佛山科学技术学院学报（自然科学版）2025，Vol.43Issue(6)：45-51,7.
基于轻量型DeepLabv3+的桥梁裂缝智能识别研究
Structural crack intelligence based on lightweight DeepLabv3+identification research
摘要
Abstract
In order to solve the problems of weak generalization ability and large storage resource demand of existing bridge crack semantic segmentation models,an improved DeepLabv3+model was proposed,which replaced the feature extraction network with the lightweight network MobileNetv2,and combined with the Swish activation function and transfer learning strategy.In order to verify the effectiveness of the improved model,a bridge crack dataset was constructed by using different types of bridge crack images under complex background interference,and the cracks of the improved DeepLabv3+model,DeepLabv3+model,Segnet model and Unet model were trained for crack recognition,and the recognition effects of the four models were compared and analyzed from the aspects of segmentation accuracy,average interaction ratio and model size.The analysis results show that the segmentation accuracy of the improved DeepLabv3+model reaches 93.41%,the average interaction ratio reaches 78.51%,and the F1 score reaches 83.60%.The size of the improved model is only 6.64 MB,which is at the same order of magnitude as the size of the Segnet model and significantly smaller than that of the DeepLabv3+and Unet models.关键词
DeepLabv3+/MobileNetv2/桥梁裂缝/迁移学习/语义分割Key words
DeepLabv3+/MobileNetv2/bridge cracks/transfer learning/semantic segmentation分类
建筑与水利引用本文复制引用
陈舟,温嘉豪,卢汉文,杜和坪..基于轻量型DeepLabv3+的桥梁裂缝智能识别研究[J].佛山科学技术学院学报（自然科学版）,2025,43(6):45-51,7.基金项目
科技部高端外国专家引进计划项目资助(G2022030014L) （G2022030014L）
