## 3 Results
### Comparison with the-State-of-Art Methods
The effects of the rotating anchors generation module and multi-feature fusion module on the method are verified and the results are shown in Table 2. Compared with the optimal HBB detection method (Faster RCNN, FPN and RetinaNet), the aircraft detection performance of our proposed OBB is improved by about 8% (8.12%), although there is a slight decrease (about 1.62 fps) in speed performance. The compared results between R3 Det and proposed method show R3 Det has the best detection performance (F1 = 97.49%), which is 0.43% higher than the proposed method. However, the feature refinement module FRM in R3 Det requires a lot of computing resources. Table 2 shows that the speed performance decrease about 2.35 fps. Through the analysis of experimental comparison, it is shown that the proposed method can achieve the considerable detection performance as R3 Det while with better performance in speed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & **Feature Fusion** & **Rotation** & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1**} \\  & **Module** & **Anchor** & & & \\ \hline ResNet-50 & \(\times\) & \(\times\) & 0.8616 & 0.8556 & 0.8584 \\ ResNet-101 & \(\times\) & \(\times\) & 0.9072 & 0.9016 & 0.8894 \\ ResNet-50 & \(\times\) & \(\surd\) & 0.9633 & 0.9534 & 0.9592 \\ ResNet-101 & \(\times\) & \(\surd\) & 0.9750 & 0.9552 & 0.9641 \\ ResNet-50 & \(\surd\) & \(\surd\) & 0.9691 & 0.9569 & 0.9601 \\ ResNet-101 & \(\surd\) & \(\surd\) & 0.9863 & 0.9583 & 0.9706 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablative study of each component in our method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Backbone** & \multirow{2}{*}{**R**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**FPS**} \\  & **Network** & & & & \\ \hline Faster RCNN & Resnet-101 & 76.95\% & 89.24\% & 82.64\% & 9.11 \\ FPN & Resnet-101 & 79.30\% & 90.84\% & 84.68\% & 9.71 \\ RetinaNet & Resnet-101 & 90.72\% & 90.16\% & 88.94\% & 13.0 \\ SSD & VGG-16 & 84.09\% & 90.17\% & 86.40\% & 28.04 \\ YoloV4 & CSPDarknet53 & 86.10\% & 96.03\% & 91.08\% & 41.02 \\  & Resnet-50 & 97.01\% & 95.96\% & 96.12\% & 9.27 \\ R3 Det & Resnet-101 & 98.70\% & 96.39\% & 97.49\% & 9.03 \\ RetinaNet + Rotating & Resnet-50 & 96.33\% & 95.34\% & 95.92\% & 12.79 \\ Anchor & Resnet-101 & 97.50\% & 95.52\% & 96.41\% & 12.12 \\ Proposed Method & Resnet-50 & 96.91\% & 95.69\% & 96.01\% & 11.85 \\  & Resnet-101 & 98.63\% & 95.83\% & 97.06\% & 11.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of aircraft detection performance of deep learning methods. In addition, the visualization results of the proposed OBB aircraft detection method in this paper and HBB detection by FPN are shown in Figure 5. First, we can see that rotation detection significantly reduces the overlap and nesting of detection boxes in the red rectangular region. What's more, compared with the results of HBB and OBB detection methods proposed in this paper, in the purple rectangular region, the redundancy of the rotation detection boxes is significantly reduced, and the rotation detection boxes fit the aircrafts better, obtaining much more accurate information of aircraft scales and locations.