Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN
In urban road detection using Ground Penetrating Radar (GPR), challenges arise from complex and variable road structures and diversified detection environments. These unstable factors decrease GPR detection signal strength and cause signal shape distortion, negatively affecting detection accuracy. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/823 |
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| author | Xinxin Huang Jialin Liu Feng Yang Xu Qiao Liang Gao Tingyang Fu Jianshe Zhao |
| author_facet | Xinxin Huang Jialin Liu Feng Yang Xu Qiao Liang Gao Tingyang Fu Jianshe Zhao |
| author_sort | Xinxin Huang |
| collection | DOAJ |
| description | In urban road detection using Ground Penetrating Radar (GPR), challenges arise from complex and variable road structures and diversified detection environments. These unstable factors decrease GPR detection signal strength and cause signal shape distortion, negatively affecting detection accuracy. This reduces the interpretive accuracy of GPR images, impacting precise diagnosis of underground structures and hidden defects in urban roads. Therefore, understanding and overcoming these challenges is practically important for improving GPR performance and interpretive efficiency in urban road detection. To address these issues, this study proposes an innovative strategy using unsupervised learning for GPR image restoration. Specifically, it utilizes the Cycle-Consistent Adversarial Network (CycleGAN) with the Convolutional Block Attention Module (CBAM) generator and integrates the Multi-Scale Structural Similarity Index (MS-SSIM) loss function to enhance restoration quality. The method is trained and validated using field experimentally collected datasets with and without road surface interference, and the performance is evaluated through qualitative and quantitative analysis of restored GPR B-scan images. The experimental results show that the proposed method improves image restoration by 4.9% in SSIM, 39.15% in PSNR, and 76.88% in MAE, confirming its significant effect in GPR image restoration. |
| format | Article |
| id | doaj-art-238b4a6a490c4ceebb4d54be3aa237ad |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-238b4a6a490c4ceebb4d54be3aa237ad2025-08-20T02:59:15ZengMDPI AGRemote Sensing2072-42922025-02-0117582310.3390/rs17050823Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGANXinxin Huang0Jialin Liu1Feng Yang2Xu Qiao3Liang Gao4Tingyang Fu5Jianshe Zhao6School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, ChinaIn urban road detection using Ground Penetrating Radar (GPR), challenges arise from complex and variable road structures and diversified detection environments. These unstable factors decrease GPR detection signal strength and cause signal shape distortion, negatively affecting detection accuracy. This reduces the interpretive accuracy of GPR images, impacting precise diagnosis of underground structures and hidden defects in urban roads. Therefore, understanding and overcoming these challenges is practically important for improving GPR performance and interpretive efficiency in urban road detection. To address these issues, this study proposes an innovative strategy using unsupervised learning for GPR image restoration. Specifically, it utilizes the Cycle-Consistent Adversarial Network (CycleGAN) with the Convolutional Block Attention Module (CBAM) generator and integrates the Multi-Scale Structural Similarity Index (MS-SSIM) loss function to enhance restoration quality. The method is trained and validated using field experimentally collected datasets with and without road surface interference, and the performance is evaluated through qualitative and quantitative analysis of restored GPR B-scan images. The experimental results show that the proposed method improves image restoration by 4.9% in SSIM, 39.15% in PSNR, and 76.88% in MAE, confirming its significant effect in GPR image restoration.https://www.mdpi.com/2072-4292/17/5/823urban roadssurface interferenceGround-Penetrating RadarCycleGANCBAMimage restoration |
| spellingShingle | Xinxin Huang Jialin Liu Feng Yang Xu Qiao Liang Gao Tingyang Fu Jianshe Zhao Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN Remote Sensing urban roads surface interference Ground-Penetrating Radar CycleGAN CBAM image restoration |
| title | Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN |
| title_full | Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN |
| title_fullStr | Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN |
| title_full_unstemmed | Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN |
| title_short | Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN |
| title_sort | study on gpr image restoration for urban complex road surfaces using an improved cyclegan |
| topic | urban roads surface interference Ground-Penetrating Radar CycleGAN CBAM image restoration |
| url | https://www.mdpi.com/2072-4292/17/5/823 |
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