**6. Conclusions**

In this paper, a balanced rotated feature-aligned network (RBFA-Net) is proposed for SAR ship recognition. Firstly, a balanced-attention FPN can better integrate multi-scale image information and reduce the impact of multi-scale ship feature differences. In the balanced-attention FPN, non-local module can effectively enhance the network's response to global information. Secondly, rotated anchors can effectively reduce the interference of complex background in SAR ship recognition. At the same time, they can also reduce the problem of true-value suppression caused by NMS for densely gathered ships. In addition, the feature alignment module aligns the feature map with the anchor boxes, which reduces the misalignment between the rotation anchor and the feature map. Furthermore, in the rotational detection network, we add a task decoupling module to adjust the feature maps and recalibrate them for the classification task and regression task. Lastly, we use balanced L1 loss to reduce the imbalance between regression loss and classification loss. We conduct extensive ablation experiments and confirm the effectiveness of each improvement. The

experimental results show that RBFA-Net has the best performance compared with the other eight methods on the SRSDD dataset.

SAR ship detection methods based on deep learning have the advantages of full automation, high speed and strong model migration ability. On the basis of a large amount of data training, the deep-learning method can mine features that cannot be mined by traditional algorithms, so as to better realize SAR ship detection. The key advantage of SAR ship detection based on deep learning lies in the large amount of high-quality data and appropriate network models. In the future, there will be more high-quality datasets and more networks that can better mine SAR image features.

Our future works are as follows:


**Author Contributions:** Conceptualization, Z.S.; methodology, Z.S.; software, Z.S.; validation, Z.S.; formal analysis, Z.S.; investigation, Z.S.; resources, Z.S.; data curation, Z.S.; writing—Original draft preparation, Z.S.; writing—Review and editing, X.Z. and T.Z. (Tianwen Zhang); visualization, Z.S. and X.X.; supervision, T.Z. (Tianjiao Zeng); project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China (61571099).

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to thank all editors and reviewers for their valuable comments for improving this manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.
