[1F4-GS-10] AI application: vision analysis

[1F4-GS-10-01] Visualization of Cracks inside Concrete using Quasi-3D Radar Images and GAN

〇Yoshihito YAMAMOTO¹, Kazutaka Mitsutani², Yasushi Kanazawa³, Kaito Tokushige³, Jun Sonoda⁴, Tomoyuki Kimoto⁵ (1. Hosei University, 2. Nagoya University, 3. Toyohashi University of Technology, 4. National Institute of Technology, Sendai College, 5. National Institute of Technology, Oita College)

[[Online]]

Keywords:Visualization, Internal crack, GAN, GPR, Quasi-3D

This study aims to improve the accuracy of the proposed method to visualize cracks in concrete using pix2pix from radar images by adding quasi-3D information. In a previous study, ground-penetrating radar tests were conducted on concrete specimens in which artificial defects were embedded in varying positions, angles, and sizes, and cross-sectional images including geometric information of the defects and corresponding radar image pairs were obtained. The dataset was applied to pix2pix to construct a model that outputs the cross-sectional image from the radar image. In this study, we further attempt to apply a method to output the cross-section image by applying two radar images, in which the radar scanning positions are shifted by about the maximum size of coarse aggregate in concrete. The proposed method can visualize defects with high accuracy, even in cases where the reflection intensity of electromagnetic waves becomes small, and the accuracy of conventional methods decreases.