Enhancing object detection robustness: A synthetic and natural perturbation approach

TL;DR

This paper investigates how combining synthetic and natural perturbations can improve the robustness of object detection models against real-world distribution shifts, through systematic analysis and data augmentation techniques.

Contribution

It introduces a comprehensive study on using synthetic perturbations to enhance object detection robustness against natural variations, with detailed ablation analysis.

Findings

Synthetic perturbations improve model robustness against real-world shifts.

Optimal synthetic perturbation levels enhance detection performance.

Synthetic augmentation significantly benefits practical object detection applications.

Abstract

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.