Detection of Rail Surface Defects Based on Ensemble Learning of YOLOv5

Railway transportation has increased its capacity with the increase in railway line length in recent years. The development of high-speed trains also contributed to this situation. The increase in passenger and cargo capacity has further increased the importance of security measures. In order to ensure the safety of the railway line, it is necessary to inspect the line at certain intervals. Detection of defects on the rail is extremely important in the maintenance of the railway line. This study focuses on the detection of defects on the rail component, which is an important part of railway maintenance. In the study, it was tried to detect the defects on the rail with YOLO, which is an object detection method. In the study, it has been shown that model ensembling gives better results than YOLO models that validate alone. A method based on ensemble learning is proposed for different YOLO models. Experiment results showed that the detection rate including all classes on the data set containing 8 different defects was over 80%.