Automatic Fault Detection of Multiple Targets in Railway Maintenance Based on Time-Scale Normalization

Automatic fault inspection, aiming at either a single target or an object of a position predictive in the image, is a common paradigm in railway maintenance. However, the fault detection of multiple targets with unknown initial locations is rarely conducted. In this paper, an automatic fault detection system for multiple targets based on time-scale normalization is proposed with the use of high-quality linear camera. For location of the multiple targets in the image sequences, it is important to make sure that the target and standard images are exactly aligned. To deal with this problem, the image distortion, caused by the velocity fluctuation of the moving train or the discordant movement between the linear scan camera and the train, must be corrected according to the size of the standard image. The scale-invariant feature transformation points based on Sobel gradient are first extracted and matched accurately with the geometric constraint. Then, the matched images are divided into subblocks in horizontal direction. The feature points in each subblock are quantized to a typical feature point. Finally, the time-scale normalization is used to achieve the accurate alignment for the target images. In the procedure of fault inspection, an image subtraction approach is presented for locating the positions of the potential fault regions. According to the priori knowledge, the type of the possible fault regions can be finally confirmed, and the customized high-level image understanding knowledge is used to identify the status of multiple targets. The practical application in detecting the abnormalities of China Railway High-Speed parts demonstrates that the proposed method exhibits an excellent performance in monitoring multiple targets with unknown positions in the high-speed railway.