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Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach
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Orientador(es)
Resumo(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.
Descrição
Palavras-chave
YOLOv7 Image processing Learning method
Contexto Educativo
Citação
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
Editora
Springer Nature