Intelligent Detection of Surface Defects in High‐Speed Railway Ballastless Track Based on Self‐Attention and Transfer Learning

Abstract

The detection of ballastless track surface (BTS) defects is a prerequisite for ensuring the safe operation of high‐speed railways. Traditional convolutional neural networks fail to fully exploit contextual information and lack global pixel representations. The extensive stacking of convolutions leads deep learning models to play a black‐box detection role, lacking interpretability. Due to the current lack of sufficient high‐quality surface data for ballastless tracks, it is a severe constraint on the accurate identification of the substructure state in high‐speed railways. This paper proposes an intelligent detection method for BTS defects named TrackNet based on self‐attention and transfer learning. The method enhances the fusion ability of global features of BTS defects using multihead self‐attention. The model’s dependence on extensive defect data is reduced by transferring knowledge from large‐scale publicly available datasets. Experimental results demonstrate that compared to advanced Swin Transformer model results, the TrackNet model achieves improvements in average accuracy and F1‐score by 5.15% and 5.16%, respectively, on limited test data. The TrackNet model visualizes the decision regions of the model in identifying BTS defects, revealing the black‐box recognition mechanism of deep learning models. This research performs engineering applications and provides valuable insights for the multiclass recognition of BTS defects in high‐speed railways.