Institute of Transportation, MOTC

      The rail fasteners are responsible for fixing the tracks on the sleepers to prevent the tracks from loosening and deforming. The traditional inspection method of railway track fasteners adopts human visual inspection. Artificial intelligence (AI) deep learning has developed rapidly and made a breakthrough in many fields in recent years. Therefore, this study uses artificial intelligence to detect missing components of railway tracks and reduce the burden and time of manual inspection. The goal is to promote this technology to field applications. Firstly, we have surveyed some commercial track fastener inspection products from both domestic and foreign; analyze the deployed equipment and method for domestic applicability. Then, the image collecting equipment for the track components, including image recording and lighting, is established. There are ten and six types of classes defined for the top and side view of the track, respectively. YOLOv4-tiny, for its superior performance, is selected for our application. More than 70 km of track images were recorded in experiments. Moreover, the performance metrics (recall rate, mAP) are (91%, 91.7%) and (94%, 99.2%) for the top and side-view, respectively. The execution speed of YOLOv4-tiny also reached 150 fps. Meanwhile, the AI model was compared with the human eye-sight inspection, and the overall performance was better than human inspections.