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Currently, deep learning is extensively utilized for ship target detection; however, achieving accurate and real-time detection of multi-scale targets remains a significant challenge. Considering the diverse scenes, varied scales, and complex backgrounds of ships in optical remote sensing images, we introduce a network model named YOLO-RSA. The model consists of a backbone feature extraction network, a multi-scale feature pyramid, and a rotated detection head. We conduct thorough tests on the HRSC2016 and DOTA datasets to validate the proposed algorithm. Through ablation experiments, we assess the impact of each improvement component on the model. In comparative experiments, the proposed model surpasses other models in terms of Recall, Precision, and MAP on the HRSC2016 dataset. Finally, in generalization experiments, our proposed ship detection model exhibits excellent detection performance across various scenarios. The method can accurately detect multi-scale ships in the image and provide a basis for marine ship monitoring and port management.
Fang et al. (Sat,) studied this question.