Multi-Class Rail Defect Detection Using Advanced Artificial Intelligence

Railway infrastructure demands continuous monitoring to ensure operational safety and prevent catastrophic failures. Conventional inspection approaches, including manual visual examination and non-destructive testing, exhibit constraints in scalability, processing speed, and automation capability. This investigation presents a rigorous evaluation of three advanced deep learning frameworks—YOLOv11, YOLO-NAS, and Roboflow 3.0 (Robolow’s default model) on roboflow platform—for detecting multiple rail defect categories, with particular attention to addressing class imbalance challenges. A real-world dataset containing 7,587 images with 35,549 annotations spanning ten defect categories was assembled from North Western Railways, India. The research implements targeted augmentation methodologies and class-weighted training protocols to address severe imbalance (ranging from 0.4% for Broken-Rail to 17.6% for Shelling). Performance assessment across accuracy metrics, computational efficiency, and deployment viability demonstrates YOLOv11 achieving superior mean Average Precision (89.8%), while YOLO-NAS exhibits competitive speed-accuracy trade-offs. Critical examination of cross-regional generalization, temporal robustness, and real-time deployment constraints delivers practical insights for railway authorities implementing AI-based inspection frameworks.