Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix
This work addresses the reliability of perception systems for autonomous vehicles in challenging weather, representing an incremental improvement through tailored data augmentation.
This study tackled the problem of object detection in adverse weather conditions for autonomous vehicles by using Instruct Pix2Pix to generate realistic weather-augmented datasets, resulting in enhanced robustness of models like Faster R-CNN and YOLOv10 as demonstrated on BDD100K and ACDC datasets.
Enhancing the robustness of object detection systems under adverse weather conditions is crucial for the advancement of autonomous driving technology. This study presents a novel approach leveraging the diffusion model Instruct Pix2Pix to develop prompting methodologies that generate realistic datasets with weather-based augmentations aiming to mitigate the impact of adverse weather on the perception capabilities of state-of-the-art object detection models, including Faster R-CNN and YOLOv10. Experiments were conducted in two environments, in the CARLA simulator where an initial evaluation of the proposed data augmentation was provided, and then on the real-world image data sets BDD100K and ACDC demonstrating the effectiveness of the approach in real environments. The key contributions of this work are twofold: (1) identifying and quantifying the performance gap in object detection models under challenging weather conditions, and (2) demonstrating how tailored data augmentation strategies can significantly enhance the robustness of these models. This research establishes a solid foundation for improving the reliability of perception systems in demanding environmental scenarios, and provides a pathway for future advancements in autonomous driving.