The railway system is a green mode of transport that is essential for contributing significantly to economic growth, enhancing accessibility, and easing regional integration. The safety and efficiency of railway transport systems depend on the condition of wheels, as deterioration of wheels is a major cause of both human life and financial loss. Therefore, real-time monitoring is essential for early detection and preventing failures. This study presents an AI-based framework for real-time railway wheel defect detection, leveraging advanced You Only Look Once (YOLO) models (v5-v12) and a Real-Time Detection (RTD) Transformer model. A custom wayside imaging system was developed, capturing high-resolution images to construct the FaultSeg dataset, addressing class imbalances and annotation challenges. Eight YOLO models and the RTD Transformer were evaluated, with extensive hyperparameter tuning, to identify defects such as wheel flats, shelling, discoloration, and cracks/scratches. The YOLOv5-seg model demonstrated superior performance with 91% precision, 90% recall, and 92% mAP@0.5, achieving real-time processing at 30 FPS with latency under 30 ms. The optimized model was deployed on an edge device for operational railway environments, showcasing its feasibility for real-time defect detection, enhancing predictive maintenance, and improving railway safety. This work contributes to the advancement of AI in condition monitoring by providing a publicly available dataset and demonstrating the practical deployment of a real-time defect detection system.
Shaikh et al. (2025) studied this question.