TY - JOUR
T1 - A machine vision system based on driving recorder for automatic inspection of rail curvature
AU - Wang, Su Mei
AU - Liao, Ching Lung
AU - Ni, Yi Qing
N1 - Funding Information:
The work described in this paper was supported by a grant (RIF) from the Research Grants Council of the Hong Kong Special Administrative Region (SAR), China (Grant No. R-5020-18) and a grant from the Innovation and Technology Commission of the Hong Kong SAR Government to Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center (Grant No. K-BBY1). (Corresponding author: Yi-Qing Ni) S. M. Wang is with Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center; and Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (e-mail: [email protected]).
Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Because of long distance of railway lines, it is difficult to find an appropriate method to inspect the rail track condition efficiently and accurately. In this paper, a machine vision system based on driving recorder and image signal processing is proposed to evaluate the rail curvature automatically. The proposed machine vision system consists of four modules including the video acquisition module, the image extraction module, the image processing module, and the track condition assessment module. Three classic edge detection methods are adopted and compared for rail edge detection. In line with the videos of driving recorder, coordinate systems for train and rail are defined in the Lagrangian space, and the track curvature is estimated using the proposed chord offset method and double measurement method. For evaluating the track condition, an index describing the concordance between the train and track is defined. In the case study, a set of videos from the driving recorders of trains during their in-service operations are analyzed by the proposed technique, and the obtained results are verified by comparison with those obtained by a track geometry inspection vehicle. It is shown that the proposed technique can evaluate the track curvature accurately. Moreover, the influence of the position of deployed driving recorder, the focal length and anti-shake of camera on the accuracy of evaluation results is discussed. It is testified that the proposed technique provides a simple and reliable way to inspect the track curvature.
AB - Because of long distance of railway lines, it is difficult to find an appropriate method to inspect the rail track condition efficiently and accurately. In this paper, a machine vision system based on driving recorder and image signal processing is proposed to evaluate the rail curvature automatically. The proposed machine vision system consists of four modules including the video acquisition module, the image extraction module, the image processing module, and the track condition assessment module. Three classic edge detection methods are adopted and compared for rail edge detection. In line with the videos of driving recorder, coordinate systems for train and rail are defined in the Lagrangian space, and the track curvature is estimated using the proposed chord offset method and double measurement method. For evaluating the track condition, an index describing the concordance between the train and track is defined. In the case study, a set of videos from the driving recorders of trains during their in-service operations are analyzed by the proposed technique, and the obtained results are verified by comparison with those obtained by a track geometry inspection vehicle. It is shown that the proposed technique can evaluate the track curvature accurately. Moreover, the influence of the position of deployed driving recorder, the focal length and anti-shake of camera on the accuracy of evaluation results is discussed. It is testified that the proposed technique provides a simple and reliable way to inspect the track curvature.
KW - Driving recorder
KW - Edge detection
KW - Machine vision
KW - Onboard inspection
KW - Track curvature
UR - http://www.scopus.com/inward/record.url?scp=85095983043&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2020.3020907
DO - 10.1109/JSEN.2020.3020907
M3 - Journal article
AN - SCOPUS:85095983043
SN - 1530-437X
VL - 21
SP - 11291
EP - 11300
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 10
M1 - 9184130
ER -