HiFE-GAN: Semantic Synthesis of GPR B-Scan Images by High-Frequency Encoding GAN

As a common infrastructure, roads, subject to heavy traffic in modern cities, are progressively becoming dangerous since they are under the threat of subsurface disasters caused by geological structure changes, groundwater overutilization, etc. Benefiting from the superiority in nondestructivity as well as efficiency, ground-penetrating radar (GPR) has been widely applied in the detection of underground disasters; however, manual interpretation of GPR images is labor-intensive and the intelligentization of GPR is impeded by the lack of labeled images. Considering that the texture pattern of echo waves in real b-scan images is the key clue for engineers to classify underground disasters, we proposed a semantic synthesis network with a high-frequency structure, i.e., HiFE-GAN, to augment GPR b-scan images for training detection networks, by which one can customize the position, scale, and size of underground disasters in the simulated b-scan images. The provided experiments show that our network can generate b-scan images more similar to the real ones compared with several current generation networks; meanwhile, mixing the simulated b-scan images synthesized by HiFE-GAN with the real ones to train detection networks can dramatically improve the detection performance for underground disasters.