人民长江2024，Vol.55Issue(4)：252-261,10.DOI:10.16232/j.cnki.1001-4179.2024.04.033
基于改进U-net的大坝表面混凝土裂缝图像分割方法
Segmentation method of dam surface crack image based on improved U-net model
摘要
Abstract
The detection and identification of surface cracks on dams is of great significance for dam safety,so we study dam surface cracks detection based on deep learning.In view of the complex topological structures and imbalance of positive and nega-tive samples of the crack images,the ASPP and CBAM optimization modules were embedded in the typical U-net model,and a Dice +BCE hybrid loss function was used to replace the single cross entropy loss function.The improved U-net model performed well on a self-made instance dam crack image dataset,with IoU being 47.05%and F1 being 62.99%respectively.Compared with typical U-net model,it had increased by 5.41%and 5.19%,and compared with PSPNet model,it had increased by 3.05%and 3.31%respectively.The improved U-net model provides more accurate pixel classification and richer multi-scale in-formation in crack segmentation tasks,providing a better means for detecting and identifying surface cracks in dam concrete struc-tures.关键词
混凝土裂缝检测/深度学习/语义分割/U-net模型优化/大坝安全Key words
concrete crack detection/deep learning/semantic segmentation/improved U-net model/dam safety分类
建筑与水利引用本文复制引用
赵普,谷艳昌,张大伟,吴云星..基于改进U-net的大坝表面混凝土裂缝图像分割方法[J].人民长江,2024,55(4):252-261,10.基金项目
国家自然科学基金项目(51979175) （51979175）
南京水科院基本科研业务费科研创新团队建设项目(Y722003) （Y722003）
南京水科院基本科研业务费重点项目(Y721005,Y721003) （Y721005,Y721003）
