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

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

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

Conference proceedings International

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

Pages: 148-163

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

Ponta Delgada, PORTUGAL, SEP 27-29, 2023

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16