Solar Filament Detection Based on an Improved Deep Learning Model

Abstract

Solar filaments are good tracers of space weather and magnetic flux ropes in the corona. Identifying and detecting filaments helps to forecast space weather and explore the solar magnetic field. Many automatic detection methods have been proposed to process the large number of observed images. Current methods face issues of unreliable dataset annotations and poor anti-interference capability. First, to address the issue of unreliable dataset annotations, we built a solar filament dataset using a manual annotation method. Second, we introduced Transformer into Convolutional Neural Networks. Transformer, with the ability to extract more global features, can help counter interference. In addition, there is large disparity in the size of solar filaments. Therefore, a multi-scale residual block is designed to extract features across various scales. Deformable large kernel attention and a res path are used to better integrate encoder and decoder information. Results show that this method outperforms the existing solar filament detection methods (improved U-Net and improved V-Net), achieving an F1 score of 91.19%. In particular, our results show lower interference by sunspots and background noise than existing methods. The ability to counter interference is improved.