Solar Event Detection Using Deep-Learning-Based Object Detection Methods

Research on the detection of solar events has been conducted over many years. Recently, deep learning and data-driven approaches have been applied to solar event recognition. In this study, we present solar event detection using deep-learning-based object detection methods for real-time space weathe...

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Bibliographic Details
Published in:Solar physics 2021-11, Vol.296 (11), Article 160
Main Authors: Baek, Ji-Hye, Kim, Sujin, Choi, Seonghwan, Park, Jongyeob, Kim, Jihun, Jo, Wonkeun, Kim, Dongil
Format: Article
Language:English
Subjects:
Artificial neural networks
Astrophysics and Astroparticles
Atmospheric Sciences
Coronal holes
Datasets
Deep learning
Meteorological satellites
Neural networks
Object recognition
Physics
Physics and Astronomy
Prominences
Solar activity
Solar observatories
Solar physics
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Space weather
Sunspots
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Summary:Research on the detection of solar events has been conducted over many years. Recently, deep learning and data-driven approaches have been applied to solar event recognition. In this study, we present solar event detection using deep-learning-based object detection methods for real-time space weather monitoring. First, we construct a new object detection dataset using imaging data obtained by the Solar Dynamics Observatory with bounding boxes as labels for three representative features: coronal holes, sunspots, and prominences. Second, we train two representative object detection models: the Single Shot MultiBox Detector (SSD) and the Faster Region-based Convolutional Neural Network (R-CNN) using the new dataset. The results show that both models perform similarly well for coronal hole and sunspot detection. For prominence detection, the SSD and Faster R-CNN exhibited relatively low performance. This study demonstrates that deep-learning-based object detection can successfully detect multiple types of solar events, and it may be extended to detect other solar events. In addition, we provide the dataset for further achievements of object detection studies in solar physics.
ISSN:0038-0938
1573-093X
DOI:10.1007/s11207-021-01902-5