[1] Li S B, Yang J, Wang Z et al[2019-07-24]. Review of development and application of defect detection technology Acta Automatica Sinica[2019-07-24].https:∥doi.org/10.16383/j.aas., c180538.

[2] Wang W C. Design of gear defect detection system based on the Halcon[J]. Journal of Mechanical Transmission, 38, 60-63, 98(2014).

[3] Wu Y Q, Xu H Y, Cheng X J. Research on gear defect recognition based on machine vision[J]. Coal Mine Machinery, 40, 170-172(2019).

[4] Wu J Y. Research on lining defect detection system based on machine vision[D]. Hangzhou: Zhejiang University of Technology(2019).

[5] Er-Raoudi M, Diany M, Aissaoui H et al. Gear fault detection using artificial neural networks with discrete wavelet transform and principal component analysis[J]. Journal of Mechanical Engineering and Sciences, 10, 2016-2029(2016).

[6] Zhuo D, Jing J F, Zhang H H et al. Classification of chopped strand mat defects based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 101009(2019).

[7] Yuan L S, Lou M Y, Liu Y Q et al. Palmvein classification based on deep neural network and random forest[J]. Laser & Optoelectronics Progress, 56, 101010(2019).

[8] Zhao H, An W S. Image salient object detection combined with deep learning[J]. Laser & Optoelectronics Progress, 55, 121003(2018).

[9] 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. New York: IEEE, 580-587(2014).

[10] 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).

[11] 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).

[12] Liu W, Anguelov D, Erhan D et al[M]. SSD: single shot multibox detector, 21-37(2016).

[13] 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, 6517-6525(2016).

[14] Peng Y Q, Zhao X S, Tao H F et al. Hand gesture recognition against complex background based on deep learning[J]. Robot, 41, 534-542(2019).

[15] Kim K J, Kim P K, Chung Y S et al. Performance enhancement of YOLOv3 by adding prediction layers with spatial pyramid pooling for vehicle detection. [C]∥2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), November 27-30, 2018, Auckland, New Zealand. New York: IEEE, 1-6(2018).

[16] Chang H T, Gou J N, Li X M. Application of Faster R-CNN in image defect detection of industrial CT[J]. Journal of Image and Graphics, 23, 1061-1071(2018).

[17] Redmon J, Farhadi A[2019-07-24]. YOLOv3: an incremental improvement [2019-07-24].https:∥arxiv., org/abs/1804, 02767.

[18] 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. New York: IEEE, 6517-6525(2017).

[19] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[20] Huang G, Liu Z. Laurens V D M. Densely Connected Convolutional Networks. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).