Solar Active Region Detection Using Deep Learning

Abstract

Solar eruptive events could affect radio communication, global positioning systems, and some high-tech equipment in space. Active regions on the Sun are the main source regions of solar eruptive events. Therefore, the automatic detection of active regions is important not only for routine observation, but also for the solar activity forecast. At present, active regions are manually or automatically extracted by using traditional image processing techniques. Because active regions dynamically evolve, it is not easy to design a suitable feature extractor. In this paper, we first overview the commonly used methods for active region detection currently. Then, two representative object detection models, faster R-CNN and YOLO V3, are employed to learn the characteristics of active regions, and finally establish a deep learning–based detection model of active regions. The performance evaluation demonstrates that the high accuracy of active region detection is achieved by both the two models. In addition, YOLO V3 is 4% and 1% better than faster R-CNN in terms of true positive (TP) and true negative (TN) indexes, respectively; meanwhile, the former is eight times faster than the latter.