水利水电技术（中英文）2024，Vol.55Issue(6)：159-171,13.DOI:10.13928/j.cnki.wrahe.2024.06.013
基于改进U2-Net模型的混凝土结构表面裂缝检测
Surface crack detection of concrete structure based on improved U2-net model
摘要
Abstract
[Objective]In view of the poor continuity and low recognition rate of structural surface cracks with complex back-ground,the crack detection method based on depth learning has the problems of large model parameters.[Methods]This paper constructs a lightweight model U2-Net_Aggregation that aggregates multi-scale information based on the U2-Net framework,which is used to learn fracture characteristics in complex background.By adding jump connections,the model enables each decoding layer to aggregate all shallow coding features above the layer to obtain sufficient feature details and improve the accuracy of crack segmentation;Using depthwise separable convolution(DSC)to improve the ReSidual U-blocks(RSU),a new residual module(RSU-DSC-ECA)is proposed to reduce the problem of increasing model complexity when aggregating multi-scale information.The efficient channel attention(ECA)improves the sensitivity of the model to fracture areas and its anti-interference ability to complex backgrounds.[Results]The ablation experiment was carried out on three sets of fracture data sets.Compared with U2-Net,the improved model(U2-Net_Aggregation)has excellent performance in precision,intersection over union and f1-measure.To verify the model's ability to identify cracks in complex backgrounds,experiments were conducted using concrete structure data collected by UAV,which outperformed FCN,SegNet,U-Net and U2-Net.[Conclusion]The improved model improved by 4.18%,2.97%and 2.03%in recall,intersection over union and f1-measure,respectively,compared to U2-Net,which can quickly and accurately detect cracks with the help of UAV images,providing a new method for structural crack detection.关键词
混凝土结构/裂缝检测/深度学习/语义分割/U2-Net/神经网络/混凝土Key words
concrete structural/crack detection/deep learning/semantic segmentation/U2-Net/neural networks/concrete分类
建筑与水利引用本文复制引用
程浩东,李怡静,李玥康,胡强,王姣..基于改进U2-Net模型的混凝土结构表面裂缝检测[J].水利水电技术（中英文）,2024,55(6):159-171,13.基金项目
江西省自然科学基金项目(20232BAB204091) （20232BAB204091）
国家自然科学基金项目(41501454) （41501454）
江西省水利厅科技项目(202123YBKT25) （202123YBKT25）
