中文摘要：

日冕物质抛射与日冕活动、空间天气以及许多行星际的扰动有着密切的联系,其检测对于空间天气防灾减灾具有重要意义.现有方法通过人为定义特征或利用简单阈值的方法进行CME检测,对弱CME或暗CME的检测效果差.为了解决这一问题,本文提出了一种端到端的日冕物质抛射检测方法：通过引入卷积神经网络,自动提取适应于描述日冕物质抛射检测的图像特征,并基于这些自动提取的特征建立日冕物质抛射检测模型.该过程不需要人为参与特征的选择及分类规则的设定,可以方便地实现数据到结果的端到端的映射.实验结果表明,在本文构建的测试数据集上可以得到98.05%的准确率,并以2007年5月的观测数据为例,检测了26个普通CME事件中的24个事件,得到了优于当前常用日志的效果.因此,本文方法可以有效地进行日冕物质抛射检测.

英文摘要：

Coronal Mass Ejections（CMEs） impact heavily on coronal activity, space weather and many interplanetary disturbance, so the detection of CMEs are important for space weather disaster prevention and reduction. The traditional methods use man-made features or predefined threshold to solve this problem. Despite the great progress in the detection of CMEs, it is still a challenging problem due to the following three aspects： Firstly, the early and late stage of the CMEs phenomenon is very weak, and the traditional image processing based method can not detect this weak CMEs well. Secondly, the noises from comet, planets and other stars can affect the detection of CMEs. Thirdly, the CMEs are complex and amorphous, and they are different in shapes, textures, grayscales, scales and so on. Because of these difficulties, it is difficult to detect CMEs well by the traditional image processing method without modeling the CMEs. With the development of convolutional neural networks（CNNs）, it is possible to develop deep neural networks based CMEs detection models to better solve this problem. For realizing this, this paper presents an end-to-end detection method of Coronal Mass Ejections detection： We design a deep neural network with 4 convolution layers, 1 full connection layer and 1 output layer. This deep neural network can automatically extract the image features that are suitable to describe the Coronal Mass Ejections, and can establish the CMEs detection model based on the extracted features. In order to achieve good performance, we construct two datasets, one is mainly made up of strong CMEs, and the other is made up of weak or dark CMEs. Training is first done on the strong CMEs to obtain the initial CMEs detection model. Based on the initial model established on the strong CMEs, finetuning is used on the weak CMEs to acquire the final CMEs detection model. By using this scheme, training efficiency and good performance can be guaranteed. In addition, the process is able to achieve selection of features and s