# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 4 Experiments
### Experimental Results  
We compare our model with common state-of-the-art detectors used on the COCO dataset. To ensure fairness, we also reproduce the results of other advanced detectors used on the RUOD dataset and compare them with our model. In our experimental setup, the number of noises (initial proposal box) was set to 300 for the COCO dataset and 100 for the RUOD dataset. Models with settings differing from the default configuration are specifically annotated.  
In Table 1, we present the performance comparison of our proposed DifAda method with various state-of-the-art detectors used on the COCO 2017 validation set. Using ResNet-50 with the FPN as the image encoder and a default setting of 300 noises, DifAda achieves 46.4% AP, surpassing the baseline model DiffusionDet, which only achieves 45.5% AP with 500 noises. This indicates that DifAda requires fewer noises while maintaining superior accuracy, demonstrating the effectiveness of our adaptive noise mechanism in diffusion-based detectors. Thanks to the adaptive denoising structure, our model significantly reduces the parameter count, requiring only 50 M parameters compared to the baseline's 111 M, which greatly enhances spatial efficiency. And under the same image decoder conditions, our model outperforms many established detectors. Compared to Faster R-CNN [13], Cascade R-CNN [43], and Sparse R-CNN [23], DifAda achieves improvements of 6.1%, 2.1%, and 1.4% AP, respectively, with improvements across all accuracy metrics. Additionally, our model's parameter count is significantly lower than these models, especially when compared to Sparse R-CNN and DiffusionDet, reducing parameters by 56 M and 61 M, respectively--this reduction is greater than the total parameter count of our model. When compared to transformer-based detectors, our model also performs well. DifAda achieves improvements of 4.4% and 2.1% AP over DETR [21] and deformable DETR [20], respectively. This demonstrates that our method not only achieves superior performance but also offers significant efficiency advantages.  
Table 2 extends our analysis to the RUOD dataset, which presents a variety of underwater detection challenges, including haze, color distortion, light interference, and the presence of complex marine life. These conditions make accurate detection particularly difficult, but DifAda excels across all evaluation metrics. Using the same evaluation metrics as COCO, DifAda achieves improvements of 2.9% and 1.2% AP, respectively, compared to Sparse R-CNN and DiffusionDet. Compared with some classic methods, although the ResNeXt-101 used in reproducing them is a more advanced variant of ResNet, our decision to not modify and to continue using ResNet-50 demonstrates that DifAda can provide superior performance even without taking advantage of the latest backbone architectures. This proves the superiority of our model architecture and design, enabling it to outperform models that adopt more advanced backbone networks, such as FCOS, Faster R-CNN, Cascade R-CNN, and Libra R-CNN, with improvements of 8.0%, 5.9%, 3.9%, and 3.9% AP, respectively.