Robustness of Object Detectors in Degrading Weather Conditions
This addresses a critical safety issue for autonomous driving systems, but it is incremental as it focuses on evaluation rather than proposing new methods.
The paper tackles the problem of object detectors failing in degrading weather conditions like rain, fog, and snow, showing that architectures performing well in clear weather often degrade significantly, with detailed evaluations revealing limitations in dual-modality approaches.
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.