DeepSpaceYoloDataset: Annotated Astronomical Images Captured with Smart Telescopes

Data Pub Date : 2024-01-10 DOI:10.3390/data9010012
Olivier Parisot
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Abstract

Recent smart telescopes allow the automatic collection of a large quantity of data for specific portions of the night sky—with the goal of capturing images of deep sky objects (nebula, galaxies, globular clusters). Nevertheless, human verification is still required afterwards to check whether celestial targets are effectively visible in the images produced by these instruments. Depending on the magnitude of deep sky objects, the observation conditions and the cumulative time of data acquisition, it is possible that only stars are present in the images. In addition, unfavorable external conditions (light pollution, bright moon, etc.) can make capture difficult. In this paper, we describe DeepSpaceYoloDataset, a set of 4696 RGB astronomical images captured by two smart telescopes and annotated with the positions of deep sky objects that are effectively in the images. This dataset can be used to train detection models on this type of image, enabling the better control of the duration of capture sessions, but also to detect unexpected celestial events such as supernova.
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DeepSpaceYoloDataset:智能望远镜拍摄的带注释的天文图像
最新的智能望远镜可以自动收集夜空特定部分的大量数据,目的是捕捉深空天体(星云、星系、球状星团)的图像。不过,事后仍需要人工验证,以检查这些仪器生成的图像中是否能有效地看到天体目标。根据深空天体的亮度、观测条件和数据采集的累积时间,图像中可能只有恒星。此外,不利的外部条件(光污染、明月等)也会给拍摄带来困难。在本文中,我们介绍了 DeepSpaceYoloDataset,这是一组由两台智能望远镜拍摄的 4696 张 RGB 天文图像,并标注了图像中有效的深空天体的位置。该数据集可用于在这类图像上训练检测模型,从而更好地控制捕捉会话的持续时间,还可用于检测超新星等突发天体事件。
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