Structure-Aware Conditional GAN for High-Fidelity GPR B-Scan Image Synthesis in Airport Runway Defect Detection

Airport runways are critical transportation infrastructures whose structural integrity is continuously threatened by subsurface defects induced by cyclic aircraft loading and environmental erosion. Ground-penetrating radar (GPR) B-scan imaging is widely employed for subsurface inspection, yet deep learning-based interpretation is severely constrained by the scarcity of high-quality labeled data. Synthetic data generation offers a promising remedy, but existing GPR generators typically operate at a single feature scale and lack defect-category awareness, so that weak reflections of subtle defects are easily suppressed while large-scale defects are rendered as fragmented or structurally implausible patterns. To address these limitations, we propose SA-CGAN, a structure-aware conditional generative adversarial network that synthesizes high-fidelity, defect-specific GPR B-scan images for multiple defect types within a unified conditional framework, jointly guided by a defect-class condition and a spatially aligned bounding-box mask. Two complementary attention modules are embedded into the generator to close the fidelity gap from opposite ends. A Class-aware Local Structure Enhancement Attention (CLSEA) amplifies weak directional reflections of subtle defects by coupling a parameter-free multi-directional energy prior with class-conditional modulation under mask guidance, whereas a Structure-Localized Deformable Enhancement Attention (SLDEA) preserves the spatial coherence of large-scale, irregularly shaped defects via mask-conditioned deformable sampling and gated residual refinement. Experiments on real airport runway GPR data collected from 11 runways show that SA-CGAN achieves favorable FID, KID, and mFSIM scores compared with competitive generative baselines, and that augmenting real data with SA-CGAN-synthesized samples consistently improves downstream YOLOv10s detection, raising the F1-score to 0.94, 0.86, 0.93, and 0.93 on cracks, voids, looseness, and subsidence, respectively.