Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning
Water leakages are very important signals that characterize the serious potential structural damages or flaws in shield tunnels. Automatic, timely, and accurate detection of water leakages is of great significance to the safe operation and maintenance for shield tunnels. However, existing methods (e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9395081/ |
| _version_ | 1818416212677230592 |
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| author | Xiaolong Cheng Xuhang Hu Kai Tan Lingwen Wang Lingjing Yang |
| author_facet | Xiaolong Cheng Xuhang Hu Kai Tan Lingwen Wang Lingjing Yang |
| author_sort | Xiaolong Cheng |
| collection | DOAJ |
| description | Water leakages are very important signals that characterize the serious potential structural damages or flaws in shield tunnels. Automatic, timely, and accurate detection of water leakages is of great significance to the safe operation and maintenance for shield tunnels. However, existing methods (e.g., passive optical images) are highly limited by the confined spaces and dim light conditions in shield tunnels. This paper proposes a new method that uses the intensity images of terrestrial mobile LiDAR (Light Detection and Ranging) for automatic leakage detection in shield tunnels based on deep learning. A self-developed terrestrial mobile LiDAR system (Faro Focus X330) are used to simultaneously obtain the point clouds and intensity information of the shield tunnels. The original intensity data are corrected for the distance effect to generate intensity images. A improved Fully Convolutional Network (FCN) based VGG-19 that is a network structure in deep learning is constructed to achieve accurate tunnel leakage detection based on intensity images. The results show that the proposed method can rapidly and accurately detect leakages in shield tunnels and the leakage detection results are not affected by the tunnel attachments (e.g., bolt holes, cables, and metal facilities). The error rate and segmentation speed are 4.8% and 0.6 s, respectively. |
| first_indexed | 2024-12-14T11:47:18Z |
| format | Article |
| id | doaj.art-bce53e12429c42ba9feda376dec2579f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:47:18Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bce53e12429c42ba9feda376dec2579f2022-12-21T23:02:31ZengIEEEIEEE Access2169-35362021-01-019553005531010.1109/ACCESS.2021.30708139395081Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep LearningXiaolong Cheng0https://orcid.org/0000-0003-4380-0991Xuhang Hu1Kai Tan2Lingwen Wang3Lingjing Yang4Department of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, ChinaDepartment of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, ChinaState Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, ChinaSGIDI Engineering Consulting (Group) Company Ltd., Shanghai, ChinaDepartment of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, ChinaWater leakages are very important signals that characterize the serious potential structural damages or flaws in shield tunnels. Automatic, timely, and accurate detection of water leakages is of great significance to the safe operation and maintenance for shield tunnels. However, existing methods (e.g., passive optical images) are highly limited by the confined spaces and dim light conditions in shield tunnels. This paper proposes a new method that uses the intensity images of terrestrial mobile LiDAR (Light Detection and Ranging) for automatic leakage detection in shield tunnels based on deep learning. A self-developed terrestrial mobile LiDAR system (Faro Focus X330) are used to simultaneously obtain the point clouds and intensity information of the shield tunnels. The original intensity data are corrected for the distance effect to generate intensity images. A improved Fully Convolutional Network (FCN) based VGG-19 that is a network structure in deep learning is constructed to achieve accurate tunnel leakage detection based on intensity images. The results show that the proposed method can rapidly and accurately detect leakages in shield tunnels and the leakage detection results are not affected by the tunnel attachments (e.g., bolt holes, cables, and metal facilities). The error rate and segmentation speed are 4.8% and 0.6 s, respectively.https://ieeexplore.ieee.org/document/9395081/LiDAR intensity correctionintensity imagesdeep learningfully convolutional networkleakage detectionshield tunnels |
| spellingShingle | Xiaolong Cheng Xuhang Hu Kai Tan Lingwen Wang Lingjing Yang Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning IEEE Access LiDAR intensity correction intensity images deep learning fully convolutional network leakage detection shield tunnels |
| title | Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning |
| title_full | Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning |
| title_fullStr | Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning |
| title_full_unstemmed | Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning |
| title_short | Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning |
| title_sort | automatic detection of shield tunnel leakages based on terrestrial mobile lidar intensity images using deep learning |
| topic | LiDAR intensity correction intensity images deep learning fully convolutional network leakage detection shield tunnels |
| url | https://ieeexplore.ieee.org/document/9395081/ |
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