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Corresponding Author

胡靖，男，博士，副研究员.E-mail：220213513@seu.edu.cn

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 F 1 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.

Publication Date

6-23-2025

DOI

10.14048/j.issn.1671-2579.2025.03.005

First Page

37

Last Page

45

Submission Date

August 2025

Reference

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