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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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 14; H. 2; S. 251
Hauptverfasser:	Wang, Yuanzheng, Qin, Hui, Tang, Yu, Zhang, Donghao, Yang, Donghui, Qu, Chunxu, Geng, Tiesuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2022
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Qu, Chunxu Tang, Yu Yang, Donghui Geng, Tiesuo Wang, Yuanzheng Qin, Hui Zhang, Donghao
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SubjectTerms	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
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Title	RCE-GAN: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images
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