Low-light image/video analysis is essential for various applications, e.g., night surveillance and photography, high-speed imaging, and autonomous vehicles. Under such conditions, cameras suffer from low signal-to-noise ratio, which degrades image quality severely and poses challenges for downstream tasks such as object detection. Data-driven methods have achieved enormous success for normal-light image/video restoration and high-level vision tasks. However, the lack of a high-quality benchmark dataset with accurate semantic annotations for low-light images and especially videos greatly hinders research progress. In this paper, we contribute the first multi-illuminance, multi-camera, low-light dataset, DarkVision, serving both image/video enhancement and object detection applications. We provide bright and dark pairs with pixel-wise registration, in which the bright counterpart provides a reliable reference for enhancement and annotation. This dataset comprises 13,455 images of 900 static scenes with objects from 15 categories, and 89,411 frames of 32 dynamic scenes with 4 categories of objects. For each scene, images/videos were captured at 5 illuminance levels using three cameras of different quality grades; average photon numbers can be reliably estimated from the calibration curves for quantitative studies. The static images and dynamic videos respectively contain around 7344 and 320,667 object instances in total. With DarkVision, we establish baselines for image/video enhancement and object detection by representative algorithms. To demonstrate an exemplary application of DarkVision, we propose two simple yet effective approaches to improve the performance of video enhancement and object detection respectively by exploiting temporal cues. Furthermore, we study the relationship between image enhancement and object detection. We believe DarkVision can help to advance the state-of-the art in both low-light image/video enhancement and object detection, as well as benefiting cross-task studies.
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