Solar Radio Burst Detection Based on Deformable DETR

Abstract Solar radio burst (SRB) detection is crucial for solar physics research and space weather forecasting. The main challenges faced are noise interference in the spectrum and the diversity of SRBs. However, most research focuses on classifying whether SRBs exist or detecting a single type of SRB. Existing detection models exhibit deficiencies in the accuracy of SRB detection. Moreover, existing detection models cannot effectively handle background noise interference in solar radio spectrograms and the significant scale variations among different burst types. This paper proposes a high-performance detection model for SRBs based on the Deformable DEtection TRansformer (DETR) called DETR4SBRs. First, this study designed a scale-sensitive attention module to better address the scale variations of SRBs. Subsequently, this study introduced collaborative hybrid auxiliary training to mitigate the positive–negative sample imbalance issue in Deformable DETR. The experimental results demonstrate that the proposed model achieves a mAP@50 of 83.5% and a recall rate of 99.4% on the SRBs data set. Additionally, the model exhibits excellent noise-robust performance and can efficiently detect and locate Type II, III, IV, and V SRBs. The model proposed in this study provides robust support for preliminary SRB data processing and has significant implications for space weather forecasting. The source code and data are available on ​​​​​​ https://github.com/onewangqianqian/SSA-Co-Deformable-DETR.git , and the software is archived on Zenodo.