Abstract:In order to achieve accurate,rapid and automatic detection of high-speed railway defective fasteners,an adaptive vision detection algorithm for high-speed railway fastener was proposed based on image processing technology. Aiming at the particularity of high-speed railway fastener images,the improved LBP (local binary pattern) operator was used to extract the salient features of fastener. Based on the prominent feature maps of fastener,the template matching algorithm was used to obtain the precise position of fastener region in the original image,and then get the sub-map of fastener and use the position information of fastener to verify the localization result; The difference between two adjacent sub-maps was used as the judgment basis,if the difference was greater than the preset threshold,the corresponding fasteners were judged as defective fasteners. The detection algorithm was applied to the real fastener image provided by the track maintenance division. The results show that the adaptive fastener detection algorithm proposed in this paper performs worst on rainy days,with a correct detection rate of 96% and a false detection rate of 0.50%; It performs best on sunny days,with a correct detection rate of 100% and a false detection rate of 0.22%; It achieves a comprehensive correct detection rate of 99% and a comprehensive false detection rate of 0.33% under different weather,lighting and environment.
肖新标,金学松,温泽峰. 钢轨扣件失效对列车动态脱轨的影响[J]. 交通运输工程学报,2006,6(1): 10-15XIAO Xinbiao, JIN Xuesong, WEN Zefeng. Influence of rail fastener failure on vehicle dynamic derailment[J]. Journal of Traffic and Transportation Engineering, 2006, 6(1): 10-15
[2]
翟婉明,赵春发. 现代轨道交通工程科技前沿与挑战[J]. 西南交通大学学报,2016,51(2): 209-226ZHAI Wanming, ZHAO Chunfa. Frontiers and challenges of sciences and technologies in modern railway engineering[J]. Journal of Southwest Jiaotong University, 2016, 51(2): 209-226
[3]
LI Q, REN S. A real-time visual inspection system for discrete surface defects of rail heads[J]. IEEE Transaction on Instrumentation and Measurement, 2012, 61(8): 2189-2199
[4]
WU X, YUAN P, PENG Q, et al. Detection of bird nests in overhead catenary system images for high-speed rail[J]. Pattern Recognition, 2016, 51: 242-254
[5]
SUN J, XIAO Z, XIE Y. Automatic multi-fault recognition in TFDS based on convolutional neural network[J]. Neurocomputing, 2017, 222: 127-136
[6]
QIAO Y, CAPPELLE C, RUICHEK Y. Visual localization across seasons using sequence matching based on multi-feature combination[J]. Sensors, 2017, 17: 24-42
[7]
SINGH M,SINGH S,JAISWAL J,et al. Autonomous rail track inspection using vision based system[C]//Proceedings of IEEE Int. Conf. Comput. Intell. Homeland Security Personal Safety. Piscataway:IEEE,2006: 56-59.
[8]
MARINO F, DISTANTE A, MAZZEO P, et al. A real-time visual inspection system for railway maintenance:automatic hexagonal-headed bolts detection[J]. IEEE Transactions on Systems,Man and Cybernetics,Part C (Applications and Reviews), 2007, 37(3): 418-428
[9]
STELLA E,MAZZEO P,NITTI M,et al. Visual recognition of missing fastening elements for railroad maintenance[C]// Proceedings of IEEE Int. Conf. Intell. Transp. Syst. Piscataway: IEEE,2002: 94-99.
[10]
YANG J,TAO W,LIU M,et al. An efficient direction field-based method for the detection of fasteners on high-speed railways[J]. Sensors,2011,11:7364-7381
[11]
XIA Y,XIE F,JIANG Z. Broken railway fastener detection based on adaboost algorithm[C]//Proceedings of International Conference on Optoelectronics and Image Processing. Washington D. C.:IEEE,2010:313-316.
[12]
刘甲甲,李柏林,罗建桥,等. 融合PHOG和MSLBP特征的铁路扣件检测算法[J]. 西南交通大学学报,2015,50(2): 256-263LIU Jiajia, LI Bailin, LUO Jianqiao, et al. Railway fastener detection algorithm integrating PHOG and MSLBP features[J]. Journal of Southwest Jiaotong University, 2015, 50(2): 256-263
[13]
LI Y,OTTO C,HAAS N,et al. Component-based track inspection using machine-vision technology[C]// Proceedings of the 1st ACM International Conference on Multimedia Retrieval. New York: ACM,2011: 60-61.
[14]
FENG H, JIANG Z, XIE F, et al. Automatic fastener classification and defect detection in vision-based railway inspection systems[J]. IEEE Trans. Instrumentation and Measurement, 2014, 63(4): 877-888
[15]
OJALA T, PIETIKAINEN M, MAENPAA T. Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987