Robustness of Object Detectors in Degrading Weather Conditions

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.• The performance should degrade gracefully with changing/degrading weather conditions.

Robust 3D Object Detection in Cold Weather Conditions

“…In case of adverse weather conditions, the process of labeling can be extremely laborious, time consuming and expensive, due to the sparsity of point clouds and the irregularities of these effects. Moreover, object detectors trained in good weather conditions tend to perform poorly in adverse weather [3], [4]. Finding a general strategy to increase object detection robustness without changing a network structure is not trivial due to different possible architectures that can be used.…”