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Open AccessArticle

Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data

Key Laboratory of Geotechnical and Underground Engineering, Department of Geotechnical Engineering, Tongji University, Siping Road 1239, Shanghai 200092, China
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Sensors 2020, 20(22), 6669; https://doi.org/10.3390/s20226669
Received: 12 October 2020 / Revised: 17 November 2020 / Accepted: 19 November 2020 / Published: 21 November 2020
(This article belongs to the Special Issue Intelligent Sensors and Computer Vision)
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.). View Full-Text
Keywords: water leakage; mobile laser scanning; point cloud; deep learning; 3D reconstruction; shield tunnel lining water leakage; mobile laser scanning; point cloud; deep learning; 3D reconstruction; shield tunnel lining
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Huang, H.; Cheng, W.; Zhou, M.; Chen, J.; Zhao, S. Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data. Sensors 2020, 20, 6669.

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