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.
Implementation of Particle Image Velocimetry for Silo Discharge and Food Industry Seeds
Molina R.;Marsi S.;Ramponi G.;
2020-01-01
Abstract
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.File | Dimensione | Formato | |
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ApplePies_2020_compressed.pdf
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Sergio Saponara, Alessandro De Gloria -.pdf
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