Multi-Feature Fusion YOLO Approach for Fault Detection and Location of Train Running Section

This paper investigates the implementation of the YOLO (You Only Look Once) framework for defect detection, specifically targeting challenging conditions such as low-light environments, occluded objects, and requirements for non-contact measurement. Empirical evaluations demonstrate that the YOLO architecture delivers exceptional object detection capabilities, enabling rapid yet precise real-time analysis, scalability across diverse object sizes, and resilience to environmental variability. By integrating a multi-scale feature fusion strategy, the framework significantly enhances the detection accuracy of defective components. Furthermore, its non-invasive approach eliminates potential damage risks and operational costs associated with conventional inspection methodologies. Experimental results on a 1270-image dataset show 87.5% mAP, 92% recall, 89% accuracy, and 22 FPS, demonstrating high performance.