[1] Liu M Z, Ma J, Zhang M et al. Online operation method for assembly system of mechanical products based on machine vision[J]. Computer Integrated Manufacturing Systems, 21, 2343-2353(2015).
[2] Zhou X E, Wang Y N, Zhu Q et al. Research on defect detection method for bottle mouth based on machine vision[J]. Journal of Electronic Measurement and Instrumentation, 30, 702-713(2016).
[3] Wang P, Zhu L, Zhu Q J et al. An application of back propagation neural network for the steel stress detection based on Barkhausen noise theory[J]. NDT & E International, 55, 9-14(2013).
[4] Xie L J, Huang R, Gu N et al. A novel defect detection and identification method in optical inspection[J]. Neural Computing and Applications, 24, 1953-1962(2014).
[5] Halfawy M R, Hengmeechai J. Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine[J]. Automation in Construction, 38, 1-13(2014).
[6] LeCun Y, Boser B, Denker J S et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1, 541-551(1989).
[7] Dai J F, Li Y, He K M et al. R-FCN: object detection via region-based fully convolutional networks. [C]∥Proceedings of the 30th International Conference on Neural Information Processing Systems, December 5-10, 2016, Barcelona, Spain. San Diego: NIPS, 379-387(2016).
[8] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 18, 1527-1554(2006).
[9] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. [C]∥Advances in Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada, United States. San Diego: NIPS, 1097-1105(2012).
[10] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE, 580-587(2014).
[11] Girshick R. Fast R-CNN. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1440-1448(2015).
[12] 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).
[13] Sun Y, Liang D, Wang X G et al. -02-03)[2019-05-29]. https:∥arxiv.gg363., site/abs/1502, 00873(2015).
[14] Hafemann L G, Sabourin R, Oliveira L S. Learning features for offline handwritten signature verification using deep convolutional neural networks[J]. Pattern Recognition, 70, 163-176(2017).
[15] Abdel-Hamid O, Mohamed A R, Jiang H et al. Convolutional neural networks for speech recognition[J]. ACM Transactions on Audio, Speech, and Language Processing, 22, 1533-1545(2014).
[16] 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. New York: IEEE, 779-788(2016).
[17] Zhao Z B, Liu N, Wang L. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 22, 3421-3428(2015).
[18] Yao M H, Chen Z H. Deep active learning in detection of surface defects on magnetic sheet[J]. Computer Measurement & Control, 26, 29-33(2018).
[22] Liu C. Research on surface defects detection of micro parts based on convolution neural network[D]. Harbin: Harbin University of Science and Technology(2019).