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
Online-Zugang:	Volltext
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