EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images
Water leakage poses a significant threat to the safe operation of tunnels. We utilized a mobile laser Scanner (MLS) to collect point cloud data under adverse tunnel conditions. A data mapping approach was employed to generate MLS point cloud intensity images. Tailored for multiscale point cloud inte...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10836741/ |
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| _version_ | 1849344653311082496 |
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| author | Zhenyu Liu Xianjun Gao Yuanwei Yang Lei Xu Shaoning Wang Ningsheng Chen Zhiwei Wang Yuan Kou |
| author_facet | Zhenyu Liu Xianjun Gao Yuanwei Yang Lei Xu Shaoning Wang Ningsheng Chen Zhiwei Wang Yuan Kou |
| author_sort | Zhenyu Liu |
| collection | DOAJ |
| description | Water leakage poses a significant threat to the safe operation of tunnels. We utilized a mobile laser Scanner (MLS) to collect point cloud data under adverse tunnel conditions. A data mapping approach was employed to generate MLS point cloud intensity images. Tailored for multiscale point cloud intensity images, we devised a lightweight object detection network to identify areas affected by water leakage promptly. Integrating efficient receptive field expansion convolution into lightweight network models facilitated efficient feature extraction. Additionally, we designed an effective attention-inducing downsampling unit to construct a tunnel leakage detection model. This module comprehensively handles target features, enhances target context information, enlarges the receptive field, and establishes a unique information processing framework for detecting various multisize targets, achieving outstanding detection performance. Moreover, we developed a dynamic threshold adaptive loss function that automatically adjusts the loss function based on leakage detection performance to enhance the model's ability to detect challenging targets. Finally, we employed a twin attention-guided dynamic detection-head to improve detection performance. Experimental results demonstrate that our method effectively transforms the process from MLS point cloud data acquisition to high-precision target detection. The leakage detection network has achieved an optimal balance between efficiency and accuracy, surpassing comparative methods, thereby ensuring the secure operation of shield tunnels. |
| format | Article |
| id | doaj-art-7e68fe9f11304f70a6be56d2232b64b6 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-7e68fe9f11304f70a6be56d2232b64b62025-08-20T03:42:37ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01187334734610.1109/JSTARS.2025.352811110836741EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity ImagesZhenyu Liu0https://orcid.org/0009-0002-7178-0131Xianjun Gao1https://orcid.org/0000-0003-1144-8479Yuanwei Yang2Lei Xu3Shaoning Wang4Ningsheng Chen5Zhiwei Wang6Yuan Kou7School of Geosciences, Yangtze University, Wuhan, ChinaSchool of Geosciences, Yangtze University, Wuhan, ChinaSchool of Geosciences, Yangtze University, Wuhan, ChinaChina Railway Design Corporation, Tianjin, ChinaSchool of Geosciences, Yangtze University, Wuhan, ChinaSchool of Geosciences, Yangtze University, Wuhan, ChinaInner Mongolia Autonomous Region Surveying and Mapping Geographic Information Center, Hohhot, ChinaThe First Surveying and Mapping Institute of Hunan Province, Changsha, ChinaWater leakage poses a significant threat to the safe operation of tunnels. We utilized a mobile laser Scanner (MLS) to collect point cloud data under adverse tunnel conditions. A data mapping approach was employed to generate MLS point cloud intensity images. Tailored for multiscale point cloud intensity images, we devised a lightweight object detection network to identify areas affected by water leakage promptly. Integrating efficient receptive field expansion convolution into lightweight network models facilitated efficient feature extraction. Additionally, we designed an effective attention-inducing downsampling unit to construct a tunnel leakage detection model. This module comprehensively handles target features, enhances target context information, enlarges the receptive field, and establishes a unique information processing framework for detecting various multisize targets, achieving outstanding detection performance. Moreover, we developed a dynamic threshold adaptive loss function that automatically adjusts the loss function based on leakage detection performance to enhance the model's ability to detect challenging targets. Finally, we employed a twin attention-guided dynamic detection-head to improve detection performance. Experimental results demonstrate that our method effectively transforms the process from MLS point cloud data acquisition to high-precision target detection. The leakage detection network has achieved an optimal balance between efficiency and accuracy, surpassing comparative methods, thereby ensuring the secure operation of shield tunnels.https://ieeexplore.ieee.org/document/10836741/Deep learningmobile laser scanning (MLS)object detectionpoint cloud intensity images |
| spellingShingle | Zhenyu Liu Xianjun Gao Yuanwei Yang Lei Xu Shaoning Wang Ningsheng Chen Zhiwei Wang Yuan Kou EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning mobile laser scanning (MLS) object detection point cloud intensity images |
| title | EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images |
| title_full | EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images |
| title_fullStr | EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images |
| title_full_unstemmed | EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images |
| title_short | EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images |
| title_sort | edt net a lightweight tunnel water leakage detection network based on lidar point clouds intensity images |
| topic | Deep learning mobile laser scanning (MLS) object detection point cloud intensity images |
| url | https://ieeexplore.ieee.org/document/10836741/ |
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