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NEA Detection Method with Neural Network in Sidereal Tracking

文献类型：期刊论文

作者Tang, Yijun3,4; Jiang, Yunxiao3,4; Zhang, Zhen3,4; Ying, Chenchen3,4; Zhang, Songqi3,4; Liao, Liangcheng3,4; Ma, Junjie3,4; Yan, Bo3,4; Bai, Chunhai1; Feng, Guojie1
刊名RESEARCH IN ASTRONOMY AND ASTROPHYSICS
出版日期2025-09-01
卷号25期号:9页码:095003
关键词methods: data analysis techniques: image processing minor planets, asteroids: general planets and satellites: detection
ISSN号1674-4527
DOI10.1088/1674-4527/ade491
产权排序3
英文摘要Near-Earth Asteroids posed a threat to human civilization, making their monitoring crucial. As the demand for asteroid detection technology increased, precise detection of these celestial bodies became an urgent task to understand their characteristics and assess potential impact risks. To improve asteroid detection accuracy and efficiency, we proposed an advanced image processing method and a deep learning network for automatic asteroid detection. Specifically, we aligned star clusters and overlaid images to exploit asteroid motion rates, transforming them into object-like trajectories and improving the signal-to-noise ratio. This approach created the Asteroid Trajectory Image Data set under various conditions. We modified CenterNet2 network to develop AstroCenterNet by integrating Multi-channel Histogram Truncation for feature enhancement, using the SimAM attention mechanism to expand contextual information and suppress noise, and refining Feature Pyramid Network to improve low-level feature detection. Our results demonstrated a detection accuracy of 98.4%, a recall of 97.6%, a mean Average Precision of 94.01%, a false alarm rate of 1.6%, and a processing speed of approximately 17.86 frames per second, indicating that our method achieves high precision and efficiency.
WOS关键词SPACE-DEBRIS ; OBJECT DETECTION ; WIDE-FIELD ; ALGORITHMS ; FAINT
资助项目National Science and Technology Major Project[2022ZD0117401] ; National Defense Science and Technology Innovation Special Zone Project Foundation of China[19-163-21-TS-001-067-01] ; Chinese Academy of Sciences (CAS) Light of West China Program[2020-XBQNXZ-016] ; Chinese Academy of Sciences (CAS) Light of West China Program[N87]
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001526930600001
出版者IOP Publishing Ltd
资助机构National Science and Technology Major Project ; National Defense Science and Technology Innovation Special Zone Project Foundation of China ; Chinese Academy of Sciences (CAS) Light of West China Program
源URL[http://ir.xao.ac.cn/handle/45760611-7/7919]  
专题光学天文与技术应用研究室_光学天文技术研究团组
通讯作者Tang, Yijun
作者单位1.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China
2.Chinese Acad Sci, Natl Astron Observ, CAS Key Lab Opt Astron, Beijing 100101, Peoples R China
3.Zhejiang Univ Technol, Sch Phys, Hangzhou 310023, Peoples R China
4.Zhejiang Univ Technol, Collaborat Innovat Ctr Biomed Phys Informat Techno, Hangzhou 310023, Peoples R China
推荐引用方式
GB/T 7714		 Tang, Yijun,Jiang, Yunxiao,Zhang, Zhen,et al. NEA Detection Method with Neural Network in Sidereal Tracking[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2025,25(9):095003.
APA Tang, Yijun.,Jiang, Yunxiao.,Zhang, Zhen.,Ying, Chenchen.,Zhang, Songqi.,...&Jiang, Xiaojun.(2025).NEA Detection Method with Neural Network in Sidereal Tracking.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,25(9),095003.
MLA Tang, Yijun,et al."NEA Detection Method with Neural Network in Sidereal Tracking".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 25.9(2025):095003.

入库方式： OAI收割

来源：新疆天文台

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