Time to Shine: Fine-Tuning Object Detection Models with Synthetic Adverse Weather Images

The detection of vehicles, pedestrians, and obstacles plays an important role in the decision-making process of autonomous vehicles. While existing methods achieve high detection accuracy under good environmental conditions, they often fail in adverse weather conditions due to limited visibility, blurred contours, and low contrast. These "edge-case"scenarios are not well represented in existing datasets and are not handled properly by object detection algorithms. In our work, we propose a novel approach to synthesising photorealistic and highly diverse scenarios that can be used to fine-tune object detection algorithms in adverse weather conditions such as snow, fog, and rain. The approach uses the Midjourney text-to-image model to create accurate synthetic images of desired weather conditions. Our experiments show that training with our dataset significantly improves detection accuracy in harsh weather conditions. Our results are compared to baseline models and models fine-tuned on augmented clear weather images.