1. Introduction
As an important guarantee for the construction of urban civilization and healthy human life, urban drainage systems can isolate sewage and clean water, thereby improving the quality of human life [
1]. With economic growth and the continuous expansion of urban scale, the total length of drainage pipelines in China continues to grow. Consequently, the aging problem of the pipeline system is becoming more and more serious. Road collapse and environmental pollution caused by pipeline aging have caused considerable social impact [
2]. Therefore, municipal departments need to spend a lot of money and resources on the maintenance of sewage pipelines [
3]. In order to reduce the serious consequences caused by drainage pipe defects, it is very important to detect and evaluate the defects as soon as possible [
4]. Currently, the main methods of pipeline inspection include sonar inspection [
5], pipeline periscope inspection [
6], ground-penetrating radar systems [
7], pipeline closed-circuit television inspection [
4], etc. Sonar detection can detect mud and foreign matter in pipelines well, but its ability to detect the structural defects of pipelines is poor. Pipeline periscope detection has the advantages of low detection cost and high detection speed. For short drainage pipelines, clear impact data can be obtained, but the software cannot be run in pipelines with high water levels. Ground-penetrating radar (GPR) images can better represent the subsidence or collapse around the pipeline, but the detection effect of this method is poor for other pipeline diseases. Closed-circuit television inspection of pipelines has been widely used in domestic and foreign pipeline detection [
5]. Compared with traditional detection methods, closed-circuit television (CCTV) has a higher intelligence level and can provide more concise and obvious image results compared with laser detection and radar tests. The above four methods have the advantages of good accuracy and little influence from testing environments. However, due to the limitations of the instrument itself, these four methods can only undertake qualitative analysis for pipeline damage, not quantitative assessments.
In recent years, the use of 3D information for structural health analysis has become a new trend. 2D images can be used for some structural damage detection, such as crack detection. 3D information provides solutions for other types of detection; for example, the depth and volume of the damaged part can be calculated according to the 3D information of the damaged part. Some authors used Structure-from-Motion (SfM) [
8] to fuse two-dimensional images and some scale parameters to establish a 3D virtual model. Mahami et al. [
9] used SfM combined with an MVS algorithm to detect the target so as to realize automatic detection of the whole project’s progress. Golparvar-fard et al. [
10] verified that SfM can complete remote assessment of infrastructure before and after disasters. Torok et al. [
11] developed a new crack recognition algorithm based on the SfM method, which used robots to collect post-disaster information in order to complete 3D surface damage detection and analysis. Nowak et al. [
12] used TLS to complete the overall scanning of a historic building structure and obtained a nearly complete architectural geometry.
Other authors have used 3D laser scanning equipment to quickly capture infrastructure’s structural information to build 3D point cloud models. Youn et al. [
13] used 3D scanners and Revit to build a platform containing various pieces of historical information that can be used for digital twin to record wood deformation and crack information. Zeibak-shini et al. [
14] used laser scanning to compare a generated damage BIM model with a built BIM model to complete a preliminary estimation of the damaged parts of a reinforced concrete frame. Wang et al. [
15] used a 3D point cloud as the quality detection of assembled building wall panels, which can quickly and efficiently classify and segment the wall panels that need to be corrected. Turkan et al. [
16] completed concrete crack detection by combining a wavelet neural network with 3D ground laser scanning data. Liu et al. [
17] used a 3D camera combined with a classical edge detection algorithm and fuzzy logic detection algorithm to perform clear edge detection on installed panels.
Another way to obtain 3D point clouds is to use a depth camera (RGB-D) that incorporates depth information. Some authors put forward the method of obtaining 3D point cloud data from cheap depth cameras to quantify the damage of road potholes [
18]. All of these methods quantify the damage by fixing the distance and angle between the camera and the measured object, which limits their use in other scenes and is not reliable for automatic detection.
3D background removal is an important problem in surface damage research. At present, 3D point cloud segmentation methods mainly include the method based on regional growth, the method based on cluster features, and the method based on model fitting. The region growth method proposed by Besl and Jain (1988) [
19] is mainly divided into two stages: firstly, seed points are selected; secondly, adjacent points are merged according to certain standards (normal vectors within a certain threshold range). Tovari and Pfeifer [
20] proposed the point-based region growth algorithm which combined adjacent points into the same set according to their normal vectors and distance thresholds. The method based on clustering features divides the data set into different classes according to certain standards (distance or normal vectors). Biosca and Lerma [
21] developed a fuzzy clustering segmentation method to merge neighboring points whose distance is less than the set threshold into the nearest cluster. Methods based on model fitting mainly have two algorithms, namely, the Hough Transform (HT) algorithm proposed by Ballard et al. [
22] and the random sample consistency algorithm (RANSAC) proposed by Fischler et al. [
23]. The HT algorithm uses a voting method to identify parameterized models. Rabbani et al. [
24] completed automatic detection of cylinder models in point clouds based on the HT algorithm. Although the HT algorithm can segment 3D point clouds well, it has problems such as the consumption of a lot of memory and computing time [
25]. The RANSAC algorithm randomly selects data points at first, estimates model parameters according to the selected data points, then puts the remaining points into the model, and, finally, selects the model with the maximum number of points as the best model. The RANSAC algorithm, firstly, randomly selects data points, estimates model parameters according to the selected data points, then puts the remaining points into the model, and, finally, selects the model with the maximum number of points as the best model. Chen et al. [
26] segmented polyhedral roofs using an improved RANSAC algorithm and classified primitive elements using a region growth algorithm.
