华东交通大学学报2025，Vol.42Issue(1)：52-60,9.
基于双模态融合的钢轨表面缺陷分割研究
Research on Rail Surface Defect Segmentation Based on Bimodal Fusion
摘要
Abstract
Due to the long-term repeated loading,surface defects occur in high-speed railway steel rails.In order to improve the accuracy and speed of surface defect detection for multiple classes and scales of steel rails in com-plex scenarios,a steel rail surface defect segmentation network based on multimodal fusion(DAFNet)is de-signed.Firstly,a steel rail surface defect dataset containing visible light and infrared channels is constructed,and an improved dual-branch network architecture is adopted to increase segmentation speed.Simultaneously,a bi-modal adaptive fusion module(BAFM)is designed to achieve adaptive feature fusion,improving the segmenta-tion accuracy of steel rail surface defects in complex scenarios.Additionally,a spatial detail extraction module(SDEM)and a key information enhancement module(KIEM)are designed to further enhance the perception of defect edges and address the low contrast between defects and backgrounds in complex scenarios.Experiments show that the accuracy and mIoU of the designed network segmentation reach 68.13%and 59.96%respectively,which are significantly better than other mainstream networks.Moreover,FLOPs,parameter quantity,and model size are 17.41 GFLOPs,1.38 M,and 5.67 MB respectively,which are better than most mainstream networks.The designed network significantly improves the segmentation accuracy of steel rail surface defects and has a high segmentation speed,which is of great significance for ensuring the safe operation of high-speed railways.关键词
语义分割/钢轨表面缺陷/深度学习/红外图像/可见光图像/双模态融合Key words
semantic segmentation/rail surface defects/deep learning/infrared image/visible light image/bi-modal fusion分类
交通工程引用本文复制引用
罗晖,韩岳霖,马治伟,斯成浩..基于双模态融合的钢轨表面缺陷分割研究[J].华东交通大学学报,2025,42(1):52-60,9.基金项目
国家自然科学基金项目(62262021) （62262021）
