摘要
针对道路裂缝目标分割任务中存在的过拟合、计算速度低和目标信息提取不足等问题,该文基于U⁃Net网络构建Swin⁃U网络模型。该模型以Swin⁃Transformer作为特征提取模块,提高模型的拟合程度,可更精准地提取裂缝特征,从而提高分割精度;同时引入稳定的损失函数Focal Loss来提高目标分割的精度。在自有道路裂缝数据集上的试验结果表明:Swin⁃U网络模型实现了裂缝图像的像素级分割,其性能显著优于传统的U⁃Net,在测试集上的交并比和F1分数分别提高了25.00%和27.61%。该改进模型不仅为道路养护决策提供了更可靠的技术支持,也为道路裂缝分割方法的优化提供了参考。
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.
作者
王华
汪良财
熊峰
胡靖
WANG Hua;WANG Liangcai;XIONG Feng;HU Jing(Jiangxi Jiujiang Yangtze River Expressway Bridge Co.,Ltd.,Jiujiang,Jiangxi 332105,China;Intelligent Transportation System Research Center,Southeast University,Nanjing,Jiangsu 211189,China)
出处
《中外公路》
2025年第3期37-45,共9页
Journal of China & Foreign Highway
基金
国家重点研发计划项目(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)
江西省交通运输厅科技项目(编号:2023H0046)。
