[1] Zhang Y J, Qi X L, Dong C J. Surface detection of gun steel core based on image gray value morphology under machine vision[J]. Journal of Sichuan Ordnance, 33, 59-61(2012).
[2] Shi J W, Guo C Y, Liu H N[J]. Study on detection system of bullet surface defect based on machine visionModular Machine Tool & Automatic Manufacturing Technique, 2013, 59-64.
[3] Shi J W, Guo C Y, Liu H N et al. Bullet surface defect extraction based on Hough transformation and two-peak algorithm[J]. Fire Control & Command Control, 38, 129-132(2013).
[4] Wang P, Guo C Y, Liu H N. Research on automatic online detection system of bullet surface defect[J]. Journal of Ordnance Engineering College, 27, 50-53(2015).
[5] Wang P, Guo C Y, Liu H N. Bullet surface defect recognition and classification based on support vector machine[J]. Computer Engineering & Science, 38, 1943-1949(2016).
[6] Wang L, Zhang H H. Application of faster R-CNN model in vehicle detection[J]. Journal of Computer Applications, 38, 666-670(2018).
[7] Zhang H Y, Wang S N, Hu W B. Improved method for estimating number of people based on convolution neural network[J]. Laser & Optoelectronics Progress, 55, 121503(2018).
[8] Tian Q, Yuan T Y, Yang D et al. A pedestrian detection method based on dark channel defogging and deep learning[J]. Laser & Optoelectronics Progress, 55, 111007(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). http://www.tandfonline.com/servlet/linkout?suffix=CIT0014&dbid=8&doi=10.1080%2F2150704X.2018.1475770&key=27295650
[12] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[M]. ∥ Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8689, 818-833(2014).
[13] Simonyan K. -04-10)[2018-12-05]. https:∥arxiv., org/abs/1409, 1556(2015).
[14] Li J N, Zhang B H. Face recognition by feature matching fusion combined with improved convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 101504(2018).
[15] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017). http://www.tandfonline.com/servlet/linkout?suffix=CIT0044&dbid=16&doi=10.1080%2F15481603.2018.1426091&key=10.1109%2FCVPR.2015.7298965
[16] Everingham M. Eslami S M A, van Gool L, et al. The Pascal visual object classes challenge: a retrospective[J]. International Journal of Computer Vision, 111, 98-136(2015).
[17] Jia Y Q, Shelhamer E, Donahue J et al. Caffe. [C]∥Proceedings of the ACM International Conference on Multimedia, November 3-7, 2014, Orlando, Florida, USA. New York: ACM, 675-678(2014).
[18] Feng X Y, Mei W, Hu D S. Aerial target detection based on improved faster R-CNN[J]. Acta Optica Sinica, 38, 0615004(2018).