摘要
文章针对隧道结构渗漏水病害巡检效率低的问题,基于隧道结构三维激光扫描影像建立了一个具备了一定规模的渗漏水病害数据集,选择了三种经典的图像分割的深度学习模型,分析和比较了三种模型在渗漏水病害识别的区别及差异,验证了图像分割的深度学习模型的有效性。
Aiming at the problem of low efficiency in the inspection of water leakage diseases in tunnel structures,this paper establishes a data set of water leakage diseases with a certain scale based on 3D laser scanning images of tunnel structures,and chooses three classic deep learning models for image segmentation,analyzes and compares the distinctions and differences of the three models in the recognition of water leak disease,and verifies the effectiveness of the deep learning model for image segmentation.
作者
徐艺文
王维
王鲁杰
陈颖
郭春生
李家平
XU Yi-wen;WANG Wei;WANG Lu-jie;CHEN Yin;GUO Chun-sheng;LI Jia-ping(SGIDI Engineering Consulting(Group)Co.,Ltd.;Shanghai Metro Monitoring Management Co.,Ltd.)
出处
《智能建筑与智慧城市》
2024年第1期160-163,共4页
Intelligent Building & Smart City
