An Extended Evaluation Split for DeepSpaceYoloDataset

TL;DR

This paper introduces an extended evaluation split for the DeepSpaceYoloDataset, enhancing its ability to assess deep sky object detection models with more diverse images for Electronically Assisted Astronomy.

Contribution

It presents a new test2026 split for the DeepSpaceYoloDataset, improving model evaluation by increasing image diversity.

Findings

The new split enables more comprehensive model testing.

Enhanced dataset diversity improves detection robustness.

Supports development of accessible astronomy detection tools.

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.