RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes
This dataset addresses the problem of limited training data for machine perception in rainy conditions for autonomous driving systems, but it is incremental as it builds upon existing street scene datasets.
The authors introduced RaidaR, a large annotated dataset of rainy street scenes to support autonomous driving research, containing 58,542 images with semantic and instance segmentations, and demonstrated its utility by improving segmentation accuracy through data augmentation.
We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research. The new dataset contains the largest number of rainy images (58,542) to date, 5,000 of which provide semantic segmentations and 3,658 provide object instance segmentations. The RaidaR images cover a wide range of realistic rain-induced artifacts, including fog, droplets, and road reflections, which can effectively augment existing street scene datasets to improve data-driven machine perception during rainy weather. To facilitate efficient annotation of a large volume of images, we develop a semi-automatic scheme combining manual segmentation and an automated processing akin to cross validation, resulting in 10-20 fold reduction on annotation time. We demonstrate the utility of our new dataset by showing how data augmentation with RaidaR can elevate the accuracy of existing segmentation algorithms. We also present a novel unpaired image-to-image translation algorithm for adding/removing rain artifacts, which directly benefits from RaidaR.