  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.
