Object Detection And Recognition In GPR Images Using AI Techniques

Abstract

In this paper, we adapt AI techniques, especially YOLOv5 algorithm, for the detection and classification of underground buried objects in B-scan ground penetrating radar (GPR) images. We used gprMax toolbox to prepare the dataset and we considered seven models: voids, tunnels, groundwater, rebar and subsurface concrete. Data augmentation was applied to enlarge the Dataset. The recognition results were promising and the algorithm was tested on real GPR images taken from different references. This study shows that YOLOv5 can detect and classify underground targets, which makes it a promising methodology for practical investment to analyze this kind of specific GPR data even with few training samples.