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

Abstract

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.

Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full‐Disk Solar Images

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Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images

“…For automatic object detection, they are more versatile and efficient than traditional feed-forward networks. They have gained popularity as one of the best methods for image analysis [14], and they are also a promising tool for the task of detecting sunspots [15][16][17][18].…”

Towards the Identification and Classification of Solar Granulation Structures Using Semantic Segmentation

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