Abstract
With the wide application of Particle Image Velocimetry (PIV) technology in various engineering and research fields, the requirements for the accuracy, computational efficiency, and robustness of PIV algorithms are increasing. Although traditional algorithms have wide applicability, they suffer from low accuracy, large computational cost, and poor robustness. Recently, deep learning algorithms have provided new solutions, especially, convolutional neural networks with different structures, which have achieved good performance on synthetic PIV datasets. This paper proposes a structural improvement scheme for PIV convolutional neural network models. Experiments verify that the proposed method can significantly optimize the performance of the model on synthetic PIV datasets, providing a novel approach for improving other convolutional neural networks for PIV analysis.
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