Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data
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 th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/22/6669 |
| _version_ | 1797547206470795264 |
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| author | Hongwei Huang Wen Cheng Mingliang Zhou Jiayao Chen Shuai Zhao |
| author_facet | Hongwei Huang Wen Cheng Mingliang Zhou Jiayao Chen Shuai Zhao |
| author_sort | Hongwei Huang |
| collection | DOAJ |
| description | 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.). |
| first_indexed | 2024-03-10T14:41:02Z |
| format | Article |
| id | doaj.art-7891552c930b41bfa7618f32a42f29d6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:41:02Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7891552c930b41bfa7618f32a42f29d62023-11-20T21:48:41ZengMDPI AGSensors1424-82202020-11-012022666910.3390/s20226669Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning DataHongwei Huang0Wen Cheng1Mingliang Zhou2Jiayao Chen3Shuai Zhao4Key Laboratory of Geotechnical and Underground Engineering, Department of Geotechnical Engineering, Tongji University, Siping Road 1239, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering, Department of Geotechnical Engineering, Tongji University, Siping Road 1239, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering, Department of Geotechnical Engineering, Tongji University, Siping Road 1239, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering, Department of Geotechnical Engineering, Tongji University, Siping Road 1239, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering, Department of Geotechnical Engineering, Tongji University, Siping Road 1239, Shanghai 200092, ChinaOn-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.).https://www.mdpi.com/1424-8220/20/22/6669water leakagemobile laser scanningpoint clouddeep learning3D reconstructionshield tunnel lining |
| spellingShingle | Hongwei Huang Wen Cheng Mingliang Zhou Jiayao Chen Shuai Zhao Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data Sensors water leakage mobile laser scanning point cloud deep learning 3D reconstruction shield tunnel lining |
| title | Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data |
| title_full | Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data |
| title_fullStr | Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data |
| title_full_unstemmed | Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data |
| title_short | Towards Automated 3D Inspection of Water Leakages in Shield Tunnel Linings Using Mobile Laser Scanning Data |
| title_sort | towards automated 3d inspection of water leakages in shield tunnel linings using mobile laser scanning data |
| topic | water leakage mobile laser scanning point cloud deep learning 3D reconstruction shield tunnel lining |
| url | https://www.mdpi.com/1424-8220/20/22/6669 |
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