GPR Image Enhancement and Object Detection-Based Identification for Roadbed Subsurface Defect

Roadbed disease detection is essential for maintaining road functionality. Ground penetrating radar (GPR) enables non-destructive detection without drilling. However, current identification often relies on manual inspection, which requires extensive experience, suffers from low efficiency, and is highly subjective. As the results are presented as radar images, image processing methods can be applied for fast and objective identification. Deep learning-based approaches now offer a robust solution for automated roadbed disease detection. This study proposes an enhanced Faster Region-based Convolutional Neural Networks (R-CNN) framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation (2D-DFT) for frequency-domain feature fusion. A dedicated GPR image dataset comprising 1650 annotated images was constructed and augmented to 6600 images via median filtering, histogram equalization, and binarization. The proposed model segments defect regions, applies binary masking, and fuses frequency-domain features to improve small-target detection under noisy backgrounds. Experimental results show that the improved Faster R-CNN achieves a mean Average Precision (mAP) of 0.92, representing a 0.22 increase over the baseline. Precision improved by 26% while recall remained stable at 87%. The model was further validated on real urban road data, demonstrating robust detection capability even under interference. These findings highlight the potential of combining GPR with deep learning for efficient, non-destructive roadbed health monitoring.