[1] LIU R Y. Principle and application of trouble of moving freight car detection system (TFDS)［J］. Chinese Railways, 2005, (5): 26-37. (in Chinese)

[2] ZHENG C, WEI Z Z. Automatic online vision-based inspection system of coupler yoke for freight trains［J］. Journal of Electronic Imaging, 2016, 25(6): 061602.

[3] ZHAO D X, WANG B, ZHOU W T, et al.. Image matching algorithm of release valve bonnet based on contours reconstructed by oriented line segment and pairwise geometrical histogram［J］. China Mechanical Engineering, 2017, 28(6): 721-727. (in Chinese)

[4] SUN G D, TANG H B, LIN K, et al.. Automatic fault recognition algorithm for key parts of train based on multi-feature fusion and BP-AdaBoost［J］. China Mechanical Engineering, 2017, 28(21): 2588-2594. (in Chinese)

[5] JIANG Y, ZHOU F Q, ZHANG G J. An automatic recognition method for trouble of sleeper springs of freight cars［J］. Optical Technique, 2007, 33(5): 662-665. (in Chinese)

[6] LIU L, ZHOU F, HE Y. Automated status inspection of fastening bolts on freight trains using a machine vision approach［J］. Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail & Rapid Transit, 2016, 230(7): 1159-66.

[7] LIU L, ZHOU F Q, HE Y Z. Automated visual inspection system for bogie block key under complex freight train environment［J］. IEEE Transactions on Instrumentation and Measurement, 2016, 65(1): 2-14.

[8] LI N, WEI Z, CAO Z. Automatic fault recognition for Brake-Shoe-Key losing of freight train［J］. Optik - International Journal for Light and Electron Optics, 2015, 126(23): 4735-4742.

[9] SUN G D, FENG W, ZHAO D X, et al.. An automatic fault recognition method for side frame key in TFDS［J］. The Open Mechanical Engineering Journal, 2015, 9(1): 22-27.

[10] LIU L, ZHOU F, HE Y. Vision-based fault inspection of small mechanical components for train safety［J］. Iet Intelligent Transport Systems, 2016, 10(2): 130-139.

[11] SUN J H, XIAO Z W, XIE Y X. Automatic multi-fault recognition in TFDS based on convolutional neural network［J］. Neurocomputing. 2017, 222: 127-136.

[12] LI C, WEI Z, XING J. Online inspection system for the automatic detection of bolt defects on a freight train［J］. Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail & Rapid Transit, 2016, 230(4): 271-288.

[13] ZOU R, LI J K, XU J X, et al.. Deflection fault detection for locking plate of freight trains under complex scene［J］. Journal of Railway Science and Engineering, 2015, (4): 917-922. (in Chinese)

[14] ZHOU F, ZOU R, QIU Y, et al.. Automated visual inspection of angle cocks during train operation［J］. Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail & Rapid Transit, 2013, 228(7): 794-806.

[15] LU S, NAN G, ZHANG G. Automated visual inspection of brake shoe wear［C］. Applied Optics and Photonics China, 2015: 96752F.

[16] DAVID G. Distinctive image features from scale-invariant keypoints［J］. International Journal of Computer Vision. 2004, 60(2): 91-110.

[17] QU Y F, LIU Z Y, JIANG Y Q, et al.. Self-adaptative variable-metric feature point extraction method［J］. Opt. Precision Eng., 2017, 25(1): 188-197. (in Chinese)

[18] LAZEBNIK S, SCHMID C, PONCE J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories［J］. IEEE Conference Society Conference on Computer Vision & Pattern Recognition, 2006, 2(1/2): 2169-2178.

[19] DONG B. A new scene classification method based on spatial pyramid matching model［J］. Journal of Information & Computational Science. 2015, 12(3): 1073-1080.

[20] HAN Y L, ZHAO Y P, WANG Q S, et al.. Reconstruction of super resolution for noise image under the sparse representation［J］.Opt. Precision Eng., 2017, 25(6) : 1619-1626. (in Chinese)

[21] WANG X H, YANG J, AI D N, et al.. Liver segmentation in CT image based on priori sparse dictionary and hole filling［J］. Opt. Precision Eng., 2015, 23(9): 2687-2697. (in Chinese)

[22] ZHANG K, ZHANG L, YANG M H. Fast compressive tracking［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 36(10): 2002-2015.

[23] YANG J, ZHENG N, LIU D, et al.. Optimization of features with weight and model parameters of SVM based on genetic algorithm［J］. Computer Simulation. 2008, 25(9): 115-117. (in Chinese)

[24] ZHANG Y. Hierarchical feature matching of the fault image based improved Markov random field and exact height function in TFDS［D］. Wuhan: Hubei University of Technology, 2017. (in Chinese)

[25] ZHAO Z, FENG G C. Efficient algorithm for sparse coding and dictionary learning with applications to face recognition［J］. Journal of Electronic Imaging, 2015, 24(2): 023009.

[26] SUN G D, ZHANG Y, TANG H B, et al.. Railway equipment detection using exact height function shape descriptor based on fast adaptive Markov random field［J］. Optical Engineering, 2018, 57(5): 1.