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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/56958
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dc.creatorAndré A. Santospt_BR
dc.creatorFilipe A. S. Rochapt_BR
dc.creatorAgnaldo J. da R. Reispt_BR
dc.creatorFrederico Gadelha Guimarãespt_BR
dc.date.accessioned2023-07-25T17:31:33Z-
dc.date.available2023-07-25T17:31:33Z-
dc.date.issued2020-10-12-
dc.citation.volume20pt_BR
dc.citation.issue20pt_BR
dc.citation.spage5762pt_BR
dc.citation.epage16pt_BR
dc.identifier.doihttps://doi.org/10.3390/s20205762pt_BR
dc.identifier.issn1424-8220pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/56958-
dc.description.resumoConveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. Therefore, autonomous methods that can help human beings to perform the inspection of the belt conveyor system is a major concern for companies. In this context, we present in this work a novel and automatic visual detector that recognizes dirt buildup on the structures of conveyor belts, which is one of the tasks of the maintenance inspectors. This visual detector can be embedded as sensors in autonomous robots for the inspection activity. The proposed system involves training a convolutional neural network from RGB images. The use of the transfer learning technique, i.e., retraining consolidated networks for image classification with our collected images has shown very effective. Two different approaches for transfer learning have been analyzed. The best one presented an average accuracy of 0.8975 with an F-1 Score of 0.8773 for the dirt recognition. A field validation experiment served to evaluate the performance of the proposed system in a real time classification task.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICApt_BR
dc.publisher.departmentENGENHARIA - ESCOLA DE ENGENHARIApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofSensorspt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectConvolutional neural networkpt_BR
dc.subjectConveyor beltpt_BR
dc.subjectMachine learningpt_BR
dc.subject.otherEngenharia elétricapt_BR
dc.subject.otherAprendizado do computadorpt_BR
dc.titleAutomatic system for visual detection of dirt buildup on conveyor belts using convolutional neural networkspt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.mdpi.