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.

中文翻译：

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使用深度学习进行太阳能活动区域检测

太阳爆发事件可能会影响无线电通信、全球定位系统和太空中的一些高科技设备。太阳活动区是太阳爆发事件的主要源区。因此，活动区的自动检测不仅对日常观测很重要，对太阳活动预报也很重要。目前，活动区域的提取采用传统的图像处理技术进行人工或自动提取。由于活动区域动态演化，因此设计合适的特征提取器并不容易。在本文中，我们首先概述了目前常用的活动区域检测方法。然后，两个代表性的物体检测模型，faster R-CNN 和 YOLO V3，被用来学习活动区域的特征，并最终建立基于深度学习的活动区域检测模型。性能评估表明，两种模型都实现了活动区域检测的高精度。此外，YOLO V3 是$4\%$ 和 $1\%$分别在真阳性 (TP) 和真阴性 (TN) 指标方面优于更快的 R-CNN；同时，前者比后者快八倍。