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

Mendes, João; Silva, Adriano S.; Roman, Fernanda; Díaz de Tuesta, Jose Luis; Lima, José; Gomes, Helder; Pereira, Ana I.

http://hdl.handle.net/10198/30379

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Authors

Mendes, João

Silva, Adriano S.

Roman, Fernanda

Díaz de Tuesta, Jose Luis

Lima, José

Gomes, Helder

Pereira, Ana I.

Abstract(s)

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.

Keywords

YOLOv7 Image processing Learning method

URI

http://hdl.handle.net/10198/30379

Citation

Mendes, João; Silva, Adriano S.; Roman, Fernanda F.; Tuesta, Jose L. Diaz de; Lima, José; Gomes, Helder T.; Pereira, Ana I. (2024). Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 148–163. ISBN 978-3-031-53035-7