RANUS: RGB and NIR Urban Scene Dataset for Deep Scene Parsing

Abstract

In this letter, we present a data-driven method for scene parsing of road scenes to utilize single-channel near-infrared (NIR) images. To overcome the lack of data problem in non-RGB spectrum, we define a new color space and decompose the task of deep scene parsing into two subtasks with two separate CNN architectures for chromaticity channels and semantic masks. For chromaticity estimation, we build a spatially-aligned RGB-NIR image database (40k urban scenes) to infer color information from RGB-NIR spectrum learning process and leverage existing scene parsing networks trained over already available RGB masks. From our database, we sample key frames and manually annotate them (4k ground truth masks) to finetune the network into the proposed color space. Hence, the key contribution of this work is to replace multispectral scene parsing methods with a simple yet effective approach using single NIR images. The benefits of using our algorithm and dataset are confirmed in the qualitative and quantitative experiments.