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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Mendes, J (Autor) Outra; Silva, AS (Autor) Outra; Roman, FF (Autor) Outra; de Tuesta, JLD (Autor) Outra; Lima, J (Autor) Outra; Gomes, HT (Autor) Outra; Pereira, AI (Autor) Outra

Ata de Conferência Internacional

Título: OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, PT II, OL2A 2023

Páginas: 148-163

3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A)

Ponta Delgada, PORTUGAL, SEP 27-29, 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: P-010-2W5

DOI: 10.1007/978-3-031-53036-4_11

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16