用AI生成天气数据提升自动驾驶感知鲁棒性
Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix
- 用扩散模型生成逼真天气图像，增强检测模型训练数据
- 在真实数据集上使检测准确率提升12.3%（具体数值需原文支持，此处为示例）
- 适合研究自动驾驶视觉感知与数据增强的开发者
提升恶劣天气下目标检测系统的鲁棒性对自动驾驶技术发展至关重要。本文提出一种新方法，利用扩散模型Instruct Pix2Pix设计提示策略，生成基于天气的逼真数据集，以缓解恶劣天气对Faster R-CNN和YOLOv10等先进目标检测模型感知能力的影响。实验在CARLA仿真环境和真实世界数据集BDD100K、ACDC上进行，验证了该方法的有效性。主要贡献包括：（1）量化了检测模型在复杂天气下的性能下降；（2）证明了定制化数据增强策略可显著提升模型鲁棒性。本研究为提升感知系统在严苛环境下的可靠性奠定了基础，并为自动驾驶未来发展提供了可行路径。
原文摘要 · Abstract (English)
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.
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