Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix

TL;DR

This paper introduces a novel data augmentation approach using Instruct Pix2Pix diffusion models to improve object detection robustness in adverse weather conditions for autonomous vehicles, validated in simulation and real-world datasets.

Contribution

It proposes a weather-based data augmentation methodology with Instruct Pix2Pix to enhance object detection under challenging conditions, a novel application in autonomous driving.

Findings

Augmentation reduces performance gap in adverse weather detection.

Method improves robustness of Faster R-CNN and YOLOv10 models.

Effective in both simulated and real-world datasets.

Abstract

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.