## 4 Experiments
### Comparison with Other Methods  
We also conducted experiments to compare our model with other state-of-the-art methods. To ensure fairness, all models were loaded with pre-trained models and run for 12 epochs using the DOTA-1.0 single-scale train and val dataset. The evaluation metrics were tested on the undisclosed label test set. To compare the effectiveness of our proposed method in more detail, we added indicators and visual images of the head categories large vehicle (LV) and small vehicle (SV), regular categories harbor (HA) and bridge (BR), and tail categories basketball court (BC) and tennis court (TC) in the results. Both qualitative and quantitative results are shown in Figure 7 and Table 3. In Figure 7, comparing (a) and (b), for the head category, the improved network output has higher confidence, increasing the recall rate and the probability of misclassification. Therefore, from Table 3, the head category does not show significant improvement compared to LSKNet. From (c), due to the sensitivity of slender objects such as bridges to changes in angle, adding random angle offsets may increase the probability of false positives. From (d), for categories like the harbor, the detection effect is better than that of the bridge, and the indicators are also better. From (e) and (f), our improved network performs better in detecting samples of tail categories compared to the LSKNet. As can also be seen from Table 3, our network has higher detection accuracy. In summary, our network performs better in detecting tail categories than LSKNet.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **AP50** & **AP75** & **mAP5:95** & **FPS(s)** & **Parameter(M)** & **GFLOPs** \\ \hline LSKNet & 75.86 & 47.65 & 45.46 & 22.6 & 21.0 & 124.42 \\ LSK-CLS & 76.43 & 48.77 & 46.18 & 22.7 & 21.0 & 124.42 \\ LSK-ARC & 75.91 & 47.41 & 45.53 & 6.3 & 22.77 & 73.2 \\ \hline DOUNet(ours) & 76.54 & 49.01 & 46.22 & 6.3 & 22.77 & 73.2 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of model accuracy without pre-training.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Method** & \multicolumn{2}{c}{**Head Categories**} & \multicolumn{2}{c}{**Normal Categories**} & \multicolumn{2}{c}{**Tail Categories**} & \\
**SV** & **LV** & **TC** & **BC** & **HA** & **BR** & **mAP(\%)** \\ \hline Rotated RepPoints [4] & 71.27 & 51.78 & 90.51 & 70.46 & 50.69 & 36.32 & 59.44 \\ SASM [22] & 72.08 & 72.35 & 90.90 & 75.69 & 63.75 & 45.41 & 66.43 \\ Rotated RetinaNet [2] & 77.51 & 62.63 & 90.89 & 82.31 & 56.46 & 40.88 & 68.42 \\ KFIoU [15] & 72.30 & 64.01 & 90.90 & 82.33 & 63.00 & 46.25 & 69.59 \\ CFA [23] & 79.05 & 74.14 & 90.87 & 80.35 & 64.40 & 49.19 & 69.63 \\ Oriented RepPoints [4] & 78.38 & 73.14 & 90.87 & 83.75 & 63.69 & 49.61 & 71.72 \\ PSC [12] & 78.92 & 70.08 & 90.90 & 82.82 & 61.35 & 42.48 & 71.91 \\ H2 RBo-v2 [24] & 79.81 & 74.77 & 90.88 & 85.47 & 65.87 & 50.75 & 72.61 \\ PKINet-T [6] & 76.35 & 81.92 & 90.85 & 84.14 & 67.11 & 51.24 & 73.60 \\ S\({}^{2}\)A-Net [8] & 78.61 & 77.37 & 90.90 & 86.28 & 66.08 & 51.57 & 73.92 \\ RetinaNet ConvNeXt [25] & 78.91 & 74.11 & 90.90 & 87.75 & 62.62 & 46.78 & 74.47 \\ Oriented R-CNN [10] & 78.33 & 77.82 & 90.88 & 85.60 & 67.67 & 51.78 & 74.99 \\ LSKNet [5] & 75.02 & 83.77 & 90.90 & 86.30 & 68.65 & 50.87 & 75.86 \\ \hline DOUNet(ours) & 75.11 & 82.95 & 90.90 & 87.92 & 73.27 & 51.04 & 76.54 \\ \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison with other rotation detectors on the DOTA-v1.0 dataset.  
Figure 7: Qualitative visualization results of LSKNet and DOUNet. (**a,b**) Visualization of detection results for head categories, with purple representing small vehicles and orange red representing large vehicles. (**c,d**) Visualization of normal categories, lemon yellow for harbor and pink for ship. (**e,f**) Visualization of tail categories, sky blue for tennis-court, yellow for baseball-diamond, white for basketball-court, light pink for soccer-ball-field.