# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 5 Conclusions  
This paper introduces DiFda, an innovative diffusion-based object detection model that builds upon and advances the noise-to-box pipeline established by DiffusionDet.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**AAPI** & **ACPI** & **AP** & \(\mathbf{AP_{50}}\) & \(\mathbf{AP_{75}}\) \\ \hline \(\times\) & \(\times\) & 58.2 & 82.7 & 63.0 \\ \(\checkmark\) & \(\times\) & 58.4 & 83.4 & 63.3 \\ \(\times\) & \(\checkmark\) & 58.3 & 83.4 & 63.2 \\ \(\checkmark\) & \(\checkmark\) & 58.7 & 83.6 & 63.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation of adaptive interaction.  
Figure 5: Our model’s inference time for the RUOD test set when using different numbers of iteration counts and sampling steps.  
DifAda implements three key improvements--adaptive noise, adaptive denoising, and adaptive interaction--which effectively tackle the challenge of dense object candidates induced by abundant noise and the issue of excessive model parameters. These advancements achieve superior detection performance while significantly reducing the model complexity. Moreover, DifAda demonstrates its versatility by achieving a flexible balance between speed and accuracy, making it adaptable to diverse application needs. Not only does DifAda excel in common scenarios, but it also achieves outstanding performance in challenging environments such as underwater detection, overcoming obstacles like light interference and object occlusion. While DifAda introduces concrete contributions, it is not yet lightweight enough for seamless deployment on resource-constrained devices. Future research could explore the further optimization of the model's computational efficiency to enable deployment on low-performance devices, such as embedded systems and mobile platforms. Additionally, investigating multi-modal learning paradigms may enhance DifAda's adaptability and broaden its applicability across diverse tasks.  
Conceptualization, [PERSON] ([PERSON]) and K.H.; methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), K.H., [PERSON] ([PERSON]), W.W., and N.W.; formal analysis, [PERSON] ([PERSON]), [PERSON] ([PERSON]), and W.W.; investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, N.W. and K.H.; visualization, [PERSON] ([PERSON]); supervision, K.H.; project administration, N.W. and K.H.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Key R&D Program, grant number 2023 AB28007.  
Not applicable.  
This research utilized two publicly available datasets: COCO (accessible at [[https://cocodataset.org](https://cocodataset.org)]([https://cocodataset.org](https://cocodataset.org)), accessed on 11 November 2023) and RUOD (accessible at [[https://github.com/dlut-dimt/RUOD](https://github.com/dlut-dimt/RUOD)]([https://github.com/dlut-dimt/RUOD](https://github.com/dlut-dimt/RUOD)), accessed on 11 November 2023). Additionally, the model code developed for this study is publicly available and can be accessed on GitHub at [[https://github.com/shau88/DifAda](https://github.com/shau88/DifAda)]([https://github.com/shau88/DifAda](https://github.com/shau88/DifAda)), accessed on 11 November 2023.  
The authors declare no conflicts of interest.