Automated Visual Inspection of Multiple Missing Fastening Bolts on the Sides of High-Speed Train Body in Railway Maintenance

Abstract

The rapid development of China Railway High-speed (CRH) has provided safe and comfortable transportation for many people. The fastening bolts (FB) on the sides of CRH fall off occasionally, which might cause serious traffic accidents. However, the automatic inspection of multiple missing FBs on the sides of CRH is seldom conducted. In this study, an automated visual inspection system based on space-scale normalization is proposed to inspect the status of FBs on the sides of CRH. To locate the missing FBs in sequence images, the exact alignment of the target image and its reference image must be ensured. This problem can be solved by normalizing the target image according to its reference image. First, the FBs are located in a specific CRH region, which is called the region of interest (ROI). The target image is cropped according to the size of the ROI. Second, the keypoints are extracted and matched by using the gradient-enhanced scale-invariant feature transform (GESIFT). Furthermore, the horizontal shift between the target image and its reference image is obtained during keypoint matching. Third, the target image and its neighboring image are stitched according to the horizontal shift. Then, the target image is resegmented according to its reference image to complete the normalization of the target image. Finally, the normalized target image can be exactly aligned with its reference image. We propose an image subtraction method to locate the potential missing FBs. The FBs that may be lost are finally determined according to prior knowledge, and the state of the FBs is determined by using high-level image understanding knowledge. Experimental results show that our presented method performs excellently in monitoring the missing FBs on the sides of CRH.