CV-YOLO: A Complex-Valued Convolutional Neural Network for Oriented Ship Detection in Single-Polarization Single-Look Complex SAR Images

Deep learning has significantly advanced synthetic aperture radar (SAR) ship detection in recent years. However, existing approaches predominantly rely on amplitude information while largely overlooking the critical phase component, limiting further performance improvements. Additionally, unlike optical images, which benefit from a variety of enhancement techniques, complex-valued SAR images lack effective processing methods. To address these challenges, we propose Complex-Valued You Only Look Once (CV-YOLO), an anchor-free, oriented bounding box (OBB)-based ship detection network that fully exploits both amplitude and phase information from single-polarization, single-look complex SAR images. Furthermore, we introduce novel complex-valued data augmentation strategies—including complex-valued Gaussian filtering, complex-valued Mosaic data augmentation, and complex-valued mixed sample data augmentation—to enhance sample diversity and significantly improve the generalization capability of complex-valued networks. Experimental evaluations of the Complex-Valued SAR Images Rotation Ship Detection Dataset (CSRSDD) demonstrate that our method surpasses real-valued networks with identical architectures and outperforms leading real-valued approaches, validating the effectiveness of our proposed methodology.