# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 4 Experiments
### Discussions  
We also conduct some experiments to discuss the option of parameters in our method and determine the optimal settings.  
* Signal scaling.  
In the course of the diffusion process, the signal scaling factor plays a pivotal role in regulating the signal-to-noise ratio (SNR). As revealed in Table 4, our experiments demonstrate that a scaling factor of 1.0 yields the most optimal performance. It is worth  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Oriented Box Representation** & **Feature Alignment** & **Accelerated Diffusion** & **mAP** & **FPS** \\ \hline \hline \multirow{3}{*}{\(\checkmark\) *} & & & 73.20 & 10.8 \\  & & & 75.93 & 11.7 \\ \cline{3-5}  & \(\checkmark\) & & 74.21 & 11.9 \\ \cline{3-5}  & \(\checkmark\) & \(\checkmark\) & 78.96 & 11.3 \\ \hline \hline \end{tabular}
* A checkmark indicates that this module is selected.  
\end{table}
Table 3: Ablation experiment on the DOTA validation set.  
noting that when generating an oriented box, six parameters are involved as opposed to the four required for a horizontal box. Our OrientedDiffDet model benefits from training targets standardized to a value of 1.0, which simplifies the training process and contributes to its superior performance.  
In cases where the prediction scores fall below a certain threshold, reactivation is employed to enhance the generative boxes. The impact of different score thresholds for box renewal is illustrated in Table 5. From the results, it is evident that a threshold value of 0.3 yields slightly superior performance compared to other thresholds. Consequently, in our experiments, we opt for a box renewal threshold of 0.3.  
Comparison using accelerated diffusion modules.  
We compare different accelerated diffusion modules in Table 6. In this study, we made an in-depth comparison of different types of samplers, including methods such as DDPM [21] and DDIM [43]. By analyzing the performance of various samplers, we found that the accelerated diffusion sampler has a significant advantage in the repeated calculation of a reduced coefficient. First, compared to the other sampler, the accelerated diffusion sampler focuses on the dynamic changes of the coefficient during the calculation, thus reducing the number of repeated calculations. With this optimization, we implemented the accelerated diffusion sampler to generate high-quality samples faster with the same computational resources. The experimental results show that the accelerated diffusion sampler generates an increased sample speed under the same conditions compared to our implemented DDPM and DDIM samplers. In particular, the advantage of the accelerated diffusion sampler is more obvious when sampling the high-dimensional complex distribution. Through further optimization of the accelerated diffusion sampler, we believe that there is much room for its performance in practical applications. For example, further studies can be conducted on how to better balance the diffusion velocity in all directions during accelerated diffusion to improve the sampling efficiency. Moreover, the accelerated diffusion sampler can be combined with other optimization algorithms to achieve better sampling results in a wider field. In conclusion, the accelerated diffusion sampler proposed in this paper has achieved remarkable results in reducing repeated calculations and providing a new idea for efficient sampling. In the future work, we will further explore the potential of the accelerated diffusion sampler in order to exert its advantages in more practical application scenarios.