A New Method for Railway Fastener Detection Using the Symmetrical Image and Its EA-HOG Feature

Abstract

Railway fastener recognition and detection is an important task for railway operation safety. However, the current automatic inspection methods based on computer vision can effectively detect the intact or completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In our method, we exploit the EA-HOG feature fastener image, generate two symmetrical images of original test image and turn the detection of the original test image into the detection of two symmetrical images, then integrate the two recognition results of symmetrical image to reach exact recognition of original test image. The potential advantages of the proposed method are as follows: First, we propose a simple yet efficient method to extract the fastener edge, as well as the EA-HOG feature of the fastener image. Second, the symmetry images indeed reflect some possible appearance of the fastener image which are not shown in the original images, these changes are helpful for us to judge the status of the symmetry samples based on the improved sparse representation algorithm and then obtain an exact judgment of the original test image by combining the two corresponding judgments of its symmetry images. The experiment results show that the proposed approach achieves a rather high recognition result and meets the demand of railway fastener detection.