Diffusion model-based synthetic image augmentation for enhancing vehicle detection model performance
Abstract
A critical challenge for modern object detection models is the significant performance degradation encountered in adverse weather or poor lighting conditions. This issue primarily stems from the imbalance of training datasets, which are predominantly composed of normal daytime scenes, resulting in reduced robustness when models face challenging scenarios. Although traditional data augmentation techniques exist, they are limited to pixel-level adjustments and inherently restricted in generating data diversity. Consequently, this research proposes an approach utilizing diffusion models to generate realistic synthetic images that simulate new atmospheric and environmental conditions. To maximize the efficiency of synthetic data generation, we investigated a crucial factor: the quality of the reference images. We introduce a Quality-Aware Reference Selection method that filters candidates based on resolution, blur levels, and object clarity. Experiments conducted on a Thailand highway traffic surveillance dataset (14,856 images covering 14 vehicle classes) using the Qwen-Image-Edit model demonstrate that our Quality-Aware Selection method, utilizing 2,768 synthetic images, achieved a mean mAP50 of 84.5% ± 0.6%. This represents a statistically significant improvement (p < 0.05) compared to the Baseline (83.2% ± 1.0%), Random Reference Selection (83.5%), and Traditional Augmentation (84.4%), despite the latter employing a much larger set of 10,788 images. The findings of this…
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