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

2024Conference paper Restricted access
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dc.contributor.authorMendes, João
dc.contributor.authorSilva, Adriano S.
dc.contributor.authorRoman, Fernanda
dc.contributor.authorDíaz de Tuesta, Jose Luis
dc.contributor.authorLima, José
dc.contributor.authorGomes, Helder
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2024-10-08T14:22:49Z
dc.date.available2024-10-08T14:22:49Z
dc.date.issued2024
dc.description.abstractThis 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.pt_PT
dc.description.sponsorshipThis work has been supported by FCT - Funda¸c˜ao para a Ciiência e Tecnologia within the R&D Units Project Scope: UIDB/05757/2020, UIDP/05757/2020, UIDB/00690/2020, UIDB/50020/2020, and UIDB/00319/2020. Adriano Silva was supported by Doctoral Grant SFRH/BD/151346/2021 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from NORTE 2020, under MIT Portugal Program. Fernanda F. Roman was supported by FCT and FSE with the PhD research grant SFRH/BD/143 224/2019.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMendes, 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 978-3-031-53035-7pt_PT
dc.identifier.doi10.1007/978-3-031-53036-4_11pt_PT
dc.identifier.isbn978-3-031-53035-7
dc.identifier.isbn978-3-031-53036-4
dc.identifier.urihttp://hdl.handle.net/10198/30379
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationMountain Research Center
dc.relationLaboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials
dc.relationALGORITMI Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectYOLOv7pt_PT
dc.subjectImage processingpt_PT
dc.subjectLearning methodpt_PT
dc.titleDeep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approachpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleMountain Research Center
oaire.awardTitleLaboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials
oaire.awardTitleALGORITMI Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
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oaire.citation.endPage163pt_PT
oaire.citation.startPage148pt_PT
oaire.citation.title3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)pt_PT
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person.familyNameMendes
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person.familyNameLima
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person.givenNameJoão
person.givenNameAdriano S.
person.givenNameFernanda
person.givenNameJosé
person.givenNameHelder
person.givenNameAna I.
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person.identifier.orcid0000-0001-6898-2408
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project.funder.nameFundação para a Ciência e a Tecnologia
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