Crack Detection of Track Slab Based on RSG-YOLO

The surface cracks on high-speed railway ballastless track slabs directly influence their lifespan, while the efficiency of damage detection and maintenance is crucial for ensuring operational safety. Leveraging deep learning image processing technology can significantly enhance detection efficiency. Therefore, in response to the specific attributes of ballastless track slab crack detection, this paper introduces the RSG-YOLO model. By implementing a reparameterized dual-fused feature pyramid structure, we bolster the network’s feature extraction capacity and curtail the loss of crack features during extraction. SIoU is used to replace CIoU to optimize the bounding box regression loss function, reduce the degrees of freedom of the loss function, and improve the convergence speed The GAM attention mechanism is integrated to heighten the model’s responsiveness to diverse channel information. The proposed RSG-YOLO model was evaluated against mainstream models in the field of crack detection. The results demonstrated improved detection accuracy and recall rates. Specifically, when compared to baseline models, our approach exhibited significant advancements in reducing both missed detections and false alarms. These improvements were quantified by a 4.34% increase in crack detection accuracy and a 3.08% rise in mAP_0.5. Consequently, the RSG-YOLO model effectively enables precise detection of track slab cracks.