[1] Wang D Y, Wang X K, Yu W W et al. Off-axis LED curved array lighting design for leather defect detection[J]. Laser & Optoelectronics Progress, 56, 082202(2019).

[2] Li D J, Li R H. Mugdefect detection method based on improved Faster RCNN[J]. Laser & Optoelectronics Progress, 57, 041515(2020).

[3] Zhang G S, Ge G Y, Zhu R H et al. Geardefect detection based on the improved YOLOv3 network[J]. Laser & Optoelectronics Progress, 57, 121009(2020).

[4] Liu J C, Shi T L, Wang K et al. Defect detection of flip-chip solder joints using modal analysis[J]. Microelectronics Reliability, 52, 3002-3010(2012).

[5] Chen C S, Huang C L, Yeh C W. A hybrid defect detection for in-tray semiconductor chip[J]. The International Journal of Advanced Manufacturing Technology, 65, 43-56(2013). http://link.springer.com/article/10.1007/s00170-012-4149-5

[6] Liao G L, Du L, Su L et al. Using RBF networks for detection and prediction of flip chip with missing bumps[J]. Microelectronics Reliability, 55, 2817-2825(2015).

[7] Xu Z S, Shi T L, Lu X N et al. Using active thermography for defects inspection of flip chip[J]. Microelectronics Reliability, 54, 808-815(2014).

[8] Xu Z S, Shi T L, Lu X N et al. Failures detection of flip-chip using active thermography method based on wavelet transform[J]. Infrared and Laser Engineering, 43, 3233-3237(2014).

[9] Feng L, Gong Z H. An algorithm for chip surface defect detection based on sequential similarity and light source automatic adjustment[J]. Modern Electronics Technique, 40, 58-62(2017).

[10] Liu C, Yuan X F, Tian Z M et al. Research on chip defect detection algorithm based on point pattern matching[J]. Laser Journal, 41, 39-44(2020).

[11] Li Y T, Xie Q S, Huang H S et al. Surface defect detection based on fast regions with convolutional neutral network[J]. Computer Integrated Manufacturing Systems, 25, 1897-1907(2019).

[12] Wu T, Wang W B, Yu L et al. Insulator defect detection method for lightweight YOLOv3[J]. Computer Engineering, 45, 275-280(2019).

[13] Zhong J J, He D Q, Miao J et al. Weld defect detection of metro vehicle based on improved faster R-CNN[J]. Journal of Railway Science and Engineering, 17, 996-1003(2020).

[14] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 580-587(2014).

[15] Girshick R. Fast R-CNN[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 1440-1448(2015).

[16] Ren S Q, He K M, Girshick R et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[17] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

[18] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, 779-788(2016).

[19] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6517-6525(2017).

[20] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2020-08-20]. https://arxiv.org/abs/1804.02767