Affiliation:
1. School of Civil Engineering, Central South University, Changsha 410075, China
2. Key Laboratory of Heavy Haul Railway Engineering Structure, Ministry of Education, Changsha 410075, China
3. Research Center for Intelligent Monitoring of Rail Transit Infrastructure, Central South University, Changsha 410075, China
4. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
5. National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), The Hong Kong Polytechnic University, Hong Kong 999077, China
Abstract
The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high accuracy, and ease to deploy. The 2D B-scan image output by ultrasonic detectors contains rich features of defects; however, rail engineers manually identify and localize the defect image, which can be time-consuming, and the image may be subject to missing identification or mistakes. This paper adopted state-of-the-art deep learning algorithms for novel B-scan images for the automatic identification and localization of rail internal tracks. First, through image pre-processing of classification, denoising, and augmentation, four categories of defect image datasets were established, namely crescent-shaped fatigue cracks, fishbolt hole cracks, rail web cracks, and rail base transverse cracks; then, four representatives of deep learning object detection networks, YOLOv8, YOLOv5, DETR, and Faster R-CNN, were trained with the defects dataset and further applied to the testing dataset for defect identification; finally, the performances of the three detection networks were compared and evaluated at the data level, the network structure level, and the interference adaptability level, respectively. The results show that the YOLOv8 network can effectively classify and localize four categories of internal rail defects in B-scan images with a 93.3% mean average precision at three images per second, and the detection time is 58.9%, 376.8%, and 123.2% faster than YOLO v5, DETR, and Faster R-CNN, respectively. The proposed approach could ensure the real-time, accurate, and efficient detection and analysis of internal defects to a rail.
Funder
High-Speed Railway Infrastructure Joint Fund of the National Natural Science Foundation of China
the National Natural Science Foundation of China
National Natural Science Foundation of China
The Hong Kong Polytechnic University Postdoctoral Fellowship Scheme