在深度学习框架中利用边缘增强注意力机制进行超新星检测

IF 1.9 4区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS
K. Yin , J. Jia , F. Li , X. Gao , T. Sun
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引用次数: 0

摘要

最近的研究显示了卷积神经网络在超新星分类和探测方面的优势。在我们之前的工作中,我们采用了单级物体检测框架来解决超新星检测中预设位置和不同图像尺寸的难题。值得注意的是,在可视化热图中,物体检测器的主干自然强调候选区域的边缘,这反映了人类观察者所采取的策略。利用这种相似性,我们引入了一个创新的边缘关注模块,专门用于优先考虑候选区域的边缘,从而提高了超新星探测器的性能。与此同时,我们将科学图像(当前图像)、模板图像(参考图像)和差分图像整合到一个三通道配置中,开发了一个三通道超新星探测数据集。新数据集中的候选者更加明显。为了评估边缘注意模块的功效,我们在提议的数据集上进行了一系列实验。实验结果证明了所提出的方法在检测超新星方面的优越性。此外,特征图的可视化显示,所提出的边缘关注能够重新分配候选边缘周围的权重,从而证实了它的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing edge-enhanced attention mechanisms for supernova detection in deep learning frameworks

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.

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来源期刊
Astronomy and Computing
Astronomy and Computing ASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍: Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.
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