基于改进Faster RCNN的马克杯缺陷检测方法 下载: 1394次
李东洁, 李若昊. 基于改进Faster RCNN的马克杯缺陷检测方法[J]. 激光与光电子学进展, 2020, 57(4): 041515.
Dongjie Li, Ruohao Li. Mug Defect Detection Method Based on Improved Faster RCNN[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041515.
[1] 刘明周, 马靖, 张淼, 等. 基于机器视觉的机械产品装配系统在线作业方法[J]. 计算机集成制造系统, 2015, 21(9): 2343-2353.
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, 2015, 21(9): 2343-2353.
[2] 周显恩, 王耀南, 朱青, 等. 基于机器视觉的瓶口缺陷检测方法研究[J]. 电子测量与仪器学报, 2016, 30(5): 702-713.
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, 2016, 30(5): 702-713.
[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, 2013, 55: 9-14.
[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, 2014, 24(7/8): 1953-1962.
[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, 2014, 38: 1-13.
[6] LeCun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4): 541-551.
[7] Dai JF, LiY, He KM, 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, 2016: 379- 387.
[8] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
[9] KrizhevskyA, SutskeverI, Hinton GE. 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, 2012: 1097- 1105.
[10] GirshickR, DonahueJ, DarrellT, 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, 2014: 580- 587.
[11] GirshickR. Fast R-CNN[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1440- 1448.
[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, 2017, 39(6): 1137-1149.
[13] SunY, LiangD, Wang XG, et al. ( 2015-02-03)[2019-05-29]. https:∥arxiv.gg363.site/abs/1502. 00873.
[14] Hafemann L G, Sabourin R, Oliveira L S. Learning features for offline handwritten signature verification using deep convolutional neural networks[J]. Pattern Recognition, 2017, 70: 163-176.
[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, 2014, 22(10): 1533-1545.
[16] RedmonJ, DivvalaS, GirshickR, 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, 2016: 779- 788.
[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, 2015, 22(6): 3421-3428.
[18] 姚明海, 陈志浩. 基于深度主动学习的磁片表面缺陷检测[J]. 计算机测量与控制, 2018, 26(9): 29-33.
Yao M H, Chen Z H. Deep active learning in detection of surface defects on magnetic sheet[J]. Computer Measurement & Control, 2018, 26(9): 29-33.
[19] 洪伟, 李朝锋. 基于区域全卷积网络结合残差网络的火焰检测方法[J]. 激光与光电子学进展, 2018, 55(4): 041011.
[20] 韩燮, 赵融, 孙福盛. 基于卷积神经网络的棋子定位和识别方法[J]. 激光与光电子学进展, 2019, 56(8): 081007.
[21] 欧攀, 张正, 路奎, 等. 基于卷积神经网络的遥感图像目标检测[J]. 激光与光电子学进展, 2019, 56(5): 051002.
[22] 刘聪. 基于卷积神经网络的微小零件表面缺陷检测技术研究[D]. 哈尔滨: 哈尔滨理工大学, 2019.
LiuC. Research on surface defects detection of micro parts based on convolution neural network[D]. Harbin: Harbin University of Science and Technology, 2019.
李东洁, 李若昊. 基于改进Faster RCNN的马克杯缺陷检测方法[J]. 激光与光电子学进展, 2020, 57(4): 041515. Dongjie Li, Ruohao Li. Mug Defect Detection Method Based on Improved Faster RCNN[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041515.