Underground target localization method for ground penetrating radar based on deep learning

To tackle the challenge of subsurface target localization under interference in field scenarios, a novel two-level cascade network referred to as dual cascade is proposed. The first level, Cascade-1, is a deep feature extraction network designed to extract and eliminate direct wave interference signals. On this basis, Cascade-2 is developed using domain knowledge from ground penetrating radar as prior information, and it incorporates an attention mechanism along with a feature fusion strategy to enhance the accuracy of target feature hyperbola detection. Subsequently, the least squares method is employed to fit the feature hyperbola, and location estimation is performed based on geometric equations. The proposed cascade network model has demonstrated superior performance compared to other algorithms, such as column-connection clustering algorithm, YOLOv9, and Faster R-CNN, in terms of the composite metric F1, which validates the model's effectiveness in extracting the feature hyperbola. Additionally, the proposed localization method has exhibited greater accuracy than the conventional full waveform inversion algorithm.