A High Accuracy Detection Network for Rotated Multi-Class SAR Ship Detection

At present, most of the ship detection methods use horizontal detection bounding box, which results in the interference of dense ships and decreases the detection accuracy. In addition, most of the current ship detection methods remain in single-category detection, and do not achieve multi-class ship detection, which limits the further promotion and application of these detection methods. In this work, we propose a novel network for high-precision detection of rotated multi-class ships by rotated bounding box, called Rotated Multi-Class Detection Network (RMCD-Net). In RMCD-Net, we adapt a rotated anchor-feature alignment module (RAAM) to solve the misalignment problem between rotated anchors and horizontal features. In RMCD-Net, we adapt double detection head mechanism for better regression and classification. Also, we apply focal loss to classification task. Experimental results on the public dataset SRSDD show that mAP of RMCD-Net is 61.62% that is better than the second-best model by 5.39%.