## 2 Methods
### Rotating Anchor Generation Mechanism  
The input of the multi-scale rotation anchor is the output of FPN, and the rotation area network is constructed based on the area generation network and the fully convolutional neural network. The network first generates a multi-scale rotation anchor composed of scale, aspect ratio, and angle, and puts it into two sub-networks of classification and regression. The classification sub-network predicts the probability of the target, and the regression sub-network regresses the offset between the boundary box and the true value box. Finally, the non-maximum suppression operation and backward processing operation are performed to obtain the final detection result.  
In the multi-scale rotation anchor generation mechanism, three parameters are used to control the anchor, namely, scale, aspect ratio, and angle. To save the calculation cost and make the anchor cover the aircraft as much as possible, the basic scale of the initial anchor in [P3, P4, P5, P6, P7] layers is set as {32, 64, 128, 256, 512}, the scale of each layer is set as {2\({}^{0}\), 2\({}^{1/3}\), 2\({}^{2/3}\)} to control the size of the anchor, and the aspect ratio is set as {1, 1/2, 2,1/3, 3, 1/5, 5} to control the proportion of the anchor. At the same time, five different angles are set, namely {\(-15^{\circ}\), \(-30^{\circ}\), \(-45^{\circ}\), \(-60^{\circ}\), \(-75^{\circ}\)}, to realize the anchor rotation to different directions. The multi-scale rotating anchor generation strategy can well solve the problem of detection frame nesting and overlapping in the horizontal detection of aircraft with diverse scales and arbitrary orientation. In addition, assuming that each feature point of the feature map will generate A anchors, the H*W feature map will generate A*H*W anchors.  
In the prediction stage of the sub-network, the traditional horizontal bounding box (dashed box) is usually represented by x\({}_{\text{min}}\), y\({}_{\text{min}}\), x\({}_{\text{max}}\), and y\({}_{\text{max}}\); based on these four parameters, the bounding box regression transformation is performed to obtain the predicted bounding box, but it cannot well cover the aircraft in different directions. Therefore, the representation of rotating bounding box (solid wireframe) is defined, which is not only controlled by scale and aspect ratio, but also by direction. Five variables (x, y, w, h, and \(\theta\)) are used to uniquely determine a bounding box in any direction, as shown in Figure 3. Where x and y represent the coordinates of the center point, respectively, \(\theta\) represents the angle between the horizontal axis and the first side of the rectangle, and the range of the angle is [\(-90^{\circ}\), \(0^{\circ}\)].  
Based on the above five parameters, the classification sub-network predicts the probability (KA) of the object in the anchor at each spatial position, where A represents the number of anchors, and K represents the category. The regression sub-network outputs the position information (5A) of each anchor box based on the five parameters defined above and performs regression transformation based on the offset between the anchor box, and the truth box calculates the intersection over union (IOU) and gets the rotating anchor box, which has the largest IOU with the truth box. The position information of the target in the predicted bounding box includes the coordinates, width, height, and angle of the center point. Based on the obtained position information, the center, scale, and radius of the aircraft in the prediction box can be calculated, which lays a foundation for the next processing.