Solar filaments are a significant solar activity phenomenon,typically observed in full-disk solar observations in the H-alpha band.They are closely associated with the magnetic fields of solar active regions,solar flare eruptions,and coronal mass ejections.

With the increasing volume of observational data,the automated high-precision recognition of solar filaments using deep learning is crucial.In this study,we processed full-disk H-alpha solar images captured by the Chinese H alpha Solar Explorer in 2023 to generate labels for solar filaments.

The preprocessing steps included limb-darkening removal,grayscale transformation,K-means clustering,particle erosion,multiple closing operations,and hole filling.

The dataset containing solar filament labels is constructed for deep learning.We developed the Attention U2-Net neural network for deep learning on the solar dataset by introducing an attention mechanism into U2-Net.

In the results,Attention U 2-Net achieved an average Accuracy of 0.9987,an average Precision of 0.8221,an average Recall of 0.8469,an average IoU of 0.7139,and an average F1-score of 0.8323 on the solar filament test set,showing significant improvements compared to other U-net variants.