基于Faster R-CNN的日冕物质抛射检测方法

Detection Algorithm of Coronal Mass Ejections Based on Faster R-CNN

日冕物质抛射(Coronal Mass Ejection,CME)是一种强烈的太阳爆发现象,对空间天气和人类生活有巨大的影响,因此,日冕物质抛射检测对预报日冕物质抛射、保障人类的生产生活安全具有重要意义。现有的日冕物质抛射检测多采用人为定义特征和界定阈值等方法。由于人为定义特征不能准确表征日冕物质抛射且具有普适性的阈值难于选择,现有的方法对日冕物质抛射的检测效果有待提高。提出一种基于Faster R-CNN(Faster Region-based Convolutional Neural Networks)的日冕物质抛射检测算法。该方法首先结合CDAW(Coordinated Data Analysis Workshop Data Center),SEEDS(Solar Eruptive Even Detection System)和CACTus(Computer Aoded CME Tracking software package)3个著名的日冕物质抛射目录信息,人工标注了包含9113幅日冕图像的数据集,然后根据日冕物质抛射的图像特征较自然图像少、目标尺寸与自然图像有差异等特点,在特征提取和锚点选择方面对Faster R-CNN进行改进。以2007年6月的日冕物质抛射标注数据为测试集,本文算法检出了全部22个强日冕物质抛射事件和151个弱日冕物质抛射事件中的138个,对日冕物质抛射事件的中心角和角宽度等特征参数的检测误差分别在5°和10°以内。

Coronal Mass Ejection(CME)is a strong solar eruption,which has a great impact on space weather and human life.Therefore,CME detection is of great significance in predicting CME and ensuring the safety of human production and life.The existing detection methods mostly use artificial defined features and artificial defined threshold to detect CME.Because artificially defined features cannot precisely represent weak CMEs and it is difficult to select a universal threshold,the detection effect of existing CME detection methods for weak CMEs needs to be improved.A CME detection algorithm based on Faster R-CNN is proposed in this paper.In this method,a dataset containing 9113 coronal images was manually annotated by combining the log information of three famous CME catalog,CDAW,SEEDS and CACTus.Then,according to the characteristics of CME images with fewer features than natural images and the difference in target size from natural images,Faster R-CNN was improved in feature extraction and anchor point selection.Using the CME data in June 2007 as the test set,the algorithm detected all 22 strong CME events and 138 out of 151 weak CME events,and the detection errors of characteristic parameters such as center Angle and Angle width of CME events were within 5 degrees and 10 degrees,respectively.