计算机工程2025，Vol.51Issue(4)：261-270,10.DOI:10.19678/j.issn.1000-3428.0069114
改进DeepLabv3+的道路表面裂缝检测方法
Improved DeepLabv3+Road Surface Crack Detection Method
摘要
Abstract
The effective detection of road surface cracks is key to maintaining road safety and prolonging road life.To address the problems of difficulty in identifying small cracks,segmentation fractures,and low segmentation accuracy for traditional road surface crack detection methods,an improved DeepLabv3+road surface crack detection method is proposed to simultaneously reduce the number of model parameters and improve the accuracy of crack detection.First,the backbone of the DeepLabv3+model is replaced with an optimized MobileNetv2 network to reduce the number of parameters and complexity of the model,which speeds up the operation.Second,the Strip Pooling Module(SPM)is integrated into the Atrous Spatial Pyramid Pooling(ASPP)module to enable the network to capture more crack context information and preserve the characteristics of small parts of the crack.Finally,a Convolutional Block Attention Module(CBAM)is introduced to make the network focus more on the pixel region that plays a decisive role in crack detection,which enhances the feature expression ability of crack images.According to the experimental results,the improved DeepLabv3+model achieved a Mean Pixel Accuracy(MPA)of 87.85％,Mean Intersection over Union(MIoU)of 80.53％,accuracy of 97.51％,precision of 88.65％,and F1-Score of 88.24％;compared with the basic DeepLabv3+model,the improvements are 1.77％,2.03％,0.30％,2.25％,and 1.51％,respectively.These indices of the proposed model are higher than those of the U-Net,HR-Net,and PSP-Net models.In addition,the number of parameters of the improved model is 6.382× 106,which is 88.3％of that of the basis model,and the real-time performance is better,making it more suitable for road surface crack detection.关键词
裂缝检测/语义分割/卷积神经网络/条形池化模块/注意力机制Key words
crack detection/semantic segmentation/convolutional neural network/strip pooling module/attention mechanism分类
信息技术与安全科学引用本文复制引用
杨萍,张汐..改进DeepLabv3+的道路表面裂缝检测方法[J].计算机工程,2025,51(4):261-270,10.基金项目
陕西省重点研发计划项目(2023-YBGY-208) （2023-YBGY-208）
陕西省教育厅服务地方专项计划项目(23JC016). （23JC016）
