C]//2022 IEEE 5th International Conference on Electronics Technology(ICET).IEEE,2022:1131-1135. [123]XU S,SHAO Z.An improved faster RCNN based on SwinTransformer for surface defect detection of metal workpieces[C]//Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering.2022:120-125. [124]JIANG X,GUO K,LU Y,et al.CINFormer:Transformer network with multi-stage CNN feature injection for surface defect segmentation [J].arXiv:2309.12639,2023. [125]HOWARD A G,ZHU M,CHEN B,et al.Mobilenets:Efficient convolutional neural networks for mobile vision applications [J].arXiv:1704.04861,2017. [126]SANDLER M,HOWARD A,ZHU M,et al.Mobilenetv2:Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:4510-4520. [127]HOWARD A,SANDLER M,CHU G,et al.Searching for mobilenetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:1314-1324. [128]CHOLLET F.XCEPTION:Deep Learning with Depthwise Sepa-rable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1251-1258. [129]ZHANG X,ZHOU X,LIN M,et al.Shufflenet:An extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6848-6856. [130]MA N,ZHANG X,ZHENG H T,et al.Shufflenet v2:Practical guidelines for efficient cnn architecture design[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:116-131. [131]KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNetClassification with Deep Convolutional Neural Networks [J/OL].Advances in Neural Information Processing Systems,2012,25(2).https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html. [132]XIE S,GIRSHICK R,DOLLÁ R P,et al.Aggregated residualtransformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1492-1500. [133]PAN H,PANG Z,WANG Y,et al.A new image recognition and classification method combining transfer learning algorithm and mobilenet model for welding defects [J].IEEE Access,2020,8:119951-119960. [134]HAO X,DONG T,ZHANG D.A Highly Efficient Surface Defect Detection Approach for Hot Rolled Strip Steel Based on Deep Learning [C]//Proceedings of the 2021 6th International Conference on Robotics and Automation Engineering(ICRAE).IEEE,2021:318-322. [135]TANG L,WANG S Q,JIN H B,et al.Insulator identification based on improved Xception method [J].Smart Power,2022,50(2):69-74. [136]LIANG Q,ZHU W,SUN W,et al.In-line inspection solution for codes on complex backgrounds for the plastic container industry [J].Measurement,2019,148:106965. [137]HAN K,GUO J,TANG Y,et al.Pyramidtnt:Improved trans-former-in-transformer baselines with pyramid architecture[J].arXiv:2201.00978,2022. [138]JIANG S X,ZHENG Y R,LI Y,et al.A Transformer based wafer defect detection method and system:CN202210784530.4 [P].[2024-07-09].https://cprs.patentstar.com.cn/Search/Detail?ANE=9BHB8DFA9GBA9HGG7BFA9CHA9ADB9BCC9DHF9HAD9GHH4BDA. [139]FAN X,LIU H.FlexFormer:Flexible Transformer for efficient visual recognition [J].Pattern Recognition Letters,2023,169:95-101. [140]YAN F,JIANG X,LU Y,et al.Global Context AggregationNetwork for Lightweight Saliency Detection of Surface Defects[J].arXiv:2309.12641,2023. [141]IANDOLA F N,HAN S,MOSKEWICZ M W,et al.SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size[J].arXiv:1602.07360,2016. [142]HAN K,WANG Y,TIAN Q,et al.Ghostnet:More featuresfrom cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:1580-1589. [143]TAN M,LE Q.Efficientnet:Rethinking model scaling for con-volutional neural networks[C]//International Conference on Machine Learning.PMLR,2019:6105-6114. [144]TAN M,LE Q.Efficientnetv2:Smaller models and faster trai-ning[C]//International Conference on Machine Learning.PMLR,2021:10096-10106. [145]RIPPEL O,MERTENS P,MERHOF D.Modeling the distribution of normal data in pre-trained deep features for anomaly detection[C]//2020 25th International Conference on Pattern Recognition(ICPR).IEEE,2021:6726-6733. [146]GUO X,LIU X,GARDONI P,et al.Machine vision based da-mage detection for conveyor belt safety using Fusion knowledge distillation [J].Alexandria Engineering Journal,2023,71:161-172. [147]YANG Y,YANG R,PAN L,et al.A lightweight deep learning algorithm for inspection of laser welding defects on safety vent of power battery [J].Computers in Industry,2020,123:103306. [148]ZHOU T,ZHANG J,SU H,et al.EDDs:a series of Efficient Defect Detectors for fabric quality inspection [J].Measurement,2021,172:108885. [149]TAN M,PANG R,LE Q V.Efficientdet:Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10781-10790. [150]WANG N T,WANG S Q,HUANG J F,et al.Insulator defect detection method based on improved YOLOv5 neural network [J].Laser Journal,2020,2(14):1-7. [151]ZHANG J,QIAN S,TAN C.Automated bridge surface crackdetection and segmentation using computer vision-based deep learning model [J].Engineering Applications of Artificial Intelligence,2022,115:105225. [152]KE Y,FU Y,ZHOU W Z,et al.Composite Material Multisource Image Instance Segmentation Network Based on Transformer [J].Infrared and Laser Engineering,2023,52(2):245-257. [153]DING Y,QIN H,YAN Q,et al.Towards Accurate Post-Trai-ning Quantization for Vision Transformer [C]//Proceedings of the 30th ACM International Conference on Multimedia.Association for Computing Machinery.2022:5380-5388. [154]CHU X,LI L,ZHANG B.Make repvgg greater again:A quantization-aware approach[C]//Proceedings of the AAAI Confe-rence on Artificial Intelligence.2024:11624-11632. [155]GAO Z L,WANG X,YANG P.Application of improved kNN classification algorithm in Industrial internet of things [J].Information Technology,2021(3):21-25,31. [156]ZHANG Z,XU X,GONG W,et al.Efficient federated convolutional neural network with information fusion for rolling bearing fault diagnosis [J].Control Engineering Practice,2021,116:104913. [157]ZHANG Z,HUANG P,MAO X,et al.Surface defect detectionmethod based on neural architecture search[C]//Proceedings of the 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer(ICFTIC).IEEE,2021:612-617. |
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