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2022 | OriginalPaper | Chapter

4. Deep Learning in Solar Object Detection Tasks

Authors : Long Xu, Yihua Yan, Xin Huang

Published in: Deep Learning in Solar Astronomy

Publisher: Springer Nature Singapore

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Abstract

Solar observation provides us abundant solar images containing plentiful information about solar activities. Especially, solar instruments onboard satellite continuously record high-resolution and high-cadence full-disk solar images. These images are used for solar activity forecasting and statistical analysis. Usually, it is required to mine key information from full-disk images firstly. Then, over extracted information, one can establish classification, recognition or forecasting models by using machine learning or deep learning. In a full-disk solar image, active region, filament, coronal hole and sunspot are the objects carrying major information about solar activities. In computer vision, object detection is one of the most classical tasks, which has been well investigated. In this chapter, we present two examples of object detection from solar image, by using well pre-trained deep learning models in computer vision.

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Footnotes
1
http://jsoc.stanford.edu/ajax/lookdata.html.
 
2
https://www.swpc.noaa.gov/products/solar-region-summary.
 
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Metadata
Title
Deep Learning in Solar Object Detection Tasks
Authors
Long Xu
Yihua Yan
Xin Huang
Copyright Year
2022
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-2746-1_4

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