A novel 3D GPR image arrangement for deep learning-based underground object classification

Abstract

ABSTRACT Ground-penetrating radar (GPR) is widely used for detecting buried underground object. Deep learning technique is recently being adopted into this field thanks to its powerful image classification capacity. However, it uses only GPR B-scan images, although multichannel GPR device can provide more informative three-dimensional (3D) data for underground object. In this study, a novel deep learning-based underground object classification method is proposed by using two-dimensional (2D) grid image which consists of several B-scan and C-scan images. Spatial information of an underground object can be well represented in the 2D grid image. The 2D grid images are then used to train deep convolutional neural networks. The proposed method is experimentally validated by field data collected from urban roads in Seoul, South Korea. The performance is also compared to a conventional method which uses only B-scan images. The proposed method successfully classifies cavity, pipe, manhole and subsoils background having very small false-positive errors.