Zhao, M.; Fang, Z.; Ding, N.; Li, N.; Su, T.; Qian, H. Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR. Sensors2023, 23, 9761.
Zhao, M.; Fang, Z.; Ding, N.; Li, N.; Su, T.; Qian, H. Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR. Sensors 2023, 23, 9761.
Zhao, M.; Fang, Z.; Ding, N.; Li, N.; Su, T.; Qian, H. Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR. Sensors2023, 23, 9761.
Zhao, M.; Fang, Z.; Ding, N.; Li, N.; Su, T.; Qian, H. Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR. Sensors 2023, 23, 9761.
Abstract
Traditional underground pipeline inspection primarily relies on closed-circuit television (CCTV) systems, capturing visual data of deformations within sewer systems for manual assessment of their types and severity. However, these methods heavily rely on human expertise, which leads to subjective detection with limited accuracy. Moreover, they lack the capability for quantitative analysis of deformation extent, hindering accurate assessments and limiting overall inspection effectiveness. To address these challenges, this paper proposes a method for quantitatively detecting geometric deformations in underground pipe corridors using laser point cloud data. The approach, employing laser scanning with a 3D scanner, enables objective detection of internal pipeline deformations and quantitative assessment of blockage levels. In comparison to traditional CCTV-based methods, this approach offers advantages in objectivity and quantification, thereby improving detection reliability, accuracy, and overall efficiency.
Keywords
LiDAR; Point cloud; Quantitative detection; Pipeline deformation
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
