A Feature-Enhanced Network-Based Target Detection Method for SAR Images of Ships in Complex Scenes

In the context of ship target detection with Synthetic Aperture Radar (SAR) images, misdetection and missed detection are often caused by complex background interference and the variability in target size. To address these challenges, this paper proposes an innovative method based on image enhancement and feature fusion to reduce background noise and effectively handle the detection confusion caused by differences in ship sizes. Firstly, a feature-aware enhancement network is introduced, which preserves and strengthens the edge information of the target objects. Secondly, during the feature extraction phase, a dynamic hierarchical extraction module is proposed, significantly improving the feature capture ability of convolutional neural networks and overcoming the limitations of traditional fixed kernel receptive fields. Finally, a feature fusion module based on attention gating is employed to fully leverage the complementary information between the original and enhanced images, achieving precise modeling and efficient fusion of inter-feature correlations. The proposed method is integrated with the YOLOv8 detection framework for target detection. Experimental results in the publicly available SSDD and HRSID datasets demonstrate detection accuracies of 97.9% and 93.2%, respectively, thus validating the superiority and robustness of the proposed method.