水力发电学报2024，Vol.43Issue(12)：23-33,11.DOI:10.11660/slfdxb.20241203
Swin-Unet在坝面混凝土裂缝自动标注与分割方法的研究
Automatic annotation and segmentation of dam concrete cracks in images based on Swin-Unet
摘要
Abstract
A general segmentation model for dam concrete surface cracks in images often faces a shortage of training data due to the high cost of manual annotation,resulting in insufficient accuracy in its results.This paper presents an automatic annotation and segmentation algorithm that integrates image feature extraction and deep learning techniques.The algorithm first adopts a strategy for combining binarization and edge detection to annotate unlabeled crack defects automatically,and constructs a large-scale dataset of 19,101 crack masks.Then,a hybrid model for combining Swin-Transformer and Unet(Swin-Unet)is designed by introducing the hierarchical attention mechanism of Swin-Transformer into the Unet architecture.Finally,the model is validated through experiments and result analyses on the self-constructed datasets.The results show this Swin-Unet model achieves the highest crack classification accuracy(100%)and a segmentation IoU of 93.1%or 7.5%improvement over the Unet segmentation model(85.6%).This indicates the introduction of the Swin-Transformer architecture enhances the model's capability of associating global and local features,significantly improving the crack defect segmentation accuracy.Besides,an analysis of the minimum enclosing rectangle of cracks reveals significant clustering in both the direction and shape distribution of cracks,deepening our understanding of the mechanisms of crack formation and useful for predicting crack propagation direction.关键词
深度学习/裂缝分割/自动标注/Otsu二值化/边缘检测/注意力机制Key words
deep learning/crack segmentation/automatic annotation/Otsu binarization/edge detection/attention mechanism分类
交通运输引用本文复制引用
杨汉龙,陈锦剑,潘越..Swin-Unet在坝面混凝土裂缝自动标注与分割方法的研究[J].水力发电学报,2024,43(12):23-33,11.基金项目
国家重点研发计划(2023YFC3009405) （2023YFC3009405）
