Autor(es): Mendes, João ; Silva, Adriano S. ; Roman, Fernanda ; Díaz de Tuesta, Jose Luis ; Lima, José ; Gomes, Helder ; Pereira, Ana I.
Data: 2024
Identificador Persistente: http://hdl.handle.net/10198/30379
Origem: Biblioteca Digital do IPB
Assunto(s): YOLOv7; Image processing; Learning method
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.
