KMS of Xinjiang Astronomical Observatory, CAS
| Harnessing edge-enhanced attention mechanisms for supernova detection in deep learning frameworks | |
| Yin, K.1; Jia, J.1,2; Li, F.1; Gao, X.3; Sun, T.4 | |
| 2024 | |
| Source Publication | ASTRONOMY AND COMPUTING
 |
| ISSN | 2213-1337 |
| Volume | 46Pages:100784 |
| Contribution Rank | 3 |
| Abstract | Recent studies have shown the advantages of convolutional neural networks in the classification and detection of supernovae. In our prior work, we employed one-stage object detection frameworks to address the challenges of presupposed location and varying image sizes in supernova detection. Notably, the backbone of the object detectors naturally emphasized the edges of candidate regions in the visualized heatmap, reflecting the strategies adopted by human observers. Capitalizing on this similarity, we introduce an innovative edge attention module, tailored to prioritize the edges of candidate regions, and improved the performance of supernova detectors. In parallel, we have developed a three-channel supernova detection dataset by integrating science (current), template (reference), and difference images into a three-channel configuration. The candidates in the new dataset are more conspicuous. To assess the efficacy of our edge attention module, we conducted a series of experiments on the proposed dataset. The experimental results establish the superiority of the proposed method in detecting supernovae. Additionally, visualizations of the feature maps shows the proposed edge attention is able to reallocate weights around the candidate edges, corroborating its effectiveness. |
| Keyword | Object detection Edge attention Data augmentation Supernova detection Sky survey |
| DOI | 10.1016/j.ascom.2023.100784 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | SYNOPTIC SURVEY TELESCOPE |
| WOS Research Area | Astronomy & Astrophysics ; Computer Science |
| WOS Subject | Astronomy & Astrophysics ; Computer Science, Interdisciplinary Applications |
| WOS ID | WOS:001147346500001 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/5741 |
| Collection | 中国科学院新疆天文台 |
| Corresponding Author | Yin, K. |
| Affiliation | 1.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Jiangsu, Peoples R China 2.Collaborat Innovat Ctr Novel Software Technol & In, Nanjing 210000, Jiangsu, Peoples R China 3.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Xinjiang, Peoples R China 4.Chinese Acad Sci, Purple Mt Observ, Nanjing 210023, Jiangsu, Peoples R China |
| Recommended Citation GB/T 7714 | Yin, K.,Jia, J.,Li, F.,et al. Harnessing edge-enhanced attention mechanisms for supernova detection in deep learning frameworks[J]. ASTRONOMY AND COMPUTING,2024,46:100784. |
| APA | Yin, K.,Jia, J.,Li, F.,Gao, X.,&Sun, T..(2024).Harnessing edge-enhanced attention mechanisms for supernova detection in deep learning frameworks.ASTRONOMY AND COMPUTING,46,100784. |
| MLA | Yin, K.,et al."Harnessing edge-enhanced attention mechanisms for supernova detection in deep learning frameworks".ASTRONOMY AND COMPUTING 46(2024):100784. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Yin-2024-Harnessing （3667KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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