Piratinskii Evgenii, Rabaev Irina
Software Engineering Department, Shamoon College of Engineering, 56 Bialik St., Be'er Sheva 8410802, Israel.
J Imaging. 2025 Jun 4;11(6):184. doi: 10.3390/jimaging11060184.
Accurate and efficient detection of celestial objects in telescope imagery is a fundamental challenge in both professional and amateur astronomy. Traditional methods often struggle with noise, varying brightness, and object morphology. This paper introduces COSMICA, a novel, curated dataset of manually annotated astronomical images collected from amateur observations. COSMICA enables the development and evaluation of real-time object detection systems intended for practical deployment in observational pipelines. We investigate three modern YOLO architectures, YOLOv8, YOLOv9, and YOLOv11, and two additional object detection models, EfficientDet-Lite0 and MobileNetV3-FasterRCNN-FPN, to assess their performance in detecting comets, galaxies, nebulae, and globular clusters. All models are evaluated using consistent experimental conditions across multiple metrics, including mAP, precision, recall, and inference speed. YOLOv11 demonstrated the highest overall accuracy and computational efficiency, making it a promising candidate for real-world astronomical applications. These results support the feasibility of integrating deep learning-based detection systems into observational astronomy workflows and highlight the importance of domain-specific datasets for training robust AI models.
在专业和业余天文学领域，准确高效地检测望远镜图像中的天体是一项基本挑战。传统方法在处理噪声、亮度变化和物体形态方面常常面临困难。本文介绍了COSMICA，这是一个从业余观测中收集的、经过精心整理的手动标注天文图像的新颖数据集。COSMICA有助于开发和评估旨在实际部署于观测流程中的实时物体检测系统。我们研究了三种现代YOLO架构，即YOLOv8、YOLOv9和YOLOv11，以及另外两种物体检测模型，EfficientDet-Lite0和MobileNetV3-FasterRCNN-FPN，以评估它们在检测彗星、星系、星云和球状星团方面的性能。所有模型均在包括平均精度均值（mAP）、精确率、召回率和推理速度等多个指标的一致实验条件下进行评估。YOLOv11展现出最高的总体准确率和计算效率，使其成为实际天文应用的一个有前景的候选方案。这些结果支持了将基于深度学习的检测系统集成到观测天文学工作流程中的可行性，并凸显了特定领域数据集对于训练强大人工智能模型的重要性。
