*2.3. Rotational Detection Network*

In this section, we propose a rotational detection network (RDN) to realize ship detection and classification. RDN is designed on the basis of RetinaNet [13], where the feature maps are sent to the regression subnet and classification subnet, respectively. However,

the features used for classification should remain invariant, while for the regression task, the features should represent the changes of the target position, size and rotation angle. The opposite requirements bring negative impact on detection accuracy. In order to solve this conflict, we propose a task decoupling module. Recent studies [52] have shown that decoupling the classification task and regression task in the spatial dimension of the feature maps can relieve this conflict. Therefore, we add a task decoupling module consisting of two squeeze-and-excitation (SE) modules [53]. Figure 9 shows the architecture of the rotational detection network.

As shown in Figure 9, a global average pooling layer, a full connection layer and a ReLU activation function form the encoder of the task decoupling module. For the feature maps from AFAN, their dimension is *H* × *W* × *C*. Firstly, these feature maps are put into the global average pooling layer to extract the global feature information. Then, we send the output feature maps of the global pooling layer to a full connection layer and a ReLU activation function [54]. According to the design of SENet, the size of the output feature map is compressed to 1 × 1 × *C*/8. Then, the output of the encoder will be input into two decoders composed of a full connection layer and a sigmoid activation function, respectively, and the size will be restored to 1 × 1 × *C* in this process. Finally, the feature maps are multiplied by the corresponding elements in the decoder output vector to adjust the feature maps to adapt to different learning tasks. The output feature maps are sent to the classification subnet and regression subnet, respectively.

**Figure 9.** Architecture of rotational detection network (RDN).
