[1] 贺振东, 王耀南, 毛建旭, 印峰. 基于反向P-M扩散的钢轨表面缺陷视觉检测. 自动化学报, 2014, 40(8): 1667−1679

1 He Zhen-Dong, Wang Yao-Nan, Mao Jian-Xu, Yin Feng. Research on inverse P-M diffusion-based rail surface defect detection. Acta Automatica Sinica, 2014, 40(8): 1667−1679
[2] 2 He Z D, Wang Y N, Yin F, Liu J. Surface defect detection for high-speed rails using an inverse PM diffusion model. Sensor Review, 2016, 36(1): 86−97 doi: 10.1108/SR-03-2015-0039
[3] 3 Resendiz E, Hart J M, Ahuja N. Automated visual inspection of railroad tracks. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2): 751−760 doi: 10.1109/TITS.2012.2236555
[4] 孙次锁, 张玉华. 基于智能识别与周期检测的钢轨伤损自动预警方法研究. 铁道学报, 2018, 40(11): 140−146 doi: 10.3969/j.issn.1001-8360.2018.11.020

4 Sun Ci-Suo, Zhang Yu-Hua. Research on automatic early warning method for rail flaw based on intelligent identification and periodic detection. Journal of the China Railway Society, 2018, 40(11): 140−146 doi: 10.3969/j.issn.1001-8360.2018.11.020
[5] 5 Liang B, Iwnicki S, Ball A, Young A E. Adaptive noise cancelling and time-frequency techniques for rail surface defect detection. Mechanical Systems and Signal Processing, 2015, 54−55: 41−51
[6] 6 Gibert X, Patel V M, Chellappa R. Deep multitask learning for railway track inspection. IEEE Transactions on Intelligent transportation systems, 2017, 18(1): 153−164 doi: 10.1109/TITS.2016.2568758
[7] Giben X, Patel V M, Chellappa R. Material classification and semantic segmentation of railway track images with deep convolutional neural networks. In: Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Québec, Canada: IEEE, 2015: 621−625
[8] Faghih-Roohi S, Hajizadeh S, Núñez A, Babuska R. Deep convolutional neural networks for detection of rail surface defects. In: Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, 2016: 2584−2589
[9] Masci J, Meier U, Ciresan D, et al. Steel defect classification with max-pooling convolutional neural networks. In: Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, 2012: 1−6
[10] 10 Chen J W, Liu Z Y, Wang H R, Núñez A, Han Z W. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Transactions on Instrumentation and Measurement, 2018, 67(2): 257−269 doi: 10.1109/TIM.2017.2775345
[11] 11 Liu Z G, Wang L Y, Li C J, Yang G J, Han Z W. A high-precision loose strands diagnosis approach for isoelectric line in high-speed railway. IEEE Transactions on Industrial Informatics, 2018, 14(3): 1067−1077 doi: 10.1109/TII.2017.2774242
[12] 12 Zhong J P, Liu Z T, Han Z W, Han Y, Zhang W X. A CNN-based defect inspection method for catenary split pins in high-speed railway. IEEE Transactions on Instrumentation and Measurement, 2018
[13] 袁静, 章毓晋. 融合梯度差信息的稀疏去噪自编码网络在异常行为检测中的应用. 自动化学报, 2017, 43(4): 604−610

13 Yuan Jing, Zhang Yu-Jin. Application of sparse denoising auto encoder network with gradient difference information for abnormal action detection. Acta Automatica Sinica, 2017, 43(4): 604−610
[14] 唐贤伦, 杜一铭, 刘雨微, 李佳歆, 马艺玮. 基于条件深度卷积生成对抗网络的图像识别方法. 自动化学报, 2018, 44(5): 855−864

14 Tang Xian-Lun, Du Yi-Ming, Liu Yu-Wei, Li Jia-Xin, Ma Yi-Wei. Image recognition with conditional deep convolutional generative adversarial networks. Acta Automatica Sinica, 2018, 44(5): 855−864
[15] 辛宇, 杨静, 谢志强. 基于标签传播的语义重叠社区发现算法. 自动化学报, 2014, 40(10): 2262−2275

