CGA-Det: A CNN–GNN-Based Oriented SAR Ship Detector for Complex Scenes

Compared with horizontal detection, oriented ship detection provides accurate target localization and refined boundary delineation. However, ship detection in synthetic aperture radar (SAR) imagery faces significant challenges, including complex backgrounds and densely packed targets. To address these problems, we propose a novel network based on convolutional neural networks (CNNs) and graph neural networks (GNNs), named CNN-GNN-aware detector (CGA-Det). CGA-Det includes three innovations: 1) a CNN-GNN encode network (CG-Encode Network) that captures local and global relationships to concentrate on the targets’ area in complex densely populated scenes; 2) an adaptive feature fusion module (AFFM) that dynamically selects and integrates features from multilevels to enhance detection effect; and 3) a spatial-channel awareness head (SCHead) that promotes directional sensitivity by enhancing spatial and channel representation capacity of the detection head. Experiments on the SAR ship detection dataset (RSSDD) and RSDD-SAR (RSDD) demonstrate the state-of-the-art performance of CGA-Det, with 99.19% and 97.20% mAP50, excelling in complex scenes.