Deep learning for particle image velocimetry with attentional transformer and cross-correlation embedded

Abstract

Deep learning-powered methodologies have realized remarkable advancements in the fluid mechanics community, including applications in the particle image velocimetry (PIV) task. However, previous deep learning-based methods still lack robustness and generalization in the real flow scenarios. To solve this problem, we put forward a deep learning architecture called DeepST-CC for PIV estimation, which embeds an attentional transformer and cross-correlation strategy. Specifically, we introduce the Swin Transformer into the optical flow model Recurrent All-pairs Field Transforms (RAFT) to enhance the features of flow images. Then, the global matching between features is efficiently computed by applying a 4D correlation volume. Afterwards, the conventional cross-correlation method derives the initial velocity field from a coarse correlation, which is fed to the GRU-based flow update module. This approach enhances the robustness of the proposed model by incorporating coarse velocity information. Finally, a supervised learning strategy is performed to guide the model training on the synthetic dataset. Extensive experimental are conducted to demonstrate that our proposed approach delivers exceptional performance on the public dataset. In addition, DeepST-CC exhibits good generalization ability towards complex experimental PIV images.