Journal of Imaging, Volume 11, Issue 6
2025 June - 34 articles
Cover Story: This study introduces a large, manually curated dataset of over 5,000 telescope images collected from amateur astronomers via an online community. Each image was meticulously filtered, annotated, and categorized into comets, galaxies, nebulae, and globular clusters, resulting in a robust resource for training deep learning models. Using this dataset, we conducted a comparative evaluation of different architectures, with a special focus on YOLO-based models, providing insights into their performance in real-world astronomical-object-detection scenarios. View this paper
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J. Imaging - ISSN 2313-433X