Abstract: The curvilinear motion of solar photosphere bright points, which is approximate rotation, is of great significance for studying how the energy from solar convection zone is transmitted to the corona. The existing algorithms only detect the global curvilinear motion of photosphere bright points. This paper proposes a multi-scale and multi-modal deep learning method to detect the global and local curvilinear motion of photosphere bright points. This mothod constructs a multi-scale network model based on the bidirectional long short-term memory network (Bi-LSTM)  to extract multi-scale time sequence features of the trajectories of photosphere bright points. EfficientNet-B0 is adopted to extract the spatial features of the trajectories. The temporal features and spatial features are fused into multi-modal features to detect various curvilinear motions of photosphere bright points. The experiment results show that the accuracy of this method is 85.08%, which is 6.12% higher than that of the single-scale method and 3.1% higher than that of the multi-scale and single-mode method. This method can also be applied to the motion type detection requirements in other fields.

Key words: curvilinear motion, deep learning, multi-scale, multi-modal