[1] Zhou Y Y, Yu F X, Liu S M et al. Discussion between the quality of optical fiber end face and optical property[J]. Optical Technique, 31, 806-808(2005).

[2] Wang H X, Shen L, Li C F et al. Analysis and experimental investigation of laser induced damage of optics[J]. Chinese Journal of Lasers, 44, 0302006(2017).

[3] Niu W X, Liang Y J, Jiang Y et al. Experimental study of the method for inspecting optic fiber end[J]. Applied Science and Technology, 29, 19, 20-23(2002).

[4] Xie Y M. Research on optical fiber connector face inspection[D], 30-39(2016).

[5] Pei Y. Study of optimization design based on laser interferometer for optical fiber end face[D](2014).

[6] Mei S, Wang Y D, Wen G J et al. Automated inspection of defects in optical fiber connector end face using novel morphology approaches[J]. Sensors, 18, 1408(2018).

[7] Zhao W J, Gao Y. Application of machine vision in defects inspection of optical fiber end surface[J]. Modern Electronics Technique, 34, 136-139, 143(2011).

[8] Zhu L, Xu S Y. Research on an OpenCV-based optical fiber end detection system[J]. Study on Optical Communications, 28-30, 38(2015).

[9] Liu W, Tang C H, Ma X M et al. Measurement of geometric parameters of defective fiber ends[J]. Study on Optical Communications, 35-38(2013).

[10] Zhou C. Automatic detection system of optical fiber end surface defects based on machine vision[D], 120-145(2019).

[11] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C], 580-587(2014).

[12] Girshick R. Fast R-CNN[C], 1440-1448(2015).

[13] 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).

[14] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[15] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[16] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[17] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934

[18] 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, 9905, 21-37(2016).

[19] Zhang S F, Wen L Y, Bian X et al. Single-shot refinement neural network for object detection[C], 4203-4212(2018).

[20] Wang D F, Zhang B, Cao Y et al. SFSSD: shallow feature fusion single shot multibox detector[M]. Leibe B, Matas J, Sebe N, et al. Communications, 9905, 21-37(2020).

[21] Zhang X Y, Zhou X Y, Lin M X et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C], 6848-6856(2018).

[22] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[EB/OL]. https://arxiv.org/abs/1807.06521

[23] Bai L L, Han Z N, Li Y F et al. A hybrid de-noising algorithm for the gear transmission system based on CEEMDAN-PE-TFPF[J]. Entropy, 20, 361(2018).

[24] Bai L L, Han Z N, Ren J J et al. Research on feature selection for rotating machinery based on supervision kernel entropy component analysis with whale optimization algorithm[J]. Applied Soft Computing, 92, 106245(2020).