Supernovae Detection with Fully Convolutional One-Stage Framework
文献类型：期刊论文
| 作者 | Yin, Kai1; Jia, Juncheng1,2; Gao, Xing3; Sun, Tianrui4,5; Zhou, Zhengyin1 |
| 刊名 | SENSORS
 |
| 出版日期 | 2021-03-01 |
| 卷号 | 21期号:5页码:1926 |
| 关键词 | image processing data analysis sky surveys supernova object detection |
| DOI | 10.3390/s21051926 |
| 产权排序 | 3 |
| 英文摘要 | 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. |
| WOS关键词 | SYNOPTIC SURVEY TELESCOPE |
| 资助项目 | 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研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000628545400001 |
| 出版者 | MDPI |
| 资助机构 | China Postdoctoral Science Foundation ; Collaborative Innovation Center of Novel Software Technology and Industrialization ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/3974]  |
| 专题 | 研究单元未命名 |
| 通讯作者 | Jia, Juncheng |
| 作者单位 | 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：新疆天文台
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