Solar Radio Burst Detection Based on the MobileViT-SSDLite Lightweight Model

Abstract

Real-time detection of solar radio bursts is crucial in solar physics research and space weather forecasting. However, current research on the automatic detection of solar radio bursts is limited to identifying the presence or absence of solar radio bursts or recognizing only a single type of burst, such as type II or III. Furthermore, existing methods cannot learn spectral and temporal features and often suffer from the drawbacks of large network models, resulting in slow speeds. This paper proposes an automatic recognition and localization method based on a lightweight object detection model for solar radio burst events. We collected observation data from e-CALLISTO and established a data set containing type II, III, IV, and V solar radio bursts. To address the real-time requirements of practical applications and consider the temporal and frequency domain information of spectrogram images, we improved a vision transformer with a self-attention mechanism and adopted a lightweight model for detection. The experimental results demonstrate that our proposed method achieves an average precision at a 50% intersection-over-union threshold of 78.2% and a recall rate of 92% on the established solar radio burst data set. Additionally, the model operates at a detection speed of 54.8 frames s–1, where a frame refers to a spectral image with a duration of 15 minutes, enabling efficient automated detection and localization of type II, III, IV, and V solar radio bursts.