Abstract
We have developed an identification method of an underground object form a ground penetrating radar (GPR) image by the deep neural network. In this study, in order to automatically detect an underground object from the GPR image by the DNN, we have generated several hundred thousand GPR images for training the DNN using a fast finite-difference-time-domain (FDTD) simulation with graphics processing units (GPUs). Furthermore, Characteristics of underground objects are extracted and learned from generated GPR images by a 9-layers convolutional neural network (CNN). It is shown that the CNN can identify six materials with roughly 80% accuracy in inhomogeneous underground media.
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