Domain Adaption YOLO Network to Enhance Target Detection in GPR Images

The use of deep learning to interpret ground-penetrating radar (GPR) data for automated target detection has become a rapidly developing field. However, the performance of deep learning networks is significantly affected by the quantity and diversity of the available GPR data for training. The finite-difference time-domain (FDTD) method is commonly used to supplement datasets. However, there are considerable differences between FDTD-simulated and real GPR data, which limit the effectiveness of using FDTD data for training. To address these challenges, we propose a generative adversarial network (GAN)-based domain adaptation (DA) network for detecting target reflections in GPR data. This network combines the you only look once (YOLO) network with a DA module, forming a GAN-based DA-YOLO that aligns features extracted from simulated and real GPR data to enhance the performance of the network on real GPR data. To further improve the network’s performance on real GPR data, a pseudo-labeling strategy is employed. This approach leverages the pseudo-labels generated by the network to iteratively refine and improve its accuracy. To validate the proposed method, we conduct comparative experiments to evaluate the impact of key factors, including network architecture, pseudo-labeling thresholds, and source-target-domain discrepancies, on feature alignment and the improvement of detection performance for target-domain data. The effectiveness of the proposed method is validated using real-world GPR data collected from a railway tunnel inspection. The results demonstrate that the proposed DA-YOLO network achieves the best detection performance on real data, with recall, precision, ${F}1$ score, and mAP values of 81.25%, 86.67%, 83.87%, and 69.30%, respectively, outperforming other networks that do not utilize DA methods. This study provides a solution for situations where real GPR data is scarce and only FDTD-simulated data can be used as training data.