Thanks to the excellent feature representation capabilities of neural networks, deep learning-based methods perform far better than traditional methods on target detection tasks such as ship detection. Although various network models have been proposed for SAR ship detection such as DRBox-v1, DRBox-v2 and MSR2N, there are still some problems such as mismatch of feature scale, contradictions between different learning tasks and unbalanced distribution of positive samples, which have not been mentioned in these studies. In this paper, an improved one-stage object detection framework based on RetinaNet and rotatable bounding box (RBox), which is referred as R-RetinaNet, is proposed to solve the above problems. The main improvements of R-RetinaNet as well as the contributions of this paper are threefold. First, a scale calibration method is proposed to align the scale distribution of the output backbone feature map with the scale distribution of the targets. Second, a feature fusion network based on task-wise attention feature pyramid network (TA-FPN) is designed to decouple the feature optimization process of different tasks, which alleviates the conflict between different learning goals. Finally, an adaptive Intersection over Union (IoU) threshold training method is proposed for RBox-based model to correct the unbalanced distribution of positive samples caused by the fixed IoU threshold on RBox. Experimental results show that our method obtains 13.26%, 9.49%, 8.92% and 4.55% gains in average precision (AP) under an IoU threshold of 0.5 on the public SAR ship detection data set (SSDD) compared with four state-of-the-art RBox-based methods, respectively.

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