Particle identification in particle tracking velocimetry using two-stage neural networks

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<p style='text-indent:20px;'>In this paper, a new sub-pixel intensive particle image identification method used for particle tracking velocimetry (PTV), is discussed. With aiming to improve the performance of accuracy and robustness, a two-stage deep learning framework consisting of two independent convolutional neural networks (CNN) is proposed in this method. The first neural network is to segment the particle blobs from the image, and the second one is to locate the position of the segmented particles at the sub-pixel level. A synthetic dataset containing particle images and the ground-truth positions is generated for network training. The effect of different characteristic parameters (e.g., the particle conditions; the tuning of the loss function; the noise level) is evaluated on synthetic images. To further verify the generalization capabilities of the technique, we also apply the proposed network to real-world images. Both synthetic and real-world experiment results strongly demonstrate that the proposed method possesses better accuracy and robustness than other conventional methods.</p>