# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 4 Experiments
### Comparisons with the State-of-the-Art Methods  
**Results on DOTA. ** As shown in Table 1, we compared our OrientedDiffDet with other state-of-the-art methods on the DOTA dataset. Our single-scale model achieves 76.59% mAP, outperforming the previous models. Additionally,we use DiffusionDet as a baseline model. This model utilizes horizontal boxes, and our model uses oriented boxes, which achieves substantial improvement for the baseline model (67.16% vs. 76.59%). This suggests that oriented boxed in remote-sensing images can capture the orientation of an object in a more precise manner. While our model delivered substantial performance enhancements across most categories, a slight decline was observed in a few classes when compared to previous methods. These include categories like _baseball diamond, ground track field, soccer ball field_, and others. We attribute this phenomenon to the inherent similarity in shapes and the potential for confusion between their contours and the background. We give some qualitative results of OrientedDiffDet on DOTA in Figure 4.  
In terms of Frames Per Second (FPS), our OrientedDiffDet exhibited exceptional inference speed, achieving an impressive rate of 11.3 FPS. This performance outpaces the majority of detectors, underscoring its ability to process remote-sensing images rapidly and efficiently. It is worth mentioning that while OrientedDiffDet is slightly higher than DiffusionDet in terms of FPS (11.3 vs. 10.8), it maintains a superior level of accuracy. Our approach effectively strikes a balance between accuracy and speed, making it a compelling choice for various remote-sensing image-processing tasks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RoI\_Trans [37]** & **R3 Det [18]** & **RRetinaNet [38]** & **DiffusionDet [39]** & **S2 ANet [8]** & **Ours** \\ \hline PL & 88.64 & 88.76 & 88.67 & 88.20 & **89.11** & 88.84 \\ BD & 78.52 & **83.09** & 77.62 & 77.03 & 82.84 & 82.69 \\ BR & 43.44 & 50.91 & 41.81 & 41.10 & 48.37 & **53.94** \\ GTF & **75.92** & 67.27 & 58.17 & 52.09 & 71.11 & 74.98 \\ SV & 68.81 & 76.23 & 74.58 & 71.25 & 78.11 & **78.95** \\ LV & 73.68 & 80.39 & 71.64 & 70.08 & 78.39 & **84.89** \\ SH & 83.59 & 86.72 & 79.11 & 76.55 & 87.25 & **88.86** \\ TC & 90.74 & 90.78 & 90.29 & 88.63 & 90.83 & **90.93** \\ BC & 77.27 & 84.68 & 82.18 & 80.16 & 84.90 & **87.84** \\ ST & 81.46 & 83.24 & 74.32 & 74.43 & 85.64 & **85.75** \\ SBF & 58.39 & **61.98** & 54.75 & 53.96 & 60.36 & 61.67 \\ RA & 53.54 & 61.35 & 60.60 & 57.37 & **62.60** & 60.45 \\ HA & 62.83 & 66.91 & 62.57 & 60.23 & 65.26 & **75.79** \\ SP & 58.93 & **70.63** & 69.67 & 61.47 & 69.13 & 68.65 \\ HC & 47.67 & 53.94 & 60.64 & 54.92 & 57.94 & **64.67** \\ mAP & 69.56 & 73.79 & 68.43 & 67.16 & 74.12 & **76.59** \\ FPS & **11.3** & 14.7 & 16.1 & **10.8** & 15.1 & **11.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons with state-of-the-art methods on the DOTA dataset. Figure 4: Visualization of results from OrientedDiffDet in DOTA. First, we need to understand the principle of OrientedDiffDet. This method performs object detection on remote-sensing images through a combination of rotated boxes and the diffusion model. Rotated boxes help identify local features in an image, while the diffusion model helps capture global information. This combination gives OrientedDiffDet high accuracy in detecting targets in remote-sensing images. On the other hand, the advantage of OrientedDiffDet in processing speed also deserves attention. By adopting efficient algorithms, it is able to quickly analyze images during the inference process. This allows OrientedDiffDet to still maintain a high FPS when processing large-scale remote-sensing images, thus improving the overall work efficiency. In practical applications, remote-sensing image-processing tasks often require a trade-off between speed and accuracy. The emergence of OrientedDiffDet just meets this demand.