Surface reconstruction is an important method for obtaining dimension data from damage data after segmentation. At present, the common surface reconstruction methods are polygonal mesh reconstruction, parametric surface reconstruction, and implicit surface reconstruction. The most widely used method is polygon mesh reconstruction. The polygon mesh reconstruction method uses a simple mathematical model to describe object surfaces with points, lines, and planes. Delaunay triangulation [
27] is a classical method of polygonal mesh reconstruction that was first proposed by the Russian mathematician Boris Delaunay. Delaunay triangle networks have two characteristics; each Delaunay triangle’s outer circle does not contain other points in the plane domain, namely, the empty outer circle characteristic; and, after mutual exchange, the minimum angle of the six interior angles will not increase, namely, the minimum angle maximization characteristic. Delaunay triangulation has the advantages of regularity and optimality, and many researchers have developed polygon mesh reconstruction methods based on Delaunay triangulation. Boissonnat and Cazals [
28] used natural neighborhood interpolation to construct smooth surfaces based on Delaunay triangulation and Voronoi diagrams. Amenta et al. [
29] reconstructed surfaces from disordered point clouds based on Voronoi diagrams. Recently, Bernardini et al. [
30] proposed a new polygonal mesh reconstruction method, the ball-pivoting algorithm (BPA). The basic principle of the algorithm is to set a ball with radius ρ, which contains only three data points forming a triangle. The ball continues to rotate around the surface of the point cloud and generates the next triangle until all the data in the data set are calculated. The BPA method has the advantages of strong robustness and high efficiency.
In order to quantify the damage volume of underground pipelines under the interference of a complex environment, we propose a quantitative method of assessing the damage volume of underground drainage pipelines integrating 3D point cloud surface segmentation and reconstruction. On the basis of damage segmentation, damage reconstruction and surface reconstruction were carried out with the help of pipeline surface information, and the algorithm had strong portability. The method mainly consisted of four parts: (1) conversion from 2D depth frames to 3D point gathering was completed according to the conversion relationship between the internal coordinates of the acquisition instrument and the world coordinates; (2) the data set was preprocessed by integrating voxel sampling and a Gaussian filter; (3) the parameters of the surface model were estimated by the random sampling consensus algorithm, and the point cloud of the pipeline surface was removed; (4) after the damage data were reconstructed with the surface point cloud, the BPA algorithm was used to complete the surface reconstruction in order to obtain the real damage volume. The rest of this paper includes
Section 2: Concrete Pipeline Damage Volume Quantitative Detection Framework,
Section 3: Experiments,
Section 4: Performance Analysis,
Section 5: Discussion,
Section 6: Conclusion.
5. Discussion
This paper presents a method using an inexpensive depth sensor as a pothole scanner. Compared with the existing literature on measuring pits using Kinect, the following are the main contributions of this paper.
Different materials and shapes were used as reference materials to evaluate the performance of sensors.
Joubert et al. [
33] used RANSAC for surface fitting and then manually selected damage locations for size calculation. In this paper, surface reconstruction was carried out based on segmentation and combined with drainage pipe surface information to improve damage detection efficiency.
Compared with Kamal et al. [
18], who used average-filtering depth images to calculate volume through a method using pixel point integration for depth distance, this paper used improved RANSAC surfaces to segment damaged surface point clouds and the Alpha Shapes algorithm to detect the external contour of point clouds and reconstruct the surface to finally complete volume calculation.
For the methods of collecting and processing concrete pipeline damage data, this paper adopted fixed camera shooting distance and angle to collect concrete pipeline damage measurements and combined the RANSAC segmentation algorithm and the Alpha surface reconstruction algorithm to complete static data processing. Our future research direction is to develop pipeline robots equipped with depth cameras for dynamic acquisition of damage data and real-time damage detection and processing, combined with a deep-learning method in view of the complex state of active concrete pipeline.
6. Conclusions
With the continuous improvement of vision sensors, it is possible to realize volume quantization in 3D point clouds. Concrete drainage pipe breakage is a common structural damage. In order to evaluate this structural damage accurately, the broken volume should be quantified. In this paper, we proposed a 3D point cloud volume quantification method for concrete drainage pipe damage integrating surface segmentation and reconstruction. We tested the accuracy of the depth camera Microsoft Azure Kinect DK with an RGB-D sensor in the quantification of concrete pipe damage volumes. The equipment has the advantages of high precision, real-time data transmission, and a low price and can be used to detect and quantify the damage volume of concrete pipeline. Meanwhile, the method provides ideas for other depth cameras to quantify the volume of damage in concrete pipeline. The experimental results show that this method has great potential in the measurement of the damage volumes of drainage pipeline and can provide support for system decisions and quantitative repair materials for drainage pipeline.
Although this study has demonstrated the potential of automatically quantifying damage volumes in drainage lines, there are still some limitations. For example, in our study, only drainage pipes with a single diameter were studied. Therefore, it is necessary to further study the damage of drainage pipes with different diameters. In addition, the underground drainage pipeline service environment is complex; sewage, uneven light, fog, and blockage will affect data collection. The automatic segmentation and adaptive reconstruction of drainage pipe surface point clouds is a challenging task. In this regard, the development of a calculation system that can automatically identify, segment, and quantify drainage pipeline in the complex working environment is our future research direction.