Solar radio spikes are short-duration, narrow-band burst signals, which are a fine structure of solar radio bursts. The processing and analysis of their observed data are of great significance in the study of electron acceleration in the process of solar flares and electron acceleration during the explosion and diagnosis of corona parameters. Deep learning interprets data by mimicking the mechanism of the human brain. Faster Region-based Convolutional Neural Network (Faster R-CNN) is a branch of deep learning based on region nomination, and its classification results have considerable advantages in accuracy. In this paper, Faster R-CNN will be used to identify and extract solar radio spikes. In order to improve the detection ability of small events, a multi-scale detection frame and a multi-layer feature fusion training method are used. The analysis results show that the Average Precision (AP) value of the improved network is close to 91%, which is nearly 10% higher than the original network. So the improved Faster R-CNN method can also be used for the identification and extraction of small-scale fine structures in other fields.

中文翻译：

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基于深度学习的太阳射电尖峰识别与提取

太阳射电脉冲是短时窄带爆发信号，是太阳射电爆发的精细结构。对其观测数据进行处理和分析，对研究太阳耀斑过程中的电子加速度和爆炸过程中的电子加速度以及日冕参数的诊断具有重要意义。深度学习通过模仿人脑的机制来解释数据。Faster Region-based Convolutional Neural Network（Faster R-CNN）是基于区域提名的深度学习的一个分支，其分类结果在准确率上具有相当的优势。在本文中，Faster R-CNN 将用于识别和提取太阳射电尖峰。为了提高小事件的检测能力，采用多尺度检测框和多层特征融合训练方法。分析结果表明，改进后网络的Average Precision（AP）值接近91%，比原网络提高了近10%。所以改进的Faster R-CNN方法也可以用于其他领域的小尺度精细结构的识别和提取。