Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
Due to the rebars layer’s shielding effect on Ground Penetrating Radar (GPR) waves, the hyperbolic clutter generated by the rebars interferes with the echoes from void beneath them. The overlapping waveforms of both signals result in attenuation and distortion of the void signals, making it difficul...
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3728 |
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| author | Chuan Li Qibing Ma Yawei Wang Xi Yang Hao Liu Lulu Wang |
| author_facet | Chuan Li Qibing Ma Yawei Wang Xi Yang Hao Liu Lulu Wang |
| author_sort | Chuan Li |
| collection | DOAJ |
| description | Due to the rebars layer’s shielding effect on Ground Penetrating Radar (GPR) waves, the hyperbolic clutter generated by the rebars interferes with the echoes from void beneath them. The overlapping waveforms of both signals result in attenuation and distortion of the void signals, making it difficult to identify void defects under the rebar. This study proposes an unsupervised generative network model based on Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Through a shared latent space, mapping is achieved between two image domains, effectively eliminating the multiple reflection interference signals caused by the rebar while accurately reconstructing the void defects, generating GPR B-Scan images without rebar clutter. Additionally, the channel and spatial attention module (CSA) is implemented into the model to help the network to better focus on the essential information in GPR images. The proposed model was validated through ablation and comparative experiments using synthetic data. Finally, real GPR data from the Husa Tunnel were used to verify the model’s effectiveness in practical engineering applications. The results showed that this model is highly effective; it improves the visibility of void defects signals, thereby enhancing the interpretability of GPR data for tunnel lining inspections. |
| format | Article |
| id | doaj-art-44cc000a036045bb9adb2d463555b92d |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-44cc000a036045bb9adb2d463555b92d2025-08-20T02:17:00ZengMDPI AGApplied Sciences2076-34172025-03-01157372810.3390/app15073728Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GANChuan Li0Qibing Ma1Yawei Wang2Xi Yang3Hao Liu4Lulu Wang5Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650051, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650051, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650051, ChinaYunnan Aerospace Engineering Geophysical Detecting Co., Ltd., Kunming 650029, ChinaYunnan Aerospace Engineering Geophysical Detecting Co., Ltd., Kunming 650029, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650051, ChinaDue to the rebars layer’s shielding effect on Ground Penetrating Radar (GPR) waves, the hyperbolic clutter generated by the rebars interferes with the echoes from void beneath them. The overlapping waveforms of both signals result in attenuation and distortion of the void signals, making it difficult to identify void defects under the rebar. This study proposes an unsupervised generative network model based on Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Through a shared latent space, mapping is achieved between two image domains, effectively eliminating the multiple reflection interference signals caused by the rebar while accurately reconstructing the void defects, generating GPR B-Scan images without rebar clutter. Additionally, the channel and spatial attention module (CSA) is implemented into the model to help the network to better focus on the essential information in GPR images. The proposed model was validated through ablation and comparative experiments using synthetic data. Finally, real GPR data from the Husa Tunnel were used to verify the model’s effectiveness in practical engineering applications. The results showed that this model is highly effective; it improves the visibility of void defects signals, thereby enhancing the interpretability of GPR data for tunnel lining inspections.https://www.mdpi.com/2076-3417/15/7/3728GPRvoid defectsrebar suppressionVAEGAN |
| spellingShingle | Chuan Li Qibing Ma Yawei Wang Xi Yang Hao Liu Lulu Wang Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN Applied Sciences GPR void defects rebar suppression VAE GAN |
| title | Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN |
| title_full | Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN |
| title_fullStr | Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN |
| title_full_unstemmed | Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN |
| title_short | Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN |
| title_sort | suppression of multiple reflection interference signals in gpr images caused by rebar using vae gan |
| topic | GPR void defects rebar suppression VAE GAN |
| url | https://www.mdpi.com/2076-3417/15/7/3728 |
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