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. 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.