对比三模型在哈萨克斯坦复杂路况下的泛化能力，发现RT-DETR表现最佳。
Domain Generalization in Autonomous Driving: Evaluating YOLOv8s, RT-DETR, and YOLO-NAS with the ROAD-Almaty Dataset
- 用新数据集测试三模型跨域检测性能，未重新训练
- RT-DETR在IoU=0.5时F1达0.672，优于YOLOv8s和YOLO-NAS约46%和27%
- 恶劣天气与高精度要求下性能显著下降，适合关注自动驾驶泛化问题的研究者
本研究评估了三种先进目标检测模型——YOLOv8s、RT-DETR和YOLO-NAS——在哈萨克斯坦独特驾驶环境中的域泛化能力。基于新构建的ROAD-Almaty数据集，该数据集涵盖多样化的天气、光照和交通条件，模型在未经过任何微调的情况下进行评估。定量分析显示，RT-DETR在IoU=0.5时平均F1-score达到0.672，分别比YOLOv8s（0.458）和YOLO-NAS（0.526）高出约46%和27%。此外，所有模型在更高IoU阈值（如从0.5提升至0.75）及极端环境（如大雪、低光）下均出现明显性能下降，降幅约20%。研究强调了地理多样性训练数据的重要性，以及实施专门域自适应技术以提升全球自动驾驶系统可靠性的必要性。该工作有助于理解自动驾驶中域泛化挑战，尤其在代表性不足地区。
原文摘要 · Abstract (English)
This study investigates the domain generalization capabilities of three state-of-the-art object detection models - YOLOv8s, RT-DETR, and YOLO-NAS - within the unique driving environment of Kazakhstan. Utilizing the newly constructed ROAD-Almaty dataset, which encompasses diverse weather, lighting, and traffic conditions, we evaluated the models' performance without any retraining. Quantitative analysis revealed that RT-DETR achieved an average F1-score of 0.672 at IoU=0.5, outperforming YOLOv8s (0.458) and YOLO-NAS (0.526) by approximately 46% and 27%, respectively. Additionally, all models exhibited significant performance declines at higher IoU thresholds (e.g., a drop of approximately 20% when increasing IoU from 0.5 to 0.75) and under challenging environmental conditions, such as heavy snowfall and low-light scenarios. These findings underscore the necessity for geographically diverse training datasets and the implementation of specialized domain adaptation techniques to enhance the reliability of autonomous vehicle detection systems globally. This research contributes to the understanding of domain generalization challenges in autonomous driving, particularly in underrepresented regions.
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