Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data
Abstract Recent advancements in automated tunnel defect detection have utilized high-resolution cameras and mobile laser scanners. However, the inability of cameras to accurately capture 3D spatial coordinates complicates tasks such as 3D visualization, while the relatively low resolution of laser s...
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| Format: | Article |
| Language: | English |
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Springer Nature
2025-05-01
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| Series: | AI in Civil Engineering |
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| Online Access: | https://doi.org/10.1007/s43503-025-00054-w |
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| author | Hongwei Huang Shuyi Liu Mingliang Zhou Hua Shao Qingtong Li Phromphat Thansirichaisree |
| author_facet | Hongwei Huang Shuyi Liu Mingliang Zhou Hua Shao Qingtong Li Phromphat Thansirichaisree |
| author_sort | Hongwei Huang |
| collection | DOAJ |
| description | Abstract Recent advancements in automated tunnel defect detection have utilized high-resolution cameras and mobile laser scanners. However, the inability of cameras to accurately capture 3D spatial coordinates complicates tasks such as 3D visualization, while the relatively low resolution of laser scanners makes it difficult to detect small defects such as microcracks. In this paper, a comprehensive inspection method is proposed to address these limitations by integrating multi-defect detection, 3D coordinate acquisition, and visualization. The inspection process involves the capture of both image data and point cloud data of tunnel linings using the newly developed inspection cart (MTI-300). The proposed fusion approach combines image and point cloud data, leveraging the enhanced YOLOv8-seg instance segmentation model for defect identification. The scale-invariant feature transform (SIFT) algorithm is used to match local defect regions in the image data with the corresponding point cloud data, enabling the extraction of 3D coordinates and the integration of defect pixels with the point cloud information. Subsequently, a lightweight 3D reconstruction model is developed to visualize the entire tunnel and its defects using the fused data. The performance of the proposed method is validated and substantiated through a field experiment on Metro Line 8 in Qingdao, China. |
| format | Article |
| id | doaj-art-5b983046ff7f420fa553b36ecf0f7f0e |
| institution | OA Journals |
| issn | 2097-0943 2730-5392 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | AI in Civil Engineering |
| spelling | doaj-art-5b983046ff7f420fa553b36ecf0f7f0e2025-08-20T01:49:39ZengSpringer NatureAI in Civil Engineering2097-09432730-53922025-05-014112310.1007/s43503-025-00054-wAutomated 3D defect inspection in shield tunnel linings through integration of image and point cloud dataHongwei Huang0Shuyi Liu1Mingliang Zhou2Hua Shao3Qingtong Li4Phromphat Thansirichaisree5Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering, Tongji UniversityKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering, Tongji UniversityKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering, Tongji UniversityShanghai Metro Maintenance Co., Ltd.Shanghai Shentong Metro Group Co., Ltd.Thammasat School of Engineering, Thammasat University RangsitAbstract Recent advancements in automated tunnel defect detection have utilized high-resolution cameras and mobile laser scanners. However, the inability of cameras to accurately capture 3D spatial coordinates complicates tasks such as 3D visualization, while the relatively low resolution of laser scanners makes it difficult to detect small defects such as microcracks. In this paper, a comprehensive inspection method is proposed to address these limitations by integrating multi-defect detection, 3D coordinate acquisition, and visualization. The inspection process involves the capture of both image data and point cloud data of tunnel linings using the newly developed inspection cart (MTI-300). The proposed fusion approach combines image and point cloud data, leveraging the enhanced YOLOv8-seg instance segmentation model for defect identification. The scale-invariant feature transform (SIFT) algorithm is used to match local defect regions in the image data with the corresponding point cloud data, enabling the extraction of 3D coordinates and the integration of defect pixels with the point cloud information. Subsequently, a lightweight 3D reconstruction model is developed to visualize the entire tunnel and its defects using the fused data. The performance of the proposed method is validated and substantiated through a field experiment on Metro Line 8 in Qingdao, China.https://doi.org/10.1007/s43503-025-00054-wTunnel defectAutomated inspection3D reconstructionImage segmentationPoint cloud |
| spellingShingle | Hongwei Huang Shuyi Liu Mingliang Zhou Hua Shao Qingtong Li Phromphat Thansirichaisree Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data AI in Civil Engineering Tunnel defect Automated inspection 3D reconstruction Image segmentation Point cloud |
| title | Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data |
| title_full | Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data |
| title_fullStr | Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data |
| title_full_unstemmed | Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data |
| title_short | Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data |
| title_sort | automated 3d defect inspection in shield tunnel linings through integration of image and point cloud data |
| topic | Tunnel defect Automated inspection 3D reconstruction Image segmentation Point cloud |
| url | https://doi.org/10.1007/s43503-025-00054-w |
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