Laser & Optoelectronics Progress, Volume. 56, Issue 15, 151202(2019)
Bullet Appearance Defect Detection Based on Improved Faster Region-Convolutional Neural Network
[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.
[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).
[12] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[M]. ∥ Fleet D, Pajdla T, Schiele B,
[13] Simonyan K. -04-10)[2018-12-05]. https:∥arxiv., org/abs/1409, 1556(2015).
[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).
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Xiaoyun Ma, Dan Zhu, Chen Jin, Xinxin Tong. Bullet Appearance Defect Detection Based on Improved Faster Region-Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(15): 151202
Category: Instrumentation, Measurement and Metrology
Received: Dec. 21, 2018
Accepted: Mar. 5, 2019
Published Online: Aug. 5, 2019
The Author Email: Xiaoyun Ma (maxiaoyun@sia.cn)