Simultaneous tunnel defects and lining thickness identification based on multi-tasks deep neural network from ground penetrating radar images

探地雷达 鉴定(生物学) 人工神经网络 雷达 人工智能 计算机科学 计算机视觉 地质学 遥感 工程类 电信 植物 生物
作者
Bin Liu,Jiaqi Zhang,Ming Lei,Senlin Yang,Zhangfang Wang
出处
期刊:Automation in Construction [Elsevier BV]
卷期号:145: 104633-104633 被引量:16
标识
DOI:10.1016/j.autcon.2022.104633
摘要

The overall assessment of tunnel lining, including shapes, categories, and depths of tunnel internal defects as well as the thickness of tunnel linings is vital to the safe operation of tunnels. We proposed a method comprising a multi-task deep neural network and curve fitting post-processing operation for simultaneously identifying the shapes, categories, and depths of tunnel defects as well as lining thicknesses from ground penetrating radar (GPR) images. The multi-task deep neural network, denoted as M-YOLACT, was designed to identify defects, lining profiles, and hyperbola shapes simultaneously. We introduced a curve-fitting post-processing operation to calculate the dielectric constant automatically based on the hyperbola shapes and evaluated the defect depths and lining thicknesses. The method was validated by numerical simulations, sandbox, and field tests. The method effectively identified the shapes and classes of tunnel defects as well as the thickness profiles from GPR B-Scan images. • A method comprising a multi-task deep neural network and curve fitting post-processing operation is proposed. • Attention mechanism feature fusion, background suppression and multi-task semantic segmentation modules are designed. • A curve fitting post-processing operation for in-situ estimation of the dielectric constant is introduced.
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