Abstract
Railway plug defects impact the safety of a railway system. To detect railway plug defects, we establish the framework of a visual inspection system (VIS), which is the first system that can perform railway plug inspection automatically and intelligently. Using the idea of change detection, the framework includes three algorithm modules, which are named the object location, image alignment and similarity measurement modules. After the image acquisition system captures a rail image as the input, the three algorithm modules process the image in order. First, in the object location module, a deep convolutional neural network is used to perform plug location. Second, in the image alignment module, a simple and fast method is designed to align key images using histogram of oriented gradients features. Third, in the similarity measurement module, the χ2 distance is used to compute the similarity between the two plug regions in an inspection image and in an aligned ground-truth image. The results of the similarity measurement are sorted when all inspection images are processed. Therefore, the inspection images with smaller similarity values are ranked higher and the plugs in the images have larger probabilities of defects. The framework has passed the practice tests, and the visual inspection system using this framework has already been authorized by the China Railway Corporation and will be equipped in many inspection trains belonging to local railway corporations.
Highlights
In recent years, high speed railway (HSR) transportation has become more important and the length of the HSR in China is increasing greatly
The ground-truth dataset includes serial images captured by the image acquisition subsystem (IAS) and their locations. The locations of these images are provided by the vehicle positioning system (VPS) that is standard equipment for the test trains used in China and it is not the theme of this paper
The precision-recall curve shows that the object location module in this paper (SRP+pCNN+support vector machine (SVM)) gets better results than those in our previous work [24], as represented by the curves denoted as local binary pattern (LBP)+SVM and Haar+Adaboost, as displayed in figure 13
Summary
High speed railway (HSR) transportation has become more important and the length of the HSR in China is increasing greatly. For HSR maintenance, inspection is an important and necessary preliminary task to look for and confirm defects in the tracks, catenaries, tunnels, subgrades and various equipment in or by railway lines. Unlike track and catenary inspections, plug inspections always depend on human patrolling detection before our VIS is used. To solve those problems with plug inspection, we design a VIS using a change detection framework. We greatly advance the previous work, and detail the whole inspection system for plug defects, including the hardware for image acquisition and the software for the change detection framework that contains three algorithm modules.
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