A GPU-accelerated particle-detection algorithm for real-time volumetric particle-tracking velocimetry under non-uniform illumination

Abstract

Real-time volumetric particle tracking velocimetry (VPTV) equipped with field-programmable gate array (FPGA) cameras has been used for open-space, low particle density, and large-scale airflow measurements with long measurement periods. However, the particle detection accuracy of FPGA cameras is inevitably hindered by non-uniform illumination, resulting in a reduction in the particle detection ratio and positional accuracy. In this article, we propose to use both synchronized FPGA and grayscale cameras in a VPTV system, where grayscale cameras utilize a new algorithm based on two-frame centroid and corner extraction (TFCCE) under non-uniform white-light illumination. To keep the frame rate of the FPGA cameras the same, the TFCCE algorithm was accelerated by a graphics processing unit (GPU). The simulation results showed that the 2D particle detection ratio of TFCCE was enhanced to approximately 80% with a positional accuracy of 0.57 pixels, compared to 30% and 0.94 pixels for the single-frame centroid extraction used in the FPGA. The GPU version of TFCCE was 15.09 times faster than the CPU version, resulting in a calculation time of 4.55 ms per image, compared to 68.70 ms when using the CPU. This system was also validated by the measurement of a turbulent jet flow in real-time at 120 fps. The experimental results correspond well with data published in the literature. Therefore, this new algorithm can improve VPTV systems in terms of particle detection ratio and positional accuracy in real time under conditions of non-uniform illumination.

Accurate PIV measurement on slip boundary using single-pixel algorithm

“…In addition to the PIV algorithm, particle tracking velocimetry (PTV) can be used for measuring the near-wall flow. In further work, a twodimensional particle detection ratio by PTV was enhanced to approximately 80% with a positional accuracy of 0.57 pixel [29]. The above PIV and PTV algorithms are only used to solve near-wall flows under the no-slip boundary condition.…”

A parametric study of 3D PTV algorithms based on a two-view collimated imaging model

“…In 2008, Li et al [5] proposed a regressionbased multi-frame (RMT) algorithm, which searched the possible tracks using five frames, and the root-mean-square of particle velocities served as the cost function. With the RMT algorithm, significant progress toward real-time measurement has been made using parallel computing, field-programmable gate array cameras and graphics processing unit acceleration [6][7][8]. Practical tests were conducted for large-scale airflow measurement under low particle density conditions with longtime recording [7].…”