Solar Radio Burst Detection Based on Deformable DETR

Abstract

Solar radio bursts (SRBs) 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 SRBs. Existing detection models exhibit deficiencies in the accuracy of SRBs 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 solar radio bursts (SRBs) based on Deformable DETR (DEtection TRansformers) called DETR4SBRs. Firstly, this study designed a scale sensitive attention (SSA) module better to 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 dataset. 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 solar radio burst data processing and has significant implications for space weather forecasting. The source code and data are available on the https://github.com/onewangqianqian/SSA-Co-Deformable-DETR.git and archived on Zenodo.

20 pages, 14 figures;Accepted for publication in The Astrophysical Journal Supplement

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Cited by in corpus (1)

• Type II and Type III Solar Radio Burst Classification Using Transfer Learning