# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 4 Experiments
### Comparisons with the State-of-the-Art Methods
It has not only achieved good results in surface cover classification, target detection, and change detection but also performed well in various remote-sensing image-processing competitions. This fully demonstrates the practical value and wide applicability of OrientedDiffDet in the field of remote-sensing image processing. In conclusion, OrientedDiffDet, as an efficient and accurate remote-sensing image-processing method, finds a perfect balance between speed and accuracy. Its emergence has brought new possibilities for the field of remote-sensing image processing, and is expected to lead the development trend of remote-sensing image-processing technology in the future. **Results on HRSC. ** To assess the effectiveness and practicality of our method in tackling the intricate challenges of remote-sensing image object detection, particularly in the domain of ship detection, we conducted a comprehensive evaluation on the HRSC dataset. The results of this evaluation are summarized in Table 2. As illustrated in the table, our method has emerged as a frontrunner, achieving a remarkable 90.3 mAP at AP50. Noteworthy is the significant enhancement in AP75, underscoring our model's exceptional precision in object localization. These results validate the robustness and practicality of our approach in addressing the complexities inherent in remote-sensing image object detection tasks, particularly in scenarios involving ship detection. In this evaluation, we employed multiple evaluation metrics to measure the performance of our method on the HRSC dataset. In addition to mAP (average accuracy), we also focused on the various quantiles of AP (average accuracy), such as AP50 and AP75. These metrics facilitate a more comprehensive understanding of our method's performance in various detection tasks. The results in Table 2 indicate that our method has achieved 90.3 mAP on the AP50 index, which has reached the industry-leading level. At the same time, our model achieved a significant improvement in the AP75 index, which further confirmed its accuracy in target positioning. This result not only demonstrates the superiority of our method in the remote-sensing image target detection task, but also proves its practical value in complex scenarios (such as ship detection). In conclusion, through a comprehensive evaluation on the HRSC dataset, we confirm the effectiveness and utility of our approach in addressing the complex challenges in the detection task of remote-sensing image-based targets. Especially in the particular field of ship detection, our method has achieved excellent results in various evaluation indicators, which lays a foundation for further research in this field. In future studies, we will continue to optimize our method in order to provide more efficient solutions for remote-sensing image object detection in more scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AP** & **AP\_50** & **AP\_75** \\ \hline DiffusionDet [39] & 0.506 & 0.761 & 0.606 \\ RoI\_Trans [37] & 0.614 & 0.892 & 0.758 \\ ORCNN [40] & 0.547 & 0.896 & 0.637 \\ FCOS [41] & 0.529 & 0.883 & 0.620 \\ RepPoints [42] & 0.518 & 0.837 & 0.591 \\ ours & 0.724 & 0.903 & 0.895 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison on the HRSC dataset.