RCE-GAN: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.

Automated summary

In this work, a method for improving the identification of tunnel lining voids using a rebar clutter elimination network (RCE-GAN) based on generative adversarial networks is proposed. The network utilizes cycle-consistency loss to learn high-level features between unpaired GPR images and includes attention module and dilation center part to enhance performance. Validation on synthetic and real-world GPR images demonstrates the potential of the proposed method for its lower demand on the training dataset and improvement in the identification of tunnel lining voids.