Robustness against real-world distribution shifts is crucial for the successful deployment of object detection models in practical applications. In this paper, we address the problem of assessing and enhancing the robustness of object detection models against natural perturbations, such as varying lighting conditions, blur, and brightness. We analyze four state-of-the-art deep neural network models, Detr-ResNet-101, Detr-ResNet-50, YOLOv4, and YOLOv4-tiny, using the COCO 2017 dataset and ExDark dataset. By simulating synthetic perturbations with the AugLy package, we systematically explore the optimal level of synthetic perturbation required to improve the models robustness through data augmentation techniques. Our comprehensive ablation study meticulously evaluates the impact of synthetic perturbations on object detection models performance against real-world distribution shifts, establishing a tangible connection between synthetic augmentation and real-world robustness. Our findings not only substantiate the effectiveness of synthetic perturbations in improving model robustness, but also provide valuable insights for researchers and practitioners in developing more robust and reliable object detection models tailored for real-world applications.

翻译：针对真实世界分布偏移的鲁棒性对于目标检测模型在实际应用中的成功部署至关重要。本文旨在评估并提升目标检测模型对自然扰动（如光照变化、模糊及亮度变化）的鲁棒性。我们使用COCO 2017数据集和ExDark数据集，分析了四种先进深度神经网络模型：Detr-ResNet-101、Detr-ResNet-50、YOLOv4和YOLOv4-tiny。通过AugLy包模拟合成扰动，我们系统探讨了通过数据增强技术提升模型鲁棒性所需的最优合成扰动水平。全面消融研究细致评估了合成扰动对目标检测模型在应对真实世界分布偏移时性能的影响，建立了合成增强与真实鲁棒性之间的实际联系。研究结果不仅验证了合成扰动在提升模型鲁棒性方面的有效性，还为研究人员和从业者开发更鲁棒、更可靠且适用于真实应用场景的目标检测模型提供了宝贵见解。