Estimation of geometric information of internal defects in concrete from radar images using CycleGAN

Abstract

The authors have developed a method for estimating and visualizing defect geometry information inside concrete using pix2pix from radar images. Specifically, first, a specimen with artificial defects placed at different positions, sizes, and angles is fabricated, and radar images are acquired. The acquired data is trained by pix2pix to estimate and visualize the cross-sectional image containing the defect from the radar image. Although this method has a certain level of estimation performance, it has a problem that the preparation of training data is very costly. In this study, we attempted to reduce the cost of training data generation by using CycleGAN, which does not require training data pairs. The results showed that the proposed method significantly reduces the data generation cost compared to the pix2pix method, and has the same or better estimation performance based on both qualitative and quantitative evaluations.