**2. Proposed Methods**

RBFA-Net is designed on the basis of RetinaNet [13], which consists of FPN and detection subnets. First, we improve the detection subnets with rotated anchors where RetinaNet uses horizontal anchors. Then, we use the BAFPN instead of the FPN originally used by RetinaNet to enhance feature extraction ability. In addition, we add an anchorguided feature alignment network after FPN to solve the misalignment between the features and rotated anchors. Finally, unlike RetinaNet directly inputting the features into the classification and regression subnets, we add TDN to solve the conflict problem before inputting the features into the classification and regression subnets.

The whole framework of RBFA-Net is divided into three parts: (1) a balanced-attention FPN (BAFPN), (2) an anchor-guided feature alignment network (AFAN) and (3) a rotational detection network (RDN). BAFPN is used for feature extraction, fusion and enhancement. AFAM is used for decreasing the dislocation between the rotated anchor boxes and feature maps. RDN is used for fixing the position of ships with rotated bounding boxes and classifying the categories of ships. The architecture of RBFA-Net is shown in Figure 3.

In this section, we will first introduce BAFPN, and then, we will explain AFAM. Finally, we will introduce RDN in detail. At the end of this section, we will introduce our loss function.

**Figure 3.** Architecture of RBFA-Net.
