Robustness of Object Detectors in Degrading Weather Conditions

TL;DR

This paper evaluates the robustness of object detection systems in autonomous driving under adverse weather conditions, revealing significant performance drops and limitations of current architectures in rain, fog, and snow.

Contribution

It provides a detailed evaluation of single and dual modality object detectors in real degrading weather conditions, highlighting their vulnerabilities.

Findings

Performance degrades significantly in adverse weather.

Single and dual modality architectures have limitations in bad weather.

Ablation studies reveal specific weaknesses in dual modality systems.

Abstract

State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions. However, such autonomous safety critical systems also need to work in degrading weather conditions, such as rain, fog and snow. Unfortunately, most approaches evaluate only on the KITTI dataset, which consists only of clear weather scenes. In this paper we address this issue and perform one of the most detailed evaluation on single and dual modality architectures on data captured in real weather conditions. We analyse the performance degradation of these architectures in degrading weather conditions. We demonstrate that an object detection architecture performing good in clear weather might not be able to handle degrading weather conditions. We also perform ablation studies on the dual modality architectures and show their limitations.