Getting to know low-light images with the Exclusively Dark dataset - Loh, Yuen Peng; Chan, Chee Seng (Computer vision and image understanding, 2019)
Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light imagery have seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as a benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought. It consists exclusively of low-light images captured in visible light only, with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing the visualizations of both hand-crafted and learned features. We found that the effects of low-light reach far deeper into the features than can be solved by simple “illumination invariance”. It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The dataset can be downloaded at https://github.com/cs-chan/Exclusively-Dark-Image-Dataset. •A new lowlight image only dataset, the Exclusively DARK is proposed.•Dataset contains 10 lowlight illumination types and annotation of 12 object classes.•Analysis of lowlight images with handcrafted and learned features of object detection.•Lowlight presents illumination challenges that is fundamentally different from bright.•Dataset can be the go-to database to benchmark low-light domain research.