Deep Learning and Machine Vision-Based Inspection of Rail Surface Defects

Abstract

Surface defects are usually the early phenomenon of rail failure, which threatens the safety of railroad transportation critically, and the timely detection of surface defects helps to eliminate the potential risk of rail and reduce the chance of railroad safety accidents. The existing methods of detecting surface defects on rails suffer from a large performance degradation in the application of rails containing pollutions such as rust and oil. Therefore, this article proposes a multilevel, end-to-end fast rail surface defect detection method. First, rail extraction was performed based on the stability of the standard deviation of the edge pixels. Then, differential box-counting (DBC) and GrabCut algorithm are then combined for defect segmentation to boost the speed and accuracy of extracting complex defects. Finally, YOLO v2 is used to precisely locate and detect defects. The experimental results show that the proposed method performs well, with an average accuracy of 97.11%, an average recall of 96.10%, and an average frame rate of 0.0064 s. In addition, the proposed method offers a high robustness in the tests of different use cases.