Defect detection in rail fasteners constitutes a fundamental requirement for ensuring safe and reliable railway operations. Confronted with increasingly demanding inspection requirements of modern rail networks, traditional manual visual inspection methods have proven inadequate. To achieve accurate, efficient, and intelligent detection of rail fasteners, this paper presents an enhanced YOLOv5m-based defect detection model. Firstly, a dual-attention mechanism comprising Squeeze-and-Excitation and Coordinate Attention modules is employed to enhance the model. Secondly, the network architecture is redesigned by adopting MobileNetv3 as the backbone while incorporating structures with Ghost Shuffle Convolution (GSConv) modules and lightweight upsampling operators to reduce computational overhead. Finally, the original CIoU loss function in YOLOv5 is replaced with SIoU to accelerate convergence rate during training. Experimental results on a custom-built rail fastener dataset comprising 6500 images demonstrate that the enhanced model achieves 96.5% mAP and 17.9 FPS, surpassing the baseline by 3.1% and 2.1 FPS, respectively. Compared to existing detection models, this solution exhibits higher accuracy, faster inference, and lower memory consumption, providing critical technical support for edge deployment of rail fastener defect detection systems.
Lv et al. (Thu,) studied this question.