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

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Optimization, Learning Algorithms and Applications (OL2A 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1982 ))

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Abstract

This study focuses on the analysis of emulsion pictures to understand important parameters. While droplet size is a key parameter in emulsion science, manual procedures have been the traditional approach for its determination. Here we introduced the application of YOLOv7, a recently launched deep-learning model, for classifying emulsion droplets. A comparison was made between the two methods for calculating droplet size distribution. One of the methods, combined with YOLOv7, achieved 97.26% accuracy. These results highlight the potential of sophisticated image-processing techniques, particularly deep learning, in chemistry-related topics. The study anticipates further exploration of deep learning tools in other chemistry-related fields, emphasizing their potential for achieving satisfactory performance.

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Notes

  1. 1.

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Acknowledgements

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/05757/2020, UIDP/05757/2020, UIDB/00690/2020, UIDB/50020/2020, and UIDB/00319/ 2020. Adriano Silva was supported by Doctoral Grant SFRH/BD/151346/2021 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from NORTE 2020, under MIT Portugal Program. Fernanda F. Roman was supported by FCT and FSE with the PhD research grant SFRH/BD/143 224/2019.

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Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

    João Mendes, Adriano S. Silva, José Lima & Ana I. Pereira

  2. Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

    Adriano S. Silva, Fernanda F. Roman & Helder T. Gomes

  3. Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

    João Mendes, Adriano S. Silva, Fernanda F. Roman, José Lima, Helder T. Gomes & Ana I. Pereira

  4. Chemical and Environmental Engineering Group, ESCET, Rey Juan Carlos University, Madrid, Spain

    Jose L. Diaz de Tuesta

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  1. João Mendes
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  2. Adriano S. Silva
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Correspondence to João Mendes .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Azores, Ponta Delgada, Portugal

    Armando Mendes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

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Mendes, J. et al. (2024). Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1982 . Springer, Cham. https://doi.org/10.1007/978-3-031-53036-4_11

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  • DOI: https://doi.org/10.1007/978-3-031-53036-4_11

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