Intelligent detection of fastener defects in ballastless tracks based on deep learning

The detection of fastener defects is crucial for ensuring the safety and reliability of high-speed train operations. This paper proposes an intelligent algorithm named YOLO-Fastener for detecting fastener defects in ballastless track systems. The proposed YOLO-Fastener incorporates efficient channel and spatial attention mechanisms, enhancing the extraction of crucial features related to fastener defects. Decision regions of the model in identifying fastener defects are visualized through heatmaps. The model is trained and tested on a limited dataset of high-resolution fastener images collected by a ballastless track detection vehicle equipped with 3-D laser devices. The results show that the precision and recall of the proposed model on the test set are 98.33% and 99.15%, which are 1.63% and 4.81% higher than those of the advanced Faster R-CNN model. In terms of fastener detection efficiency, the proposed model is the fastest with an inference time of 10.4 ms, which is an 18.75% improvement over the result of the advanced YOLOv7 model.