# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 1 Introduction  
Object detection is a fundamental task in computer vision with broad applications in fields like autonomous driving [1; 2; 3; 4], medical image diagnosis [5; 6; 7], and remote sensing image analysis [8; 9]. Despite significant advancements in deep learning technology, the field continues to face challenges, particularly regarding the balance between detection efficiency and accuracy. In early deep learning-based object detection methods [10; 11; 12], dense anchor boxes, often manually designed, were extensively employed to generate region proposals [13]. These anchor boxes, predefined at different scales and aspect ratios, enabled the network to predict bounding boxes for potential objects. This anchor-based approach, while effective, had inherent limitations, such as increased computational complexity and the need for the careful tuning of anchor configurations to achieve optimal performance. To address these issues, more efficient approaches have emerged, including anchor-free methods [14; 15; 16; 17], object queries [18; 19; 20; 21; 22], learnable proposals [23], and more recently, the noise-to-box approach introduced in DiffusionDet [24]. DiffusionDet redefined object detection as a generative task by progressively adding noise to the ground truth and denoising it to generate prediction boxes that approximated the ground truth. One of the key advantages of DiffusionDet is that it eliminates the need for any learnable parameters when generating object candidates, unlike query-based methods that require trainable parameters for localization. This makes DiffusionDet asimpler and more efficient approach in terms of model design. However, its reliance on completely random noise for generating object candidates presents several challenges. Due to the lack of learnable parameters, this random noise cannot provide spatial guidance like an object query, which limits its ability to accurately capture object locations. Furthermore, the initial random noise is often far from the ground truth, causing the model to rely heavily on the decoder to adjust the bounding boxes during the denoising process. Consequently, the multitude of stochastic elements introduced by the noise emerges as a significant factor undermining the efficiency of the denoising process. Building on the strengths of DiffusionDet, our proposed model, DifAda, introduces several key enhancements (adaptive noise, adaptive denoising, and adaptive interaction) to improve both the accuracy and efficiency of diffusion-based object detection. The most critical improvement is the introduction of adaptive noise, which combines random noise with latent codes. In this process, learnable parameters are used to map feature maps to latent codes, allowing the model to effectively learn positional information from the simplified feature maps. This adaptation enables the noise to be more closely aligned with actual object locations, reducing the distance between initial object candidates and the ground truth. As a result, the denoising process becomes more efficient, minimizing the need for large adjustments to the bounding boxes and thereby reducing the computational overhead. Additionally, we further improved the denoising process by introducing adaptive denoising, which simplifies the decoder structure by reducing the number of layers required to refine object candidates. This allows our decoder to flexibly adjust its sampling speed while significantly reducing the parameter count. Furthermore, we introduced an adaptive interaction mechanism to optimize feature processing within the decoder, enhancing the model's ability to handle challenging scenarios, such as small object detection. By refining interactions between feature representations and object candidates, our model efficiently improves bounding box predictions at each stage of the decoding process. These improvements streamline the detection process, providing DifAda with a balance between speed and accuracy while reducing the computational overhead.