A vision-based method for the detection of missing rail fasteners

Abstract

Visual inspection of rail fasteners is crucial to rail safety. However, the traditional method in which railway staffs manually inspect the conditions of fasteners is time-consuming and prone to human error. In this paper, we present a method to automatically detect missing rail fasteners from top-view images. Using a top-down approach, coarse bounding boxes of potential fastener areas are first located from the track and the tie regions with an edge density map and the RANSAC algorithm. Preprocessed with the guided filter, the region within the bounding boxes are then scanned to detect rail fasteners using PHOG features and e-SVR with RBF kernel. The boxes, in which no fasteners are found, are reported as missing fasteners. The proposed method was tested and has shown a degree of robustness in scenes from complex real-world environments with the 100% probability of detection and 3.47% probability of false alarm for missing fastener detection. The results also indicate that the use of guided filter, RBF kernel and the image pyramid technique for feature extraction significantly improves the performance of the classifier.