High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data

• Real-time detection of pipe defects using a binocular-camera system with YOLOv5 and stereo matching. • Continuous geometric verification of pipeline trajectory via handheld LiDAR-based point cloud analysis. • Proactive quality control enabled by integrated monitoring of defects and alignment in pipe jacking. • Validated framework enhances safety and efficiency in a 2.58-km pipe jacking project in Harbin, China. 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.

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Publication Details

Journal
Results in Engineering
Published
2025-05-16
DOI
https://doi.org/10.1016/j.rineng.2025.105358
Citations
3
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
2.86

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article

High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data

Xincong Yang, Zhuliang Chen, Haofeng Yan, Changyong Liu
3 citations
Results in Engineering
Infrastructure Maintenance and Monitoring
2.86
article

High-Precision defect detection and geometric verification in pipe jacking projects using computer vision and point cloud data

Xincong Yang, Zhuliang Chen, Haofeng Yan, Changyong Liu
article en
3 citations

Abstract

• Real-time detection of pipe defects using a binocular-camera system with YOLOv5 and stereo matching. • Continuous geometric verification of pipeline trajectory via handheld LiDAR-based point cloud analysis. • Proactive quality control enabled by integrated monitoring of defects and alignment in pipe jacking. • Validated framework enhances safety and efficiency in a 2.58-km pipe jacking project in Harbin, China. 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.

Results in EngineeringVol. 26
Harbin Institute of Technology (CN), Intelligent Health (United Kingdom) (GB)
Key Technologies Research and Development Program, Shenzhen Technology Development Program, Shenzhen Science and Technology Innovation Program
Industry, innovation and infrastructure
Openalex Percentile: Top 10%
Infrastructure Maintenance and Monitoring
2.86
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