Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach-Reference-Cited by-同舟云学术

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Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach

Published:2024 Issue: Volume: Page:148-163
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:en
Short-container-title:

Author:

Mendes João^ORCID,Silva Adriano S.^ORCID,Roman Fernanda F.^ORCID,de Tuesta Jose L. Diaz^ORCID,Lima José^ORCID,Gomes Helder T.^ORCID,Pereira Ana I.^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-53036-4_11

Reference28 articles.

1. Berton-Carabin, C.C., Schroën, K.: Pickering emulsions for food applications: background, trends, and challenges. Ann. Rev. Food Sci. Technol. 6, 263–297 (2015). https://doi.org/10.1146/annurev-food-081114-110822

2. Cai, Y., et al.: Yolov4-5d: an effective and efficient object detector for autonomous driving. IEEE Trans. Instrument. Meas. 70 (2021). https://doi.org/10.1109/TIM.2021.3065438

3. Chevalier, Y., Bolzinger, M.A.: Emulsions stabilized with solid nanoparticles: pickering emulsions. Colloids Surf. A 439, 23–34 (2013). https://doi.org/10.1016/j.colsurfa.2013.02.054

4. de Tuesta, J.L.D., et al.: Polyolefin-derived carbon nanotubes as magnetic catalysts for wet peroxide oxidation of paracetamol in aqueous solutions. Catal. Today 419, 114162 (2023). https://doi.org/10.1016/j.cattod.2023.114162

5. Farzi, G.A., Nejad, A.P.: An image-based technique for measuring droplet size distribution: the use of CNN algorithm. J. Dispersion Sci. Technol. 37(10), 1444–1452 (2016). https://doi.org/10.1080/01932691.2015.1090321

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