中外公路2025，Vol.45Issue(3)：37-45,9.DOI:10.14048/j.issn.1671-2579.2025.03.005
基于Swin-U的路面裂缝分割研究
Research on Pavement Crack Segmentation Based on Swin-U
摘要
Abstract
To address the issues of overfitting,low computational speed,and insufficient target information extraction in pavement crack segmentation tasks,this study proposed a Swin-U network model based on the U-Net architecture.The model adopted the Swin-Transformer as the feature extraction module to enhance the model's fitting capability and enable more accurate crack feature extraction,thereby enhancing segmentation accuracy.Additionally,a stable loss function,Focal Loss,was introduced to improve the accuracy of target segmentation.Experiment results on a proprietary pavement crack dataset show that the Swin-U network model achieves pixel-level segmentation of crack images and significantly outperforms the traditional U-Net.On the test set,it improves the intersection over union and F1 score by 25.00%and 27.61%,respectively.This improved model not only provides more reliable technical support for road maintenance decision-making but also offers a reference for the optimization of pavement crack segmentation methods.关键词
道路裂缝分割/图像分割/Swin-Transformer/U-Net/深度神经网络/Focal LossKey words
pavement crack segmentation/image segmentation/Swin-Transformer/U-Net/deep neural network/Focal Loss分类
交通工程引用本文复制引用
王华,汪良财,熊峰,胡靖..基于Swin-U的路面裂缝分割研究[J].中外公路,2025,45(3):37-45,9.基金项目
国家重点研发计划项目(编号:2019YFE0116300) （编号:2019YFE0116300）
中央高校基本科研业务费专项资金资助项目(编号:2242021R41163) （编号:2242021R41163）
江西省交通运输厅科技项目(编号:2023H0046) （编号:2023H0046）
