Abstract: The deterioration of track fasteners is a critical factor affecting railway traffic safety.Utilizing deep learning image re-cognition methods for segmenting images collected by track fastener detection robots can significantly improve the efficiency of fastener defect detection.This paper addresses the current lack of publicly available datasets for track fasteners and the challenges posed by complex backgrounds that increase segmentation difficulty and processing time.This paper manually creats the RFS(Rail Fastener Segmentation) track fastener dataset and proposes a segmentation method based on the Sc-DeepLabV3+ model.By replacing the backbone network of the original DeepLabV3+ model with the lightweight MobileNetV4,it accelerates computation speed and introduces an improved S-ASPP(Switchable Atrous Spatial Pyramid Pooling) module to enable the network to achieve denser pixel sampling,enhancing its ability to extract detailed features.Additionally,it incorporates the CSWin(Cross-Shaped Window Self-Attention) attention mechanism to compute horizontal and vertical attention in parallel,reducing interference from complex backgrounds.In the experimental section,this paper proposes the RailAugment data augmentation technique to effectively increase the diversity and coverage of the dataset,ultimately resulting in a total of 6 832 images,including 4 782 for training,1 366 for validation,and 684 for testing.Experimental results show that the mIoU and mPA reach 95.17% and 97.14%,respectively,which represent improvements of 2.19 percentage point and 0.36 percentage point compared to the original model.Although the performance improvement is relatively small,significant improvements are observed in detailed feature extraction and background interference handling.Furthermore,the Sc-DeepLabV3+ model is validated on the DeepGlobe dataset,demonstrating its robustness and generalization ability.Its inference speed is 51.4 ms and 66.5 ms faster than the mainstream Swin-UNet and Segmenter models,respectively,showing good efficiency and real-time performance.Therefore,this model has broad application potential in railway maintenance and other fields,effectively reducing labor and computational costs while improving detection efficiency.

Key words: Deep learning, Image semantic segmentation, DeepLabV3+, Railway fasteners, Data augmentation