用高性能计算提升深空天体检测模型的鲁棒性。
Robustness analysis of Deep Sky Objects detection models on HPC
- 基于HPC并行化训练与对比YOLO、RET-DETR模型。
- 在智能望远镜图像上实现深空天体检测，提升稳定性。
- 适合天文数据自动化处理研究者参考。
天文巡天和业余天文爱好者的参与正产生前所未有的天空图像，亟需准确且鲁棒的自动化处理方法。深空天体（如星系、星云和星团）因信号微弱且背景复杂，检测仍具挑战性。计算机视觉与深度学习的进步为该任务提供了新可能。本文利用高性能计算（HPC）并行化计算，训练并比较了不同检测模型（YOLO、RET-DETR）在智能望远镜图像上的表现，重点开展鲁棒性测试。
原文摘要 · Abstract (English)
Astronomical surveys and the growing involvement of amateur astronomers are producing more sky images than ever before, and this calls for automated processing methods that are accurate and robust. Detecting Deep Sky Objects -- such as galaxies, nebulae, and star clusters -- remains challenging because of their faint signals and complex backgrounds. Advances in Computer Vision and Deep Learning now make it possible to improve and automate this process. In this paper, we present the training and comparison of different detection models (YOLO, RET-DETR) on smart telescope images, using High-Performance Computing (HPC) to parallelise computations, in particular for robustness testing.
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。