A survey of the application of machine vision in rail transit system inspection

被引：0
|
作者：
Wei X.-K. [1 ]
Suo D. [1 ]
Wei D.-H. [1 ]
Wu X.-M. [1 ]
Jiang S.-Y. [1 ]
Yang Z.-M. [1 ]
机构：
[1] State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing
来源：
Kongzhi yu Juece/Control and Decision | 2021年 / 36卷 / 02期
关键词：
Intelligence; Machine vision; Operation assurance; Safety state inspection; Urban rail transit;
D O I：
10.13195/j.kzyjc.2020.1199
中图分类号：
学科分类号：
摘要：
Urban rail transit is mainly composed of pantograph-catenary systems, track lines, vehicles, stations, etc. traditional methods such as manual inspections have poor detection efficiency, high labor intensity, and low automation and intelligence, which have brought huge challenges to the operation assurance and further healthy development of urban rail transit. At present, due to the in-depth development of vision technology, as an important detection method, machine vision has been widely used in the field of urban rail transit system state detection. In view of this, the researches and applications of machine vision in the safety state inspection of urban rail transit systems are reviewed. Firstly, the basic concept of urban rail transit and the challenges and opportunities faced by rapid development are briefly introduced. Then, the researches and applications of the machine vision technology in the safety state detection of urban rail transit subsystems are discussed in detail: 1) aiming at the problem of pantograph-catenary system state detection, the domestic and foreign research status of machine vision in pantograph wear detection, pantograph envelope line and other defects detection, catenary geometric parameter detection, catenary wear detection and catenary suspension defect detection are introduced, respectively. 2) As for track line safety state inspection, the application and research status of machine vision in fastener safety state detection and rail surface disease detection are introduced. 3) The application and research progress of machine vision in vehicle state detection are introduced in detail from the perspective of different detection items. 4) The specific application and research of machine vision in abnormal behavior detection of station escalator safety monitoring and platform safety monitoring are sorted out and summarized. 5) The specific application and background technology of machine vision in rail transit driver behavior monitoring are presented. Finally, we look forward to the future of the application of machine vision technology in the field of urban rail transit system state inspection. Copyright ©2021 Control and Decision.
引用
收藏
页码：257 / 282
页数：25
相关论文
共 144 条
  • [71] Ruvo P D, Distante A, Stella E, Et al., A GPU-based vision system for real time detection of fastening elements in railway inspection, IEEE International Conference on Image Processing, pp. 2309-2312, (2009)
  • [72] Resendiz E, Hart J, Ahuja N., Automated visual inspection of railroad tracks, IEEE Transactions on Intelligent Transportation Systems, 14, 2, pp. 751-760, (2013)
  • [73] Yang J F, Tao W, Liu M H, Et al., An efficient direction field-based method for the detection of fasteners on high-speed railways, Sensors, 11, 8, pp. 7364-7381, (2011)
  • [74] Blei D M, Ng A Y, Jordan M I, Et al., Latent dirichlet allocation, Journal of Machine Learning Research, 3, pp. 993-1022, (2012)
  • [75] Xia Y Q, Xie F Y, Jiang Z G., Broken railway fastener detection based on adaboost algorithm, International Conference on Optoelectronics and Image Processing, pp. 313-316, (2011)
  • [76] Liu J J, Xiong Y, Li B L, Et al., Research on antomatic inspection algorithm for railway fastener defects based on computer vision, Journal of the China Railway Society, 38, 8, pp. 73-80, (2016)
  • [77] Li Y, Trinh H, Haas N, Et al., Rail component detection, optimization, and assessment for automatic rail track inspection, IEEE Transactions on Intelligent Transportation Systems, 15, 2, pp. 760-770, (2014)
  • [78] Li Y, Otto C, Haas N, Et al., Component-based track inspection using machine-vision technology, International Conference on Multimedia Retrieval, pp. 1-8, (2011)
  • [79] Feng H, Jiang Z G, Xie F Y, Et al., Automatic fastener classification and defect detection in vision-based railway inspection systems, IEEE Transactions on Instrumentation and Measurement, 63, 4, pp. 877-888, (2014)
  • [80] Wang J, Li J, Lee C Y, Et al., Dense SIFT and gabor descriptors-based face representation with applications to gender recognition, International Conference on Control, Automation, Robotics and Vision, pp. 1860-1864, (2010)
3456789101112