Improved 3-D Representation of GPR Pipelines B-Scan Sequences Using a Neural Network Framework
Tianjia Xu, Yuan Da, Wang Ping, Gexing Yang, Boyang Li, Wenli Sun
Abstract
Ground Penetrating Radar (GPR) is an efficient non-destructive testing tool used for detecting and locating buried pipelines. It helps to avoid interference with existing pipelines and determine optimal layouts, resulting in time and cost savings. However, applications in this domain often require the joint observation of sequential images, mapping from 2D B-scans to 3D spatial structures. Complex underground environments, equipment orientations, noise, and data deviations can introduce visual distortions, blurriness, and unclear structures in the collected data. Therefore, there is a need for a method to rapidly comprehend and visually analyze the true conditions of underground pipeline structures. GPR data is typically collected and stored in the form of two-dimensional B-scan sequences. In this paper, we propose a network framework that takes sparse original 2D B-scan sequences as input and outputs a dense three-dimensional target model. We first employ a Transformer model to interpolate the B-scan slice collection, generating dense 3D B-scan volume data. Subsequently, a from-coarse-to-fine back-projection strategy, based on the Transformer model, constructs a 3D volume data inversion-mapping model to transform 2D hyperbolic waves into three-dimensional pipeline information. Additionally, we apply a clutter removal mechanism based on Conditional Generative Adversarial Networks (CGAN) to Declutter and enhance the visualization of the desired hyperbolic wave structures, improving the accuracy of 3D visual imaging. Experimental results demonstrate that the proposed method is better suited for structural analysis of GPR pipeline data, particularly in complex real-world data experiments, affirming the effectiveness and practicality of the approach presented in this paper.