Implementation of Particle Image Velocimetry for Silo Discharge and Food Industry Seeds

This work focuses on determining the velocity profile of a granular flow at the outlet of a silo, using artificial vision techniques. The developed algorithm performs a frame enhancement through neural networks and the particle image velocimetry detects seed motion in the hopper. We process 50, 100, 150 and 200 frames of a video discharge for three different grains using: CPU and PYNQ-Z1 implementations with a simple image processing at pre-processing level, and CPU implementation using neural network. Execution times are measured and the differences between the involved technologies are discussed.