Abstract
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 article, 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.
Highlights
T HE railway system plays a key role in modern intercity transport in terms of its capacity and efficiency
To verify the effectiveness of the proposed track curvature and condition detection system, three sets of videos taken from the driving recorders of trains of the same type traveling on a rail route during different time periods are first analyzed, and the results are compared with those obtained by track geometry inspection vehicle
It is seen that the track curvature obtained by the proposed technique using the videos taken on 20 November 2018 highly agrees with that obtained by the track geometry inspection vehicle (Fig. 10 (a)), verifying the effectiveness of the proposed image processing method in evaluating the track geometry
Summary
T HE railway system plays a key role in modern intercity transport in terms of its capacity and efficiency. A variety of online and/or onboard sensing techniques using sensors such as strain gauges [8], fiber optic sensors [9]–[11], bogie-mounted accelerometers [12], car-body sensory devices [13], and acoustic emission [14]–[16], have been increasingly used to inspect rail geometry and/or track condition. These techniques sometimes fail to satisfy performance requirements including stability, accuracy and durability under specific conditions [17].
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