计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210900198-7.doi: 10.11896/jsjkx.210900198
郭军成1, 万刚1, 胡欣杰1, 王帅1, 严发宝2
GUO Jun-cheng1, WAN Gang1, HU Xin-jie1, WANG Shuai1, YAN Fa-bao2
摘要： 太阳射电爆发事件与多种太阳活动紧密相关,对不同类型的射电爆发事件进行研究有助于提高对太阳活动物理机制的理解,加强对空间天气的判读能力。为解决以往传统射电爆发事件检测方法存在的样本数据量小、检测速度慢、定位准确度低、人为因素干扰大等问题,文中提出采用基于深度学习的少样本目标检测方法对太阳射电频谱图中的不同射电爆发事件进行自动识别和定位。首先,由于目前缺乏公开的射电爆发事件检测数据集,基于美国绿岸太阳射电爆发频谱仪所观测到的射电频谱数据,构建了具有3种爆发类型、共745张图像的少样本目标检测数据集;然后,利用基于迁移学习的少样本学习方法解决了射电爆发事件检测数据集样本量少的问题。实验结果表明,所提方法具有可行性和有效性。
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