为天文目标检测数据集新增测试集，提升模型评估多样性。
An Extended Evaluation Split for DeepSpaceYoloDataset
- 构建新测试集test2026，覆盖更广泛图像类型
- 支持电子辅助天文观测的检测模型评估
- 适合天文图像检测与开源项目开发者
近年来天文学技术进步，特别是智能望远镜在公众中的普及，使得开发对大众友好的高效检测方案成为可能，而非仅限于大型科研观测站。2023年发布的DeepSpaceYoloDataset是一组标注图像数据集，旨在训练基于YOLO的模型以检测深空天体，特别适用于电子辅助天文观测（Electronically Assisted Astronomy）。本文提出对DeepSpaceYoloDataset的扩展，新增test2026测试集，旨在通过更丰富的图像多样性评估目标检测模型的性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。