A Feature-Enhanced Deep Learning Network for GPR Hyperbolic Target Detection in B-Scan Profiles

Abstract

(1) Background: To address the challenges in ground-penetrating radar (GPR) B-scan profiles—where targets typically exhibit hyperbolic signatures, small scales, low contrast, and severe background clutter—this paper proposes an efficient and accurate target detection algorithm based on YOLOv8n, termed YOLOv8n-PCPG. (2) Methods: First, a Pinwheel-shaped Convolution (PSConv) is integrated into the backbone to capture directional hyperbolic boundaries. Second, a lightweight CSP-PMSFA module replaces the standard C2f structures in the neck, reducing computational redundancy while preserving feature integrity. Finally, a structurally reparameterized GC-downsampling module is introduced to enhance cross-channel feature interaction without adding inference latency. (3) Extensive experiments on a self-constructed GPR B-scan dataset show that the proposed model achieves a Precision of 97.2%, a Recall of 97.3%, an mAP@0.5 of 98.8%, and an mAP@0.75 of 84.3%. Compared with the baseline YOLOv8n, the parameter count and GFLOPs are reduced by 13.0% and 7.4%, respectively, while achieving a real-time detection speed of 155.78 FPS, outperforming existing mainstream detectors. (4) Conclusions: These results demonstrate that YOLOv8n-PCPG achieves a strong trade-off between detection accuracy and computational efficiency, providing effective technical support for subsurface hyperbolic signature identification.