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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Vydané v:	Remote sensing (Basel, Switzerland) Ročník 14; číslo 2; s. 251
Hlavní autori:	Wang, Yuanzheng, Qin, Hui, Tang, Yu, Zhang, Donghao, Yang, Donghui, Qu, Chunxu, Geng, Tiesuo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.01.2022
Predmet:	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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