Abstract
Particle Image Velocimetry (PIV) technology is widely used in experimental fluid mechanics. However, due to the complexity of underwater environments, traditional PIV methods face significant challenges in terms of measurement accuracy and adaptability. With the advancement of computer vision and deep learning technologies, an increasing number of optical flow networks have been applied to PIV tasks, yet their performance in fine-grained displacement field prediction remains limited. To address this issue, this paper proposes a new optical flow network improvement model, Res2RAFT. By introducing the Bottle2neck module from the Res2Net network, we constructed the Res2BasicEncoder to enhance multiscale feature extraction capabilities. Additionally, a Global Attention Module was incorporated into the model to improve the prediction accuracy of complex flow fields by enhancing global dimension interaction features. Experimental results show that Res2RAFT achieves average endpoint errors of 2.0758, 8.0548, and 11.7254 on the Cylinder, Direct Numerical Simulation turbulence, and Surface Quasi-Geostrophic categories, respectively, representing improvements of 17.4%, 30.3%, and 27.3% compared to the RAFT-PIV model. Overall, the model achieved an accuracy improvement exceeding 20% in complex flow fields, providing new possibilities for the application of PIV technology in more complex fluid scenarios.
Published Version
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