土木与环境工程学报（中英文）2024，Vol.46Issue(1)：215-222,8.DOI:10.11835/j.issn.2096-6717.2022.079
基于M-Unet的混凝土裂缝实时分割算法
Real-time segmentation algorithm of concrete cracks based on M-Unet
摘要
Abstract
Mainstream deep learning algorithm for crack segmentation consumes a lot of computing resources while the traditional image processing methods are of low detection accuracy and lost crack features.In order to realize the real-time detection of concrete cracks and the segmentation of cracks at the pixel level,a crack semantic segmentation model based on lightweight convolutional neural network M-Unet is proposed.Firstly,the MobileNet_V2 lightweight network is improved,its network structure is trimmed and the activation function is optimized,and then the encoder part with huge parameters of U-Net is replaced by the improved MobileNet_V2 to realize the lightweight of the model and improve the segmentation effect of cracks.The SegCracks data set containing 5 160 crack images is constructed to verify the proposed method.The experimental results show that the crack segmentation effect of the optimized M-Unet is better than the mainstream segmentation networks of U-Net,FCN8 and SegNet and the traditional image processing techniques,the obtained IoU_Score is 96.10％,F1_Score is 97.99％.Compared with the original U-Net,the weight file size M-Unet is reduced by 7％,the iteration time and prediction time are reduced by 63.3％and 68.6％respectively,and the IoU_Score and F1_Score are increased by 5.79％and 3.14％respectively.The cross validation results on different open source data sets are good,which shows that the proposed network has the advantages of high accuracy,good robustness and strong generalization ability.关键词
混凝土裂缝/卷积神经网络/深度学习/裂缝检测/裂缝分割Key words
concrete cracks/convolutional neural network/deep learning/crack detection/crack segmentation分类
建筑与水利引用本文复制引用
孟庆成,李明健,万达,胡垒,吴浩杰,齐欣..基于M-Unet的混凝土裂缝实时分割算法[J].土木与环境工程学报（中英文）,2024,46(1):215-222,8.基金项目
国家自然科学基金(52078442) （52078442）
四川省科技计划(2021YJ0038)National Natural Science Foundation of China(No.52078442) （2021YJ0038）
Science and Technology Program of Sichuan Province(No.2021YJ0038) （No.2021YJ0038）