15 Xin Yu, Yang Jing, Xie Zhi-Qiang. An overlapping semantic community structure detecting algorithm by label propagation. Acta Automatica Sinica, 2014, 40(10): 2262−2275
[16] 16 Denker J S, Lecun Y. Transforming neural-net output levels to probability distributions. Advances in Neural Information Processing Systems, 1991: 853−859
[17] 17 MacKay D J C. A practical Bayesian framework for backpropagation networks. Neural Computation, 1992, 4(3): 448−472 doi: 10.1162/neco.1992.4.3.448
[18] 18 Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 2014, 15(1): 1929−1958
[19] Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the 2016 International Conference on Machine Learning, 2016: 1050−1059
[20] 郑文博, 王坤峰, 王飞跃. 基于贝叶斯生成对抗网络的背景消减算法. 自动化学报, 2018, 44(5): 878−890

20 Zheng Wen-Bo, Wang Kun-Feng, Wang Fei-Yue. Background subtraction algorithm with Bayesian generative adversarial networks. Acta Automatica Sinica, 2018, 44(5): 878−890
[21] Fu J, Zheng H, Mei T. Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4438−4446
[22] Wang F, Jiang M Q, Qian C, Yang S, Li C, Zhang H G, et al. Residual attention network for image classification. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3156−3164
[23] He K M, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In: Proceedings of the 2017 IEEE International Conference on Computer Vision, 2017: 2961−2969
[24] 24 Lin H, Shi Z, Zou Z. Fully convolutional network with task partitioning for inshore ship detection in optical remote sensing images. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1665−1669 doi: 10.1109/LGRS.2017.2727515
[25] Chen L C, Zhu Y K, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the 2018 European Conference on Computer Vision (ECCV), 2018: 801−818
[26] 韩江洪, 乔晓敏, 卫星, 陆阳. 基于空间卷积神经网络的井下轨道检测方法. 电子测量与仪器学报, 2018, 32(12): 34−43

26 Han Jiang-Hong, Qiao Xiao-Min, Wei Xing, Lu Yang. Downhole track detection method based on spatial convolutional neural network. Journal of Electronic Measurement and Instrumentation, 2018, 32(12): 34−43
[27] 时增林, 叶阳东, 吴云鹏, 娄铮铮. 基于序的空间金字塔池化网络的人群计数方法. 自动化学报, 2016, 42(6): 866−874

27 Shi Zeng-Lin, Ye Yang-Dong, Wu Yun-Peng, Lou Zheng-Zheng. Crowd counting using rank-based spatial pyramid pooling network. Acta Automatica Sinica, 2016, 42(6): 866−874
[28] Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. Semantic image segmentation with deep convolutional nets and fully connected CRFs, arXiv preprint arXiv: 1412. 7062, 2014
[29] 张辉, 金侠挺, Wu Q M Jonathan, 贺振东, 王耀南. 基于曲率滤波和改进GMM的钢轨缺陷自动视觉检测方法. 仪器仪表学报, 2018, 39(4): 181−194

29 Zhang Hui, Jin Xia-Ting, Wu Q. M. Jonathan, He Zhen-Dong, Wang Yao-Nan. Automatic visual detection method of railway surface defects based on curvature filtering and Improved GMM. Chinese Journal of Scientific Instrument, 2018, 39(4): 181−194
[30] 骆小飞, 徐军, 陈佳梅. 基于逐像素点深度卷积网络分割模型的上皮和间质组织分割. 自动化学报, 2017, 43(11): 2003−2013

30 Luo Xiao-Fei, Xu Jun, Chen Jia-Mei. A deep convolutional network for pixel-wise segmentation on epithelial and stromal tissues in histologic images. Acta Automatica Sinica, 2017, 43(11): 2003−2013
[31] Chollet F. Xception: deep learning with depthwise separable convolutions. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1251−1258
[32] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431−3440
[33] Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779−788
[34] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Proceedings of the 2015 International Conference on Medical image Computing and Computer-assisted Intervention, Springer, Cham, 2015: 234−241
[35] 35 Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481−2495 doi: 10.1109/TPAMI.2016.2644615
[36] 36 Zhao H S, Shi J P, Qi X J, Wang X G, Jia J Y. Pyramid scene parsing network. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2881−2890