Mendes, JoãoSilva, Adriano S.Roman, FernandaDíaz de Tuesta, Jose LuisLima, JoséGomes, HelderPereira, Ana I.2024-10-082024-10-082024Mendes, 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 978-3-031-53035-7978-3-031-53035-7978-3-031-53036-4http://hdl.handle.net/10198/30379This 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.engYOLOv7Image processingLearning methodconference paper10.1007/978-3-031-53036-4_11