Towards 3D Metric GPR Imaging Based on DNN Noise Removal and Dielectric Estimation

TL;DR

This paper presents a robotic system with a deep learning-based data analysis pipeline that automates 3D GPR imaging by improving data quality and subsurface dielectric estimation, enabling faster and more accurate underground scene visualization.

Contribution

It introduces a novel robotic GPR data collection method combined with a DNN-based analysis including noise removal and dielectric estimation for enhanced 3D imaging.

Findings

Improved GPR data quality through noise removal.

Faster processing speed in GPR imaging.

Enhanced 3D subsurface visualization accuracy.

Abstract

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility pipes) and reveal the underground scene. The two biggest challenges in GPR-based inspection are the GPR data collection and subsurface target imaging. To address these challenges, we propose a robotic solution that automates the GPR data collection process with a free motion pattern. It facilitates the 3D metric GPR imaging by tagging the pose information with GPR measurement in real-time. We also introduce a deep neural network (DNN) based GPR data analysis method which includes a noise removal segmentation module to clear the noise in GPR raw data and a DielectricNet to estimate the dielectric value of subsurface media in each GPR B-scan data. We use both the field and synthetic data to verify the proposed method. Experimental results demonstrate that our proposed method can achieve better performance and faster processing speed in GPR data collection and 3D GPR imaging than other methods.