KMS of Xinjiang Astronomical Observatory, CAS
| Supernovae Detection with Fully Convolutional One-Stage Framework | |
| Yin, Kai1; Jia, Juncheng1,2; Gao, Xing3; Sun, Tianrui4,5; Zhou, Zhengyin1 | |
| 2021-03-01 | |
| Source Publication | SENSORS
 |
| Volume | 21Issue:5Pages:1926 |
| Contribution Rank | 3 |
| Abstract | A series of sky surveys were launched in search of supernovae and generated a tremendous amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous burden to manually identify and report supernovae, because such data have huge quantity and sparse positives. While the traditional machine learning methods can be used to deal with such data, deep learning methods such as Convolutional Neural Networks demonstrate more powerful adaptability in this area. However, most data in the existing works are either simulated or without generality. How do the state-of-the-art object detection algorithms work on real supernova data is largely unknown, which greatly hinders the development of this field. Furthermore, the existing works of supernovae classification usually assume the input images are properly cropped with a single candidate located in the center, which is not true for our dataset. Besides, the performance of existing detection algorithms can still be improved for the supernovae detection task. To address these problems, we collected and organized all the known objectives of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and the Popular Supernova Project (PSP), resulting in two datasets, and then compared several detection algorithms on them. After that, the selected Fully Convolutional One-Stage (FCOS) method is used as the baseline and further improved with data augmentation, attention mechanism, and small object detection technique. Extensive experiments demonstrate the great performance enhancement of our detection algorithm with the new datasets. |
| Keyword | image processing data analysis sky surveys supernova object detection |
| DOI | 10.3390/s21051926 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | SYNOPTIC SURVEY TELESCOPE |
| Funding Project | China Postdoctoral Science Foundation[2017M611905] ; Collaborative Innovation Center of Novel Software Technology and Industrialization ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) |
| WOS Research Area | Chemistry ; Engineering ; Instruments & Instrumentation |
| WOS Subject | Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| WOS ID | WOS:000628545400001 |
| Publisher | MDPI |
| Funding Organization | China Postdoctoral Science Foundation ; Collaborative Innovation Center of Novel Software Technology and Industrialization ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/3975 |
| Collection | 中国科学院新疆天文台 |
| Corresponding Author | Jia, Juncheng |
| Affiliation | 1.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China 2.Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 210000, Peoples R China 3.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 4.Chinese Acad Sci, Purple Mt Observ, Nanjing 210023, Peoples R China 5.Univ Sci & Technol China, Sch Astron & Space Sci, Hefei 230026, Peoples R China |
| Recommended Citation GB/T 7714 | Yin, Kai,Jia, Juncheng,Gao, Xing,et al. Supernovae Detection with Fully Convolutional One-Stage Framework[J]. SENSORS,2021,21(5):1926. |
| APA | Yin, Kai,Jia, Juncheng,Gao, Xing,Sun, Tianrui,&Zhou, Zhengyin.(2021).Supernovae Detection with Fully Convolutional One-Stage Framework.SENSORS,21(5),1926. |
| MLA | Yin, Kai,et al."Supernovae Detection with Fully Convolutional One-Stage Framework".SENSORS 21.5(2021):1926. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Yin-2021-Supernovae （2771KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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