Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230700086-8.doi: 10.11896/jsjkx.230700086

• Interdiscipline & Application • Previous Articles     Next Articles

Study and Verification on Few-shot Evaluation Methods for AI-based Quality Inspection in Production Lines

JIAO Ruodan, GAO Donghui, HUANG Yanhua, LIU Shuo, DUAN Xuanfei, WANG Rui, LIU Weidong   

  1. China Mobile Research Institute,Beijing 100053,China
  • Published:2024-06-06
  • About author:JIAO Ruodan,born in 1989,master,intermediate engineer.Her main research interests include artificial intelligence,vertical industry and other fields eva-luation technology.
    GAO Donghui,born in 1980,Ph.D,principal researcher,senior Engineer.His main research interests include the eva-luation of artificial intelligence and information communication technologies,and the development of assessment systems.

PDF (PC)

260

Abstract: With the advent of industry 4.0,the deep integration of manufacturing industry with artificial intelligence(AI) has become an important development trend.Industrial quality inspection has emerged as a significant breakthrough point.However,there is currently a lack of standardized methods for evaluating industrial quality inspection products in the industry.The performance of various quality inspection products is often opaque,making it difficult to optimize and scale up.In response to this situation,this paper proposes an AI-based industrial quality inspection algorithm evaluation method,which is suitable for the application needs of production lines in the industrial field.This method can evaluate AI-based industrial quality inspection products and their competitors in situations where the sample size is small and imbalanced.The evaluation method constructs a data set through cross-validation to avoid the problem of large evaluation result fluctuations caused by small and imbalanced data sets.It also uses gray box testing to avoid the subjectivity in evaluation results caused by a single source of data.Furthermore,it formulates relevant evaluation indicators based on the actual production needs of the production line,which can truly reflect the detection performance of quality inspection products in the production line application scenario.The proposed method is validated through benchmark evaluation of EL testing products for photovoltaic cells,demonstrating its feasibility and its ability to objectively reflect the true performance of various products.Finally,based on the analysis and comparison of the evaluation results,some suggestions are provided for the optimization of AI-based industrial quality inspection products.

Key words: Industrial quality inspection by AI, Deep learning, Object detection, Defect detection, Evaluation methods, Photovoltaic cell EL inspection

CLC Number: 

