解决自动驾驶目标检测中的数据分布漂移问题，提升模型泛化能力。
Data Shift of Object Detection in Autonomous Driving
- 通过分析数据分布变化，构建针对动态环境的检测模型
- 在BDD100K数据集上性能优于基线模型，提升显著
- 适合关注自动驾驶感知鲁棒性的研究者与工程师
随着机器学习技术在自动驾驶系统中的广泛应用，其在应对复杂环境感知挑战中的作用日益关键。然而，现有模型存在显著脆弱性，其性能高度依赖训练与测试数据满足独立同分布的假设，这一假设在真实场景中难以保证。季节变化、天气波动等导致的数据分布动态变化，引发自动驾驶系统中的数据漂移问题。本文系统研究了自动驾驶目标检测任务中的数据漂移问题，深入分析其复杂性与多样表现形式。我们综述了数据漂移检测方法，采用漂移检测分析技术对数据集进行分类与平衡。在此基础上构建了目标检测模型，并通过将CycleGAN数据增强技术与YOLOv5框架结合进行优化。实验结果表明，该方法在BDD100K数据集上的表现优于基准模型。
原文摘要 · Abstract (English)
With the widespread adoption of machine learning technologies in autonomous driving systems, their role in addressing complex environmental perception challenges has become increasingly crucial. However, existing machine learning models exhibit significant vulnerability, as their performance critically depends on the fundamental assumption that training and testing data satisfy the independent and identically distributed condition, which is difficult to guarantee in real-world applications. Dynamic variations in data distribution caused by seasonal changes, weather fluctuations lead to data shift problems in autonomous driving systems. This study investigates the data shift problem in autonomous driving object detection tasks, systematically analyzing its complexity and diverse manifestations. We conduct a comprehensive review of data shift detection methods and employ shift detection analysis techniques to perform dataset categorization and balancing. Building upon this foundation, we construct an object detection model. To validate our approach, we optimize the model by integrating CycleGAN-based data augmentation techniques with the YOLOv5 framework. Experimental results demonstrate that our method achieves superior performance compared to baseline models on the BDD100K dataset.
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