Enhancement of intelligent GPR detection based on category-aware generative framework of B-scan images

Deep learning has advanced intelligent detection of ground penetrating radar (GPR) applied in transportation infrastructure inspection, yet its performance remains limited by the scarcity and class imbalance of labeled GPR B-scan images. To address this challenge, this study proposes GPR-ImaGen, a category-aware generative data augmentation framework for GPR B-scan images. Built on the pretrained rectified flow model Flux.1-dev, the framework generates defect category-consistent B-scan images under semantic guidance from engineering annotations, alleviating the scarcity and class imbalance of the training data for intelligent detection. To better adapt the model to the characteristics of GPR B-scan images and limited-data conditions, parameter-efficient fine-tuning, a robust loss function and a boundary-focused timestep sampling strategy are incorporated into the training process. The proposed framework achieves an FID of 10.496 and a Bscan-FID of 10.828. Extensive task-oriented experiments across multiple intelligent detection models and diverse limited-data settings show that the generated data consistently improve downstream detection performance. Among them, YOLOv11 achieves the highest detection performance (mAP@50 of 91.6%) at a 100% augmentation ratio, while mAP@50 increases from 73.6% to 81.9% under a data-scarce setting with only 25% of the real training data retained. These results indicate the practical potential of a category-aware generative framework for improving the robustness and reliability of intelligent GPR detection for transportation infrastructure inspection.