## 5 Experimental Results and Analysis
### Ablation Experiment  
To assess the contribution of each component in our proposed method, we performed a series of ablation experiments. Notably, the RIL and RFF modules were integrated to form the rotation feature enhancement module, and the analysis was conducted as a whole. (1) Effectiveness of RIL+RFF: To verify the efficacy of our rotation feature enhancement module, we integrated the RIL and RFF modules into two baseline models and evaluated their performance on the DIOR-R dataset. The results in Table 5 show that the two models achieved mAP scores of 60.66% and 58.89%, representing improvements of 2.24% and 1.34% over the original method, respectively. These results clearly demonstrate that our module significantly enhances the model's ability to adapt to rotational features. (2) Effectiveness of Dual-Aspect RIoU Loss: To investigate the effectiveness of our DARIoU Loss, we replaced the IoU loss from FCOS-O with our loss function, while not making any other modifications. The results on the DIOR-R dataset, presented in Table 5, show that the two models achieved increases of 1.41% and 0.67% in mAP over the baseline model, respectively. These results confirm the effectiveness of our approach in addressing the rotation box regression task. In addition, we compared DARIoU with several advanced regression loss functions (IoU loss, GIoU loss, DIoU loss, and CIoU loss) on the HRSC2016 dataset. As shown in Table 6, DARIoU outperforms the other loss functions, achieving 89.92% AP50, beating the others by 0.81%, 0.40%, 0.06%, and 0.28% mAP, respectively. Furthermore, the accuracy on AP95 improved by 2.5%, significantly outperforming other methods. To further evaluate the effectiveness of the DARIoU, we compared its performance with the baseline (IoU loss) over the training epochs for both the HRSC2016 and DIOR-R datasets, as shown in Figure 11. On the HRSC2016 dataset, the curve for DARIoU exhibits a much smoother progression in AP50, with a faster and more consistent increase throughout the training. Similarly, for the DIOR-R dataset, DARIoU results in a steadier and faster rise in mAP across 12 epochs, achieving a higher final value compared to the baseline. These observations indicate that DARIoU not only accelerates convergence but also leads to more stable and reliable performance during training. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loss Function** & **IoU Loss** & **GIoU Loss** & **DIoU Loss** & **CIoU Loss** & **DARIoU Loss** \\ \hline \(AP_{50}\) & 89.11 & 89.52 & 89.86 & 89.64 & **89.92** \\ \hline \(AP_{75}\) & 72.82 & 73.55 & 74.73 & 73.98 & **75.40** \\ \hline \(AP_{95}\) & 0.30 & 0.80 & 1.00 & 0.50 & **2.50** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation studies of different bounding box regressions on HRSC2016 in terms of \(AP_{80}\), \(AP_{75}\) and \(AP_{95}\) metrics. The values in bold indicate the highest performance in each column. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Baseline** & **Different Setting of Our Method** & **mAP(\%)** \\ \hline \multirow{3}{*}{FCOS-O [13]} & None & 58.42 \\  & RIL + RFF & 60.66 \\  & DARIoU & 59.83 \\  & RIL + RFF + DARIoU & **61.88** \\ \hline \multirow{3}{*}{RetinaNet-O [36]} & None & 57.55 \\  & RIL + RFF & 58.89 \\  & DARIoU & 58.22 \\  & RIL + RFF + DARIoU & **59.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation studies of different components on the DIOR-R. The values in bold indicate the highest performance in each column. (3) Combination of two components: After integrating the two modules, the comparative results of our method and the baseline model are shown in Table 5. The data indicate improvements of 3.46% and 2.39% in mAP when applying the two methods individually, both of which outperform the isolated application of each module. This suggests that the proposed modules facilitate the model's adaptation to rotational characteristics from various perspectives, thereby generating more precise outcomes.