Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images

Abstract

Solar activity has been subject to increasingly more research in the last decades. Its influence on life on Earth is now better understood. Solar winds impact the earth’s magnetic field and atmosphere. They can disrupt satellite communication and navigation tools and even electrical power grids and several other infrastructure crucial for our technology-based society. Coronal mass ejections (CMEs), solar energetic particles, and flares are the main causes of problems that affect the systems mentioned. It is possible to predict some of those by monitoring the sun and analyzing the images obtained in different spectra, thus identifying solar phenomena related to its activity, such as filaments, pores, and sunspots. Several studies have already been carried out on the subject of automation of the mentioned analysis, most of which use neural networks and other machine learning approaches. In this work, we develop a method for sunspot detection based on the YOLOv5 network, applying it to a dataset of images from the Geophysical and Astronomical Observatory of the University of Coimbra (OGAUC), which has one of the oldest and more complete datasets of sun images in the world. Our method reaches mAP@.5 over 90% with YOLOv5s, which is higher than other methods previously applied for the same dataset. This shows that CNN models can be used in spectroheliographs for detecting and tracking sunspots.

Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection

“…Yolov8 represents the latest advancement in the Yolo series of detection algorithms, and it utilizes a single prediction framework to locate and classify objects in image detection. This method exhibits superior capabilities in detecting multiple objects with enhanced speed and accuracy [39]. Comparing the improved YoloX-S to the Yolov7 and Yolov8 networks, it is clear that the identification accuracy is increased, with mAP increasing by 2.28 and 2.64 percent, respectively, while the speed decreased, with FPS reducing by 2 f/s and 3 f/s, respectively.…”

SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images

“…He [25] adopted a CornerNet-Saccade deep learning method for classifying sunspot groups based on Mount Wilson classification. Santos [26] applied the YOLOv5 network to detect sunspots; however, only clearly visible sunspots were detected correctly. The solar images were multiplied down-sampled by these deep learning methods without exception because deep convolution requires a lot of memory.…”