High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data
Pipe jacking, a trenchless method for underground pipeline installation, minimizes surface disruption in urban environments but faces significant challenges in real-time quality control due to complex subsurface conditions. Existing approaches, primarily reliant on manual inspections or post-constru...
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025014288 |
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| author | Haofeng Yan Changyong Liu Zhuliang Chen Xincong Yang |
| author_facet | Haofeng Yan Changyong Liu Zhuliang Chen Xincong Yang |
| author_sort | Haofeng Yan |
| collection | DOAJ |
| description | Pipe jacking, a trenchless method for underground pipeline installation, minimizes surface disruption in urban environments but faces significant challenges in real-time quality control due to complex subsurface conditions. Existing approaches, primarily reliant on manual inspections or post-construction surveys, are labor-intensive, lack continuity, and often fail to detect defects or geometric deviations during construction. To address these issues, this study introduces a two-stage framework integrating computer vision and point cloud analysis for internal defect detection and geometric verification during construction.The methodology comprises two stages: Stage 1 involves a CCTV pipeline inspection robot equipped with a binocular-camera system and the YOLOv5 model for real-time detection and 3D localization of internal defects using stereo matching and SLAM. Stage 2 employs a GoSLAM handheld LiDAR scanner for continuous geometric verification through RANSAC fitting and ICP registration.Validated on a 2.58-km pipe jacking project in Harbin, China, the approach was capable of accurately identifying five common defect types—cracks, holes, leakage stains, sediment deposition, and spalling—achieving high overall precision and recall, with minor challenges in detecting less distinct defects like spalling. Geometric verification achieved a mean fitting error of 6.3 mm and an RMSE of 60.7 mm, with measured deviations—radial (16 mm), vertical (78 mm), horizontal (55 mm), and curvature (0.02°)—all well within permissible tolerances (159 mm, 112 mm, 0.5°). By enabling proactive monitoring and early defect correction, this dual-stage solution enhances the safety, efficiency, and reliability of pipe jacking construction, offering a scalable strategy for modern trenchless infrastructure projects. |
| format | Article |
| id | doaj-art-5b7e2a8959ed48a6ac7045b148fd6c8d |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-5b7e2a8959ed48a6ac7045b148fd6c8d2025-08-20T01:55:37ZengElsevierResults in Engineering2590-12302025-06-012610535810.1016/j.rineng.2025.105358High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud dataHaofeng Yan0Changyong Liu1Zhuliang Chen2Xincong Yang3School of Civil Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Civil and Environmental Engineering, Harbin Institute of Technology, Shen Zhen 518055, China; Guandong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shen Zhen 518055, China; Corresponding author.Pipe jacking, a trenchless method for underground pipeline installation, minimizes surface disruption in urban environments but faces significant challenges in real-time quality control due to complex subsurface conditions. Existing approaches, primarily reliant on manual inspections or post-construction surveys, are labor-intensive, lack continuity, and often fail to detect defects or geometric deviations during construction. To address these issues, this study introduces a two-stage framework integrating computer vision and point cloud analysis for internal defect detection and geometric verification during construction.The methodology comprises two stages: Stage 1 involves a CCTV pipeline inspection robot equipped with a binocular-camera system and the YOLOv5 model for real-time detection and 3D localization of internal defects using stereo matching and SLAM. Stage 2 employs a GoSLAM handheld LiDAR scanner for continuous geometric verification through RANSAC fitting and ICP registration.Validated on a 2.58-km pipe jacking project in Harbin, China, the approach was capable of accurately identifying five common defect types—cracks, holes, leakage stains, sediment deposition, and spalling—achieving high overall precision and recall, with minor challenges in detecting less distinct defects like spalling. Geometric verification achieved a mean fitting error of 6.3 mm and an RMSE of 60.7 mm, with measured deviations—radial (16 mm), vertical (78 mm), horizontal (55 mm), and curvature (0.02°)—all well within permissible tolerances (159 mm, 112 mm, 0.5°). By enabling proactive monitoring and early defect correction, this dual-stage solution enhances the safety, efficiency, and reliability of pipe jacking construction, offering a scalable strategy for modern trenchless infrastructure projects.http://www.sciencedirect.com/science/article/pii/S2590123025014288Pipe jacking projectDefect detectionGeometric verificationComputer visionPoint cloud analysis |
| spellingShingle | Haofeng Yan Changyong Liu Zhuliang Chen Xincong Yang High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data Results in Engineering Pipe jacking project Defect detection Geometric verification Computer vision Point cloud analysis |
| title | High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data |
| title_full | High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data |
| title_fullStr | High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data |
| title_full_unstemmed | High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data |
| title_short | High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data |
| title_sort | high precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data |
| topic | Pipe jacking project Defect detection Geometric verification Computer vision Point cloud analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025014288 |
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