## 3 Results
### Comparison with the-State-of-Art Methods  
The experiment builds and trains the model based on TensorFlow framework. The model training process is carried out in the environment of two GPUs (Tesla p100-pcie). Based on the constructed aircraft training set, image samples and annotation files are put into the model for training, and the model super-parameters are adjusted according to experience and experimental results to test the best performance of the model. In this experiment, the IoU is set to 0.5, the model learning rate is initialized to 0.0005, batch size is initialized to 2 images, weight decay and momentum are initialized to 0.0001 and 0.9, respectively, and the maximum value of iteration is set to 500,000. In each iteration, the input image is subjected to a complete feed forward calculation and back propagation in the network, and the training parameters are updated until the return loss and classification loss of the network become convergent. The convergence curve of the proposed method of training is shown in Figure 4. The total loss is the sum of classification loss and regression loss. As shown in Figure 4, we can find that the loss rate is basically stable when the number of iterations reaches 50,000 and the total loss rate is only reduced about 1% when the number of iterations is increased to 50,000. Therefore, we can use the model of 50,000 iterations to detect aircraft in the real-time application. In order to verify the performance of the feature fusion module and the rotation anchor generating module, we performed an ablation study of the proposed method. At the same time, we use 50% cross validation to obtain an average value. Table 1 shows that rotating anchors can improve performance by about 7% (0.9641 versus 0.8894 as shown in Table 1), which is significant. We believe that the main reason is that the rotated anchor can fit the target well and reduce the number of negative samples, which are effective for improving performance. In addition, by fusing the multi-scale feature maps with local corner features the feature fusion module increases F1 performance by 8.12%, compared with RetinaNet (88.94%), reaching 97.06%. This phenomenon reflects the feature fusion module enhance the expression of the feature. Figure 4: Curves of classification loss (red line), regression loss (blue line), and total loss (green line). In addition, we compare the performance of the state-of-the-art deep learning networks in aircraft detection, including two-stage networks of Faster RCNN and FPN, and one-stage networks of RetinaNet, YOLOv4, SSD and R3 Det [1]. Among them, R3 Det is the one-stage method with the best performance at present for rotated detection. The two-stage methods need to generate object candidate proposals in the first stage, then classify and locate the object in the second stage. They can achieve high accuracy while taking a long time to train the network. The one-stage object detection methods directly convert the detection problem into a regression problem, which greatly improves the detection speed. It can be seen from Table 2 that the aircraft detection accuracy of FPN is the best, 90.84%, which is 1.6% and 0.68% higher than Faster RCNN and RetinaNet, respectively. The F1 value of RetinaNet is the highest, 88.94%, which is 6.3% and 4.26% higher than Faster RCNN and FPN, respectively. At the same time, according to the FPS value in Table 2 and the model training and testing time in Table 3, the efficiency of RetinaNet is the best. Experimental analysis shows that RetinaNet has better stability when it achieves better accuracy. Therefore, we use RetinaNet as the backbone network to study the effectiveness of the proposed method.