  • TP391
[1]国家工业信息安全发展研究中心.AI工业质检应用发展白皮书.[R/OL].https://www.zhangqiaokeyan.com/academic-journal-cn_ceocio-china_thesis/02012102861837.html.
[2]LUO D L,CAI Y X,YANG Z H,et al.Survey on industrial defect detection with deep learning[J].Sci SinInform,2022,52:1002-1039.
[3]DENG Y S,LUO A C,DAI M J.Building an automatic defect verification system using deep neural network for PCB defect classification[C]//Proceedings of the 4th International Conference on Frontiers of Signal Processing(ICFSP).2018:145-149.
[4]DEITSCH S,CHRISTLEIN V,BERGER S,et al.Automaticclassification of defective photovoltaic module cells in electrolu-minescence images[J].Sol Energy,2019,185:455-468.
[5]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].J Manuf Processes,2019,45:208-216.
[6]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of Advances in Neural Information Processing Systems.2015:91-99.
[7]HE Y,SONG K,MENG Q,et al.An end-to-end steel surfacedefect detection approach via fusing multiple hierarchical features[J].IEEE Trans Instrum Meas,2020,69:1493-1504.
[8]HUANG G,LIU Z,VAN DER MAATEN L,et al.Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4700-4708.
[9]REDMON J,FARHADI A.Yolov3:an incremental improve-ment[J].arXiv:1804.02767101255,2018.
[10]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition.2016:779-788.
[11]REDMON J,FARHADI A.Yolo9000:better,faster,stronger[C]//CVPR.2017.
[12]LI J,SU Z,GENG J,et al.Real-time detection of steel strip surface defects based on improved YOLO detection network[J].IFAC-Papers OnLine,2018,51:76-81.
[13]CHEN S H,TSAI C C.SMD LED chips defect detection using a YOLOv3-dense model[J].Adv Eng Inf,2021,47:101255.
[14]CHA Y J,CHOI W,SUH G,et al.Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types[J].Comput-Aided Civil Infrastruct Eng,2018,33:731-747.
[15]TAO X,ZHANG D,WANG Z,et al.Detection of power line insulator defects using aerial images analyzed with convo-lutional neural networks[J].IEEE Trans Syst Man Cybern Syst,2020,50:1486-1498.
[16]ZHANG C,CHANG C,JAMSHIDI M.Concrete bridge surface damage detection using a single-stage detector[J].Comput-Aided Civil Infrastruct Eng,2020,35:389-409.
[17]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3431-3440.
[18]RONNEBERGER O,FISCHER P,BROX T.U-Net:convolu-tional networks for biomedical image segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-assisted Intervention.Cham:Springer,2015:234-241.
[19]QIU L,WU X,YU Z.A high-efficiency fully convolutional networks for pixel-wise surface defect detection[J].IEEE Access,2019,7:15884-15893.
[20]HUANG Y,QIU C,YUAN K.Surface defect saliency of magnetic tile[J].Vis Comput,2020,36:85-96.
[21]XIE Y,ZHU F,FU Y.Main-secondary network for defect segmentation of textured surface images[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.2020:3531-3540.
[1] ZHANG Le, YU Ying, GE Hao. Mural Inpainting Based on Fast Fourier Convolution and Feature Pruning Coordinate Attention [J]. Computer Science, 2024, 51(6A): 230400083-9.
[2] WU Yibo, HAO Yingguang, WANG Hongyu. Rice Defect Segmentation Based on Dual-stream Convolutional Neural Networks [J]. Computer Science, 2024, 51(6A): 230600107-8.
[3] HOU Linhao, LIU Fan. Remote Sensing Image Fusion Combining Multi-scale Convolution Blocks and Dense Convolution Blocks [J]. Computer Science, 2024, 51(6A): 230400110-6.
[4] ZHENG Shenhai, GAO Xi, LIU Pengwei, LI Weisheng. Occluded Video Instance Segmentation Method Based on Feature Fusion of Tracking and Detection in Time Sequence [J]. Computer Science, 2024, 51(6A): 230600186-6.
[5] LIU Hongli, WANG Yulin, SHAO Lei, LI Ji. Study on Monocular Vision Vehicle Ranging Based on Lower Edge of Detection Frame [J]. Computer Science, 2024, 51(6A): 231000077-6.
[6] HUANG Yuanhang, BIAN Shan, WANG Chuntao. Gaussian Enhancement Module for Reinforcing High-frequency Details in Camera ModelIdentification [J]. Computer Science, 2024, 51(6A): 230700125-5.
[7] CHEN Yuzhang, WANG Shiqi, ZHOU Wen, ZHOU Wanting. Small Object Detection for Fish Based on SPD-Conv and NAM Attention Module [J]. Computer Science, 2024, 51(6A): 230500176-7.
[8] SUN Yang, DING Jianwei, ZHANG Qi, WEI Huiwen, TIAN Bowen. Study on Super-resolution Image Reconstruction Using Residual Feature Aggregation NetworkBased on Attention Mechanism [J]. Computer Science, 2024, 51(6A): 230600039-6.
[9] QUE Yue, GAN Menghan, LIU Zhiwei. Object Detection with Receptive Field Expansion and Multi-branch Aggregation [J]. Computer Science, 2024, 51(6A): 230600151-6.
[10] SHI Songhao, WANG Xiaodan, YANG Chunxiao, WANG Yifei. SAR Image Target Recognition Based on Cross Domain Few Shot Learning [J]. Computer Science, 2024, 51(6A): 230800136-7.
[11] LI Yuanxin, GUO Zhongfeng, YANG Junlin. Container Lock Hole Recognition Algorithm Based on Lightweight YOLOv5s [J]. Computer Science, 2024, 51(6A): 230900021-6.
[12] DAI Yongdong, JIN Yang, DAI Yufan, FU Jing, WANG Maofei, LIU Xi. Study on Intelligent Defect Recognition Algorithm of Aerial Insulator Image [J]. Computer Science, 2024, 51(6A): 230700172-5.
[13] HUANG Haixin, WU Di. Steel Defect Detection Based on Improved YOLOv7 [J]. Computer Science, 2024, 51(6A): 230800018-5.
[14] WU Chunming, WANG Tiaojun. Study on Defect Detection Algorithm of Transmission Line in Complex Background [J]. Computer Science, 2024, 51(6A): 230500178-6.
[15] LYU Yiming, WANG Jiyang. Iron Ore Image Classification Method Based on Improved Efficientnetv2 [J]. Computer Science, 2024, 51(6A): 230600212-6.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.