水力发电学报2025，Vol.44Issue(9)：114-124,11.DOI:10.11660/slfdxb.20250910
融合多尺度特征与注意力机制的混凝土裂缝语义分割模型
Semantic segmentation model for concrete cracks integrating multi-scale features and attention mechanisms
摘要
Abstract
Cracking,as one of the most common defects in concrete dams,weakens the integrity and durability of dam structures;crack detection has been a crucial task in the operation and maintenance management of concrete dams.Aimed at the drawbacks of traditional image-processing techniques in crack detection-such as substantial manual intervention and limited generalization ability,this paper presents a semantic segmentation model of dam cracks that incorporates multi-scale features and attention mechanisms.This model uses ResNet-50 as its backbone network for integrating the Path Aggregation Network to recycle shallow features,and makes use of the mechanisms of channel attention and spatial attention.These mechanisms enhance the model's ability to identify critical features,thus effectively improving its segmentation accuracy.Then,based on its semantic segmentation results,the digital image technology is adopted to quantify the geometric characteristics of cracks,including area,length,average width,and maximum width.Tests on a crack image dataset show this new model achieves a crack segmentation Intersection over Union of 82.02%and an F1 score of 90.12%;Quantification results of geometric characteristics exhibit an excellent agreement with the real values and a satisfactory accuracy.Thus,our method demonstrates significant potential for application in crack detection and geometric characteristics quantification for concrete dams.关键词
混凝土坝运维/裂缝分割/几何特征量化/深度学习/注意力机制Key words
concrete dam operation and maintenance/crack segmentation/geometric feature quantification/deep learning/attention mechanism分类
建筑与水利引用本文复制引用
封婧仪,梁晖,齐智勇,谭大文,任秋兵,李明超..融合多尺度特征与注意力机制的混凝土裂缝语义分割模型[J].水力发电学报,2025,44(9):114-124,11.基金项目
国家自然科学基金项目(52409170 （）
52179139) （）
水资源高效利用与工程安全国家工程研究中心开放基金项目(GJGCZX-JJ-202410) （GJGCZX-JJ-202410）
