Rail surface anomaly detection based on deep learning

The role of the rail is to directly bear the weight of the train and its load transmitted by the wheels, and to guide the direction of the train. Prolonged train travel can cause unavoidable defect to the rails; however, China's railways are developing at a high speed, and rail damage will also increase. In order to ensure the safety of railway operation, the detection of rail damage must be fast and efficient. So, a method is needed to quickly and accurately detect the type of rail defect. Due to the superiority of machine vision detection, a rail surface anomaly detection method based on deep learning is proposed. Firstly, the image data features are extracted through the image annotation tool labeling. After that, the YOLO v5 deep learning network is trained by the training set. Finally, the accuracy of the trained network is checked, and the relevant parameters are modified to adjust the accuracy to the best. The overall rail defect detection accuracy reaches 97%, indicating the feasibility and effectiveness of the proposed method.