Subsurface Voids Detection from Limited Ground Penetrating Radar Data Using Generative Adversarial Network and YOLOV5
Guanyi Chen, Xu Bai, Gang Wang, Long Wang, Xuerong Luo, Mingjie Ji, Pengfei Feng, Yang Zhang
Abstract
Recently, preventing road collapse caused by subsurface voids became an urgent problem that needs to be solved in the urban road safety area. While the most study in the field of subsurface object detection by deep learning method has only focused on the objects that can be acquired GPR B-scan data easily. This paper aims to realize the subsurface voids detection under the condition that lacks verified GPR B-Scan data. In this paper, a GPR B-Scan image augmentation method by SinGAN is proposed and the YOLOv5 object detection algorithm is applied correspondingly to detect subsurface voids from both real collected and generated GPR B-Scan data. The detection results show that the proposed technique realized subsurface voids detection in limited verified GPR B-scan data samples and become an inspiration for similar tasks that lacking training samples.