Enhancing Rare Object Detection on Roadways Through Conditional Diffusion Models for Data Augmentation

The detection of rare objects within traffic environments is critically important for the safety and reliability of autonomous driving systems. However, the scarcity of sufficient data and the long-tail effect associated with rare objects pose significant challenges to their detection. To address this issue, this study introduces a Conditional Diffusion Model, the RareGenDiffuse Model, which generates images of rare objects by taking traffic background images and corresponding bounding box images as inputs. Based on diffusion models, the RareGenDiffuse Model enables control over the category and location of the generated rare objects by adjusting the bounding box images. Experimental results demonstrate that the proposed model offers the advantages of high clarity and controllability. Utilizing the generated rare object dataset significantly enhances the detection outcomes for rare objects. The methodology proposed in this study can effectively reduce the efforts required for data acquisition and labeling, presenting a valuable solution to the challenges of rare object detection in traffic scenarios.