Huang Hongwei, Cheng Wen, Zhou Mingliang, Chen Jiayao, Zhao Shuai
Key Laboratory of Geotechnical and Underground Engineering, Department of Geotechnical Engineering, Tongji University, Siping Road 1239, Shanghai 200092, China.
Sensors (Basel). 2020 Nov 21;20(22):6669. doi: 10.3390/s20226669.
On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakages and the leakage information (area, location, lining segments, etc.).
地铁隧道渗漏的现场人工检查一直面临着效率低下和准确性差的问题。一种自动化、高精度且稳健的漏水检测方法对于改进人工检查方法至关重要。由于缺乏空间信息，现有方法无法提供渗漏位置。因此，本文提出了一种利用移动激光扫描获取的隧道衬砌点云数据进行漏水检测的集成深度学习方法。它由以下三个部分组成：(1) 使用获取的隧道衬砌点云建立漏水数据集；(2) 通过基于掩膜区域的卷积神经网络进行自动渗漏检测；(3) 通过一种新颖的三角网格方法对三维空间中的漏水进行可视化和定量评估。测试结果表明，该方法实现了对隧道衬砌漏水在三维空间中的自动检测和评估，为检查人员提供了检测到的漏水的直观三维全景以及漏水信息（面积、位置、衬砌段等）。
