## 5 Experimental Results and Analysis  
### Comparative Experiment  
(1) Comparative analysis on DIOR-R: We compare our method against six advanced techniques on the DIOR-R dataset, as shown in Table 3. Our models, using ResNet50-FPN and ResNet101-FPN as backbones, achieve mAP scores of 61.88% and 62.35%, respectively, surpassing all other methods listed. Notably, with ResNet101-FPN, our method achieves the best AP in nine categories and the second-best in four. Figure 7 illustrates the qualitative performance of our method in detecting rotated objects against challenging backgrounds, which effectively demonstrates the robustness and practicality of our approach. Meanwhile, we also observe that in a few specific categories, our method exhibits slightly lower performance compared to the original baseline. To better understand and address this issue, we conduct an in-depth analysis in the subsequent sections, exploring the potential causes and discussing possible directions for improvement. (2) Comparative analysis on HRSC2016: Similarly, we conducted a comparative analysis on the HRSC2016 dataset, with the results presented in Table 4. Our method achieved AP scores of 90.03% and 90.20% with ResNet50-FPN and ResNet101-FPN backbones, respectively, surpassing the performance of all other methods. Despite the significant aspect ratio variations in the HRSC2016 dataset, our method consistently produced accurate bounding box predictions. Selected results are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **Backbone** & **APL** & **APO** & **BF** & **BC** & **BR** & **CH** & **DAM** & **ETS** & **ESA** & **GF** & **GTF** & **HA** & **OP** & **SH** & **STA** & **STO** & **TC** & **TS** & **VE** & **WM** & **nAP** & **FPS** \\ \hline FOCS-O [13] & R-50-FPN & 62.01 & 30.62 & 74.81 & 81.31 & 28.36 & 72.60 & 23.45 & 75.17 & 55.78 & 74.24 & 79.17 & 34.82 & 43.92 & 77.22 &  
The Figure 9 presents the confusion matrix detailing the prediction outcomes for each individual category. Regarding the categories where our model exhibits relatively weaker performance, such as APO and GF, we hypothesize that the rotation features we extracted may not positively contribute to the detection of these categories. Figure 10 shows sample images from these categories and provides a detailed explanation of this observation. In Figure 9, the confusion matrix--extended to include all 21 output classes (with 'no prediction' as class 0)--provides a fine-grained evaluation of the performance per category of our model. The pronounced diagonal dominance attests to the strong discriminative power of our network, while off-diagonal entries reveal residual confusions, most notably between visually or semantically adjacent classes (e.g., DAM [Dam] vs. BR [Bridge] vs. OP [overpass]). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Rol-Transformer [26]** & **Gliding Vertex [37]** & **RSDet [40]** & **CSL [41]** & **R3 Det [42]** & **KLD [43]** & **FCOS-O [13]** & \multicolumn{2}{c}{**Ours**} \\ \hline Backbone & R101 + FPN & R101 + FPN & R101 + FPN & R101 + FPN & R101 + FPN & R50 + FPN & R50 + FPN & R50 + FPN & R101 + FPN \\ \hline AP(\%) & 86.20 & 88.20 & 86.50 & 89.62 & 89.20 & 89.97 & 89.11 & 90.03 & 90.20 \\ \hline FPS & 9.1 & 9.4 & - & 17.2 & 9.5 & 18.2 & 20.8 & 20.2 & 17.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results on HRSC2016. The cells highlighted in red and blue indicate the highest and second-highest values in each column, respectively. Figure 8: Visualization results of our method for HRSC2016. Figure 7: Visualization results of our method for DIOR-R. Figure 10 shows two sample images from the APO and GF categories. The APO category consists of long, strip-like airplane runways in the sample images, which blend seamlessly with the surrounding environment, making it difficult for the model to accurately distinguish them.