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.
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References
Wang, Y., Zhang, J., Cao, Y., Wang, Z.: A deep CNN method for underwater image enhancement. In: IEEE International Conference on Image Processing (ICIP), Beijing, pp. 1382–1386 (2017)
Kadar, M., Onita, D.: A deep CNN for image analytics in automated manufacturing process control. In: 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Pitesti, Romania, pp. 1–5. (2019)
Ko, S., Yu, S., Kang, W., Park, C., Lee, S., Paik, J.: Artifact-free low-light video enhancement using temporal similarity and guide map. IEEE Trans. Ind. Electron. 64(8), 6392–6401 (2017)
Job, N., Dardenne, A., Pirard, J.P.: Silo flow-pattern diagnosis using the tracer method. J. Food Eng. 91(1), 118–125 (2009)
Eurocode 1, Basis of design and actions on structures - 4: Actions in silos and tanks (1998)
Villagrán,C.: Efecto de los parámetros de forma de los granos y del ángulo de inclinación de la tolva en el flujo de semillas en silos. Trabajo Final de Licenciatura en Física - FCFMyN - UNSL, and references within (2018)
Westerweel, J.: Fundamentals of digital particle image velocimetry. Meas. Sci. Technol. 8(12), 1379–1392 (1997)
Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: WESPE: weakly supervised photo enhancer for digital cameras. In: IEEE International Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) (2018)
Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van, G.: LucDSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks, Luc (2017)
PYNQ - http://www.pynq.io/. Seen 8 2020
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Molina, R., Gonzalez, V., Benito, J., Marsi, S., Ramponi, G., Petrino, R. (2021). Implementation of Particle Image Velocimetry for Silo Discharge and Food Industry Seeds. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2020. Lecture Notes in Electrical Engineering, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-030-66729-0_1
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DOI: https://doi.org/10.1007/978-3-030-66729-0_1
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