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 me...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 14; no. 2; p. 251
Main Authors: Wang, Yuanzheng, Qin, Hui, Tang, Yu, Zhang, Donghao, Yang, Donghui, Qu, Chunxu, Geng, Tiesuo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2022
Subjects:
Accuracy
Clutter
Concrete
data collection
Deep learning
design
Discriminators
Efficiency
Electromagnetic radiation
Electromagnetic shielding
Generative adversarial networks
generative adversarial networks (GAN)
Generators
Ground penetrating radar
ground penetrating radar (GPR)
image analysis
Inspection
learning
Methods
Noise
physical models
Radar imaging
Rebar
rebar clutter elimination
Reinforced concrete
Remote sensing
Signal processing
Signatures
Tunnel linings
tunnel void
unsupervised learning
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary: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.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs14020251