基于MobileNetV2-DeepLabv3+的混凝土坝水下裂缝语义分割模型  

Semantic segmentation model for underwater cracks in concrete dams based on MobileNetV2-DeepLabv3+

作　　者：何旺 钮新强 田金章 朱延涛[1,3] HE Wang;NIU Xinqiang;TIAN Jinzhang;ZHU Yantao(College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China;State Key Laboratory of Water Resources Engineering and Management,CISPDR Corporation,Wuhan 430010,China;Research Center on National Dam Safety Engineering Technology,Wuhan 430010,China;Changjiang Institute of Survey,Planning,Design,and Research Co.,Ltd.,Wuhan 430010,China)

机构地区：[1]河海大学水利水电学院,江苏南京210098 [2]长江设计集团有限公司水资源工程与调度全国重点实验室,湖北武汉430010 [3]国家大坝安全工程技术研究中心,湖北武汉430010 [4]长江勘测规划设计研究有限责任公司,湖北武汉430010

出　　处：《水利水电科技进展》2024年第6期106-112,共7页Advances in Science and Technology of Water Resources

基　　金：国家重点研发计划项目(2022YFC3005404);国家自然科学基金项目(52309152,U23B20150);江苏省自然科学基金项目(BK20220978)。

摘　　要：为解决深度学习算法难以有效检测混凝土坝水下裂缝的问题,构建了基于MobileNetV2-DeepLabv3+的混凝土坝水下裂缝语义分割模型。该模型引入轻量化网络MobileNetV2,同时将深层特征下采样倍数降为8,以提高小数据集工况下的识别准确率和推理速度;将交叉熵损失函数与Dice损失函数的组合作为模型的损失函数,以缓解类别不平衡问题。工程实例验证结果表明:该模型在测试集上的平均像素准确率和平均交并比分别高达90.87%和86.33%,满足水下裂缝语义分割精度要求;典型工况下的混凝土坝水下裂缝的分割效果优于其他对比模型,泛化能力强;模型具有内存占比小、推理速度快的特点,可用于混凝土坝水下裂缝的检测。To tackle the difficulty of detecting underwater cracks in concrete dams effectively with deep learning algorithms,a semantic segmentation model for underwater cracks in concrete dams based on MobileNetV2-DeepLabv3+was proposed.The lightweight network MobileNetV2 was introduced into the model and the deep feature downsampling multiplier was reduced to 8,so as to improve the recognition accuracy and inference speed under small dataset conditions.To alleviate the problem of category imbalance,the combination of cross entropy loss function and Dice loss function was used as the loss function of the model.The validation results from an engineering case show that the mean pixel accuracy and mean intersection over union of the model on the test set are as high as 90.87%and 86.33%,respectively,meeting the requirements for underwater cracks of high-precision semantic segmentation.Compared with other models,the proposed model shows better segmentation effect of underwater cracks of concrete dams under typical conditions,with strong generalization ability.With features of low memory usage and high inference speed,the proposed model is suitable for underwater crack detection of concrete dams.

关 键 词：混凝土坝 水下裂缝 MobileNetV2-DeepLabv3+ 语义分割 机器视觉 

分 类 号：TV698.1[水利工程—水利水电工程]