Authors:
Duarte Branco
1
;
2
;
Rui Coutinho
1
;
2
;
Armando Sousa
1
;
2
and
Filipe Santos
2
Affiliations:
1
Faculty of Engineering (FEUP), University of Porto, Rua Dr. Roberto Frias, Porto, Portugal
;
2
INESC Technology and Science (INESC TEC), Rua Dr. Roberto Frias, Porto, Portugal
Keyword(s):
Ground Penetrating Radar, GPR, Object Detection, Subsurface Characterization, YOLO, Hyperbola Model.
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.
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