• • Achieves 99.1% average detection accuracy, a 4.29% improvement over baseline YOLOv5, directly reducing false negatives in safety-critical wheelset inspections.
• • Maintains 15 ms per image inference speed, enabling real-time deployment on edge devices for continuous subway tread monitoring without operational delays.
• • Outperforms YOLOv12 in convergence speed, detection accuracy, and inference speed, demonstrating that targeted architectural modifications can surpass newer generic detectors.
• • Integrates W-MPDIoU loss function to overcome limited labeled data and annotation inaccuracies, accelerating model convergence and improving robustness under data-scarce industrial conditions.