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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Branco, D (Autor) Outra; Coutinho, R (Autor) Outra; Armando Jorge Sousa (Autor) FEUP; Filipe Neves Santos (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics, ICINCO 2024, Porto, Portugal, November 18-20, 2024, Volume 1.

Páginas: 692-699

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-GGF

DOI: 10.5220/0013027500003822

Abstract (EN): Ground Penetrating Radar (GPR) is a geophysical imaging technique used for the characterization of a sub surface¿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 pro cessing 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 hy perbola 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. © 2024 by SCITEPRESS¿ Science and Technology Publications, Lda.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7