Research on Void and Defect Detection in Ground-Penetrating Radar Images Using Deep Learning Techniques

Ground-Penetrating Radar is a non-destructive tool for detecting subsurface structures. However, traditional image interpretation is often limited by medium complexity and noise. To improve detection efficiency and accuracy, this study combines deep learning techniques to develop an automatic embankment cavity identification system based on the YOLOv10 model. The research first constructs a training dataset containing GPR images of embankment cavities and expands the dataset through data augmentation strategies to enhance model adaptability. Subsequently, cross-validation is employed to fine-tune the hyperparameters of the YOLOv10 model, seeking optimal performance. The experimental results demonstrate that the YOLOv10 model successfully identifies cavities in radar images, achieving accuracy rates of nearly 90% and 97%. This study proves the potential of deep learning in GPR image analysis, effectively improving detection efficiency, accuracy, and automation levels, providing more reliable technical support for embankment safety inspection.