Railway Track Fasteners Fault Detection using Deep Learning

Abstract

The railway track fastener is the most important component of the rail fastening system. It can fasten the railway track onto the crosstie, and has great influence on train's stability and safety. In the early days, track patrol and maintenance were carried out on foot by track workers, and gradually adjusted to the use of track patrol vehicles, but still completely rely on manual visual inspection. However, the results of visual inspection may be limited by the vehicle 's speed and inspection angle, or even long -term visual inspection may cause fatigue, and the problem of omission. In this study, we not only collect the related research results of track inspection, but also set up the image collecting device for track fastener including image recording equipment and lighting equipment. Yolo v3 is deployed and trained as the deep learning model, then the accuracy and recall rates of damaged fasteners are verified from the test dataset. In the experiments, we used the GoPro motion camera to capture images and record a total of 20 km track fastener images. The precision rate and recall rate of fasteners detection including defects were 89% and 95%, respectively, which can meet the demand of efficient inspection for railway track workers.