Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning

Abstract

Ground penetrating radar (GPR) is widely used in subsurface utility mapping. It is a nondestructive tool that has gained popularity in supporting underground drilling projects such as horizontal directional drilling (HDD). Even with the benefits including equipment portability, low cost, and high versatility in locating underground objects, GPR has a drawback of the time spent and expertise needed in data interpretation. Recent researchers have shown success in utilizing machine learning (ML) algorithms in GPR images for the automatic detection of underground objects. However, due to the lack of availability of labeled GPR datasets, most of these algorithms used synthetic data. This study presents the application of the state-of-the-art You Only Look Once (YOLO) v5 algorithm to detect underground objects using GPR images. A GPR dataset was prepared by collecting GPR images in a laboratory setup. For this purpose, a commercially available 2GHz high-frequency GPR antenna was used, and a dataset was collected with images of metal and PVC pipes, air and water voids, and boulders. The YOLOv5 algorithm was trained with a dataset that successfully detected and classified underground objects to their respective classes.