Jia Xinyu ; Zhu Chuang ; Li Minzhen ; Tang Wenqi ; Liu Shengjie ; Zhou Wenli

Abstract

It is very challenging for various visual tasks such as image fusion,pedestrian detection and image-to-image translation in low light conditions dueto the loss of effective target areas. In this case, infrared and visibleimages can be used together to provide both rich detail information andeffective target areas. In this paper, we present LLVIP, a visible-infraredpaired dataset for low-light vision. This dataset contains 30976 images, or15488 pairs, most of which were taken at very dark scenes, and all of theimages are strictly aligned in time and space. Pedestrians in the dataset arelabeled. We compare the dataset with other visible-infrared datasets andevaluate the performance of some popular visual algorithms including imagefusion, pedestrian detection and image-to-image translation on the dataset. Theexperimental results demonstrate the complementary effect of fusion on imageinformation, and find the deficiency of existing algorithms of the three visualtasks in very low-light conditions. We believe the LLVIP dataset willcontribute to the community of computer vision by promoting image fusion,pedestrian detection and image-to-image translation in very low-lightapplications. The dataset is being released inhttps://bupt-ai-cz.github.io/LLVIP. Raw data is also provided for furtherresearch such as image registration.