An Extended Evaluation Split for DeepSpaceYoloDataset

arXiv cs.CV / 5/1/2026

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Key Points

The paper announces an updated version of the DeepSpaceYoloDataset, a 2023 annotated dataset for training YOLO-based models to detect deep-sky objects.
The key change is the addition of a new evaluation split named test2026.
The new test split is intended to evaluate detection models using a more diverse set of images, improving assessment beyond prior splits.
The work is positioned in the context of smart telescopes enabling more accessible, widely deployable astronomical detection solutions.

Abstract

Recent technological advances in astronomy, particularly the growing popularity of smart telescopes for the general public, make it possible to develop highly effective detection solutions that are accessible to a wide audience, rather than being reserved for major scientific observatories. Published in 2023, DeepSpaceYoloDataset is a collection of annotated images created to train YOLO-based models for detecting Deep Sky Objects, particularly suited for Electronically Assisted Astronomy. In this paper, we present an update to DeepSpaceYoloDataset with the addition of a new split, test2026, designed to evaluate detection models with a greater diversity of images.