{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T02:47:18Z","timestamp":1761965238392,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T00:00:00Z","timestamp":1615248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21051926","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T21:36:21Z","timestamp":1615325781000},"page":"1926","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Supernovae Detection with Fully Convolutional One-Stage Framework"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9248-6858","authenticated-orcid":false,"given":"Kai","family":"Yin","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8276-7640","authenticated-orcid":false,"given":"Juncheng","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China"}]},{"given":"Xing","family":"Gao","sequence":"additional","affiliation":[{"name":"Xinjiang Astronomical Observatory, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1166-3814","authenticated-orcid":false,"given":"Tianrui","family":"Sun","sequence":"additional","affiliation":[{"name":"Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023, China"},{"name":"School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026, China"}]},{"given":"Zhengyin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1093\/mnras\/stw641","article-title":"The Dark Energy Survey: More than dark energy\u2014An overview","volume":"460","author":"Abbott","year":"2016","journal-title":"Mon. 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