Study of Damage Quantification of Concrete Drainage Pipes Based on Point Cloud Segmentation and Reconstruction

Abstract

The urban drainage system is an important part of the urban water cycle. However, with the aging of drainage pipelines and other external reasons, damages such as cracks, corrosion, and deformation of underground pipelines can cause serious consequences such as urban waterlogging and road collapse. At present, the detection of underground drainage pipelines mostly focuses on the qualitative identification of pipeline damage, and it is impossible to quantitatively analyze pipeline damage. Therefore, a method to quantify the damage volume of concrete pipes that combines surface segmentation and reconstruction is proposed. An RGB-D sensor is used to collect the damage information of the drainage pipeline, and the collected depth frame is registered to generate the pipeline’s surface point cloud. Voxel sampling and Gaussian filtering are used to improve data processing efficiency and reduce noise, respectively, and the RANSAC algorithm is used to remove the pipeline’s surface information. The ball-pivoting algorithm is used to reconstruct the surface of the segmented damage data and pipe’s surface information, and finally to obtain the damage volume. In order to evaluate, we conducted our research on real-world materials. The measurement results show that the method proposed in this paper measures an average relative error of 7.17% for the external damage volume of concrete pipes and an average relative error of 5.22% for the internal damage measurements of concrete pipes.

Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR

“…In the work by Meng et al [ 16 ], hybrid object detection and tracking for cooperative perception are achieved using 3D LiDAR. Pang et al [ 17 ] utilized laser radar for point cloud segmentation and reconstruction, achieving reconstruction and error assessment specifically targeted at defects. Nevertheless, prevailing methods exhibit a limitation in the capability to intuitively conduct quantitative analysis of internal geometric deformations.…”

Current Situation of Drainage Pipe Network in China and its Detection Technology: a Brief Review

“…In addition, a large number of inspection technologies have been widely used for the inspection of China's damaged pipelines such as closed circuit television detection technology (CCTV), pipe quick view inspection technology (QV), sonar detection technology, ground penetrating radar (GPR) and the traditional detection methods (manual detection method, observation method, reflector method, mud bucket method, etc) [2,11,12]. Previous related research mainly focused on the application of these techniques and characterized their applicable conditions in some specific engineering cases [12][13][14][15].…”

An efficient method for producing deep learning point cloud datasets based on BIM 3D model and computer simulation

“…In recent years, 3D deep learning based on point cloud data has become one of the most important research hotspots in the field of computer vision, and the core research directions include classification of point cloud data [1][2], semantic segmentation [3][4], instance segmentation [5][6], and target detection [7][8], etc. The research results have been applied to robotics and autonomous driving, industrial vision, 3D reconstruction [9][10][11][12], etc., and also have greater potential in other fields such as engineering construction, urban operation, and structure detection [13][14]. However, such data-driven deep learning methods all require a large amount of data with annotations for model training, and unlike the simple and easy availability of 2D images, point clouds are generally obtained by 3D laser scanners, which have a large amount of data, high acquisition cost, more tedious data processing and annotation, and many cases cannot obtain "Ground Trouth ".…”