GPR B-scan Image Augmentation via GAN with Multiscale Discrimination Strategy

Being one of infrastructures for modern cities, urban roads suffer from potential subsurface disasters, which results in unexpected vital loss of economy and life. Thanks to the superiority of nondestructive detection with high efficiency, ground penetrating radar (GPR) has been widely applied to underground disaster detection; however, fenced by lack of labelled GPR data, automatic detection methods, especially the ones based on deep neural networks, have to be trained by synthetic GPR images and few real ones, which impedes the further application of deep neural networks in underground disaster detection. We proposed a network based on generative adversarial network with multiscale discrimination strategy to generate GPR b-scan images from the synthetic images, i.e., the forward GPR b-scan images generated by gprMax. Because sharing the same physical laws with real b-scan images and carrying label information of subsurface disaster, the simulated images could be utilized to augment training dataset for detection networks. The associated experiments show that the simulated images by our network are very similar to the real GPR b-scan images in appearance; meanwhile, the detection networks trained on the data set mixing the b-scan images simulated by our network and real ones could achieve better performance. Using our network as an augmentation method for GPR b-scan images contributes to the extensive application of deep neural networks in intelligent processing of GPR data.