Experimenal Images Identificaton with Simulation Images and Finetuning for Objects Identificaton of Ground Penetrating Radar Using Deep Learning

Abstract

In this study, to automatically detect underground objects from the ground penetrating radar (GPR) images by the deep neural network (DNN), we have generated GPR images for training the DNN using a fast finite-difference time-domain (FDTD) simulation with graphics processing units (GPUs). Also we have obtained characteristics of underground objects using the generated GPR images with a convolutional neural network (CNN) and finetuning using a modified VGG16 trained by the ImageNet. It is shown that the CNN and the VGG16 can identify four materials of experimental GPR images roughly 75 % and 80 % accuracy, respectively.