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

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 imagi...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-05
Main Authors: Feng, Jinglun, Yang, Liang, Xiao, Jizhong
Format: Article
Language:English
Subjects:
Algorithms
Background noise
Dielectric properties
Ground penetrating radar
Inspection
Noise
Nondestructive testing
Object recognition
Rebar
Reconstruction
Reinforcing steels
Segmentation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422