Subsurface Metallic Object Detection Using GPR Data and YOLOv8 Based Image Segmentation

Duarte Branco, Duarte Branco, Rui Coutinho, Rui Coutinho, Armando Sousa, Armando Sousa, Filipe Santos

2024

Abstract

Ground Penetrating Radar (GPR) is a geophysical imaging technique used for the characterization of a subsurface’s electromagnetic properties, allowing for the detection of buried objects. The characterization of an object’s parameters, such as position, depth and radius, is possible by identifying the distinct hyperbolic signature of objects in GPR B-scans. This paper proposes an automated system to detect and characterize the presence of buried objects through the analysis of GPR data, using GPR and computer vision data processing techniques and YOLO segmentation models. A multi-channel encoding strategy was explored when training the models. This consisted of training the models with images where complementing data processing techniques were stored in each image RGB channel, with the aim of maximizing the information. The hyperbola segmentation masks predicted by the trained neural network were related to the mathematical model of the GPR hyperbola, using constrained least squares. The results show that YOLO models trained with multi-channel encoding provide more accurate models. Parameter estimation proved accurate for the object’s position and depth, however, radius estimation proved inaccurate for objects with relatively small radii.

Paper Citation

in Harvard Style

Branco D., Coutinho R., Sousa A. and Santos F. (2024). Subsurface Metallic Object Detection Using GPR Data and YOLOv8 Based Image Segmentation. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 692-699. DOI: 10.5220/0013027500003822

in Bibtex Style

@conference{icinco24,
author={Duarte Branco and Rui Coutinho and Armando Sousa and Filipe Santos},
title={Subsurface Metallic Object Detection Using GPR Data and YOLOv8 Based Image Segmentation},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={692-699},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013027500003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Subsurface Metallic Object Detection Using GPR Data and YOLOv8 Based Image Segmentation
SN - 978-989-758-717-7
AU - Branco D.
AU - Coutinho R.
AU - Sousa A.
AU - Santos F.
PY - 2024
SP - 692
EP - 699
DO - 10.5220/0013027500003822
PB - SciTePress