Abstract

Synthetic aperture radar (SAR) ship detection is critical for maritime surveillance; however, accurately identifying targets in complex marine environments remains a persistent challenge due to severe sea clutter and coastal interference. To address the prevalent issues of missed detections and false alarms, this paper proposes a novel deep learning framework named AFN-YOLO (Adaptive Frequency Network–You Only Look Once). Specifically, we propose a C2f_FF (C2f Frequency Fusion) module that dynamically extracts and fuses frequency-domain and spatial-domain information, effectively suppressing background interference and artifacts from nearby buildings. Additionally, a TAS-FPN (Triplet Attention-based Spatial FPN) architecture is integrated to capture multiscale features, significantly improving the detection capability for small and overlapping ship targets. Furthermore, the loss function is optimized to compel the model to focus on salient target features while disregarding irrelevant background data. Extensive experiments on the SAR Ship Detection Dataset (SSDD) and the High-Resolution SAR Images Dataset (HRSID) validate the effectiveness of our approach.

IPC Classification

Keywords