Kolmogorov–Arnold–Enhanced Nonlinear Expansions for Fine-Grained Feature Amplification in Robust Near-Shore SAR Vessel Discrimination

Ship detection based on Synthetic Aperture Radar (SAR) is crucial for maritime surveillance, yet existing methods struggle with two critical challenges: (1) missed detections in nearshore scenarios due to highly similar radar reflections between ships and shore objects (e.g., buildings, docks), and (2) feature vanishing of small ships caused by SAR resolution limits and downsampling operations. To address these issues, we propose Kolmogorov-Arnold-Enhanced YOLO (KaneYOLO), a novel SAR ship detector designed for robust discrimination in complex nearshore scenes and enhanced small-target detection. Firstly, we introduce KAN Block, a feature extraction module leveraging the Kolmogorov-Arnold theorem to model complex nonlinear relationships in SAR images. By integrating Gram polynomials and B-spline-based activation functions, KAN Block dynamically calibrates feature responses to suppress background clutter (e.g., shore structures) while amplifying subtle intensity patterns and edge transitions unique to ships. Secondly, we design a Feature Fusion Allocation Structure (FFAS) with parallelized deep convolutions to aggregate multi-scale features. This structure preserves small-target details by propagating enriched semantic information across detection layers, mitigating feature loss during downsampling. Finally, a Detail Enhancement Detection Head (DEDH) is proposed to reduce computational overhead through shared convolutional layers, while enhancing local feature utilization via direction-sensitive convolutions and group normalization. KaneYOLO achieved 93.9% AP on HRSID and 98.3% AP on SSDD. These results validate its robustness in handling SAR-specific challenges, offering significant potential for real-world maritime security applications.