文献类型：期刊文献

中文题名：基于DeepLabV3+的轻量级道路裂缝分割

英文题名：DeepLabV3+ based lightweight segmentation for road cracks

作者：焦新伟[1];郭卫斌[1];胡文龙[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2026

卷号：43

期号：4

起止页码：67

中文期刊名：公路交通科技

外文期刊名：Journal of Highway and Transportation Research and Development

收录：;北大核心:【北大核心2023】；

基金：国家自然科学基金项目(62076094)。

语种：中文

中文关键词：道路工程;轻量级分割;DeepLabV3+;MobileNetV3;高效通道注意力

外文关键词：road engineering;lightweight segmentation;DeepLabV3+;MobileNetV3;efficient channel attention

摘要：【目标】针对传统道路裂缝分割方法分割精度低、模型参数量大的核心问题,提出一种轻量且高效的裂缝分割网络,以实现精度与轻量化的平衡。【方法】采用改进DeepLabV3+的编码器-解码器结构构建轻量级裂缝分割网络,核心改进包括2方面:一是将原始模型的特征提取网络Xception替换为轻量级MobileNetV3,通过深度可分离卷积与通道注意力机制结合,在高效提取裂缝特征的同时降低参数量;二是在特征提取网络输出端嵌入高效通道注意力机制模块,通过自适应分配各通道权重,强化网络对裂缝像素特征的关注,提升分割精度。整个网络以道路裂缝图像为研究载体,通过特征提取、注意力增强、编码解码映射完成裂缝分割。在道路裂缝森林数据集上,所提网络的平均交并比和F1得分较传统SegNet方法分别提升11.6%和5.4%;在CRACK500数据集上,平均交并比较SegNet提升1.1%,F1得分较NestNet提升17.5%。CRACK500数据集上的消融试验显示,与原始DeepLabV3+相比,所提网络平均交并比提升0.7%、F1得分提升0.6%,模型参数量减少89.5%,训练时间缩短64.1%,验证了网络轻量性与有效性。【结论】本研究的核心创新在于通过MobileNetV3替换与高效通道注意力机制嵌入,实现了DeepLabV3+网络的轻量化改进,解决了传统方法精度与参数量的矛盾。
[Objective]Traditional road crack segmentation methods have two core problems,i.e.,low segmentation accuracy and large number of model parameters.This study proposes a lightweight and efficient segmentation network for road cracks,aiming to balance accuracy and lightweight performance.[Method]It constructed a lightweight crack segmentation network based on the improved encoder-decoder structure of DeepLabV3+.The core improvements consisted of two aspects.First,replacing the original model's feature extraction network Xception with the lightweight MobileNetV3,which combined depthwise separable convolutions with channel attention mechanisms.This combination could extract crack features efficiently while reducing the number of model parameters.Second,embedding an efficient channel attention module to the output end of feature extraction network.This module adaptively assigned weights to each channel,thereby enhancing the network's focus on crack pixel features and improving the segmentation accuracy.The road crack images were the research carrier through entire network.It completed crack segmentation through feature extraction,attention enhancement,and encoding-decoding mapping.[Result]The proposed network improved the mean intersection over union by 11.6%on the Crack Forest Dataset compared with traditional SegNet method;and its F1-score was improved by 5.4%.The mean intersection over union of the proposed network was 1.1%higher than that of SegNet on the dataset CRACK500;and its F1-score was 17.5%higher than that of NestNet.The ablation test on dataset CRACK5OO indicated that the proposed network improved the mean intersection over union by O.7%compared with the original DeepLabV3+;and its F1-score was improved by 0.6%.Meanwhile,the model parameters were reduced by 89.5%;and the training time was shortened by 64.1%.These results verified the lightweight nature and effectiveness of the proposed network.[Conclusion]The core innovation of this study lies in two aspects.One is the replacement of MobileNetV3.The other is the embedding of efficient channel attention mechanism,realizing the lightweight optimization on DeepLabV3+network.The findings solve the contradiction between accuracy and parameter quantity in traditional methods.