Multi-category solar radio burst detection based on task-aligned one-stage object detection model

Abstract

Accurate identification of solar radio bursts (SRBs) is essential for advancing research in solar physics and predicting space weather. However, the majority of current studies mainly concentrate on detecting whether SRBs are present or absent, often focusing on only one particular type of burst. Moreover, the neural network models used for SRB detection are typically complex, involving a large number of parameters, which results in slower processing speeds. This study establishes a dataset encompassing Type II, Type III, Type IIIs, Type IV, and Type V SRBs collected from e-CALLISTO, including 8,752 SRB spectrum images and achieving annotations for 10,822 SRBs. We propose a multi-category SRB detection model based on task-aligned one-stage object detection (TOOD). TOOD can solve the problem of inconsistent predictions in classification and localization tasks, and it improves the detection recall rate. This model aligns classification and localization tasks and optimizes the neck network by incorporating a channel attention mechanism. This model achieves higher recall and accuracy with fewer parameters. This model can accurately detect five types of SBRs. The experimental results show that the model achieved an accuracy of 79.9% (AP50) and a recall rate of 95.1% on the SBRs dataset. A higher recall rate than other models means fewer SRBs are missed in automatic detection. The model we propose has the potential to make a substantial impact on solar physics research and space weather studies. Additionally, the findings in this paper could provide valuable insights for processing other small-sample astronomical, datasets. The source code and data is available at https://github.com/onewangqianqian/MobileNetVitv2-TOOD.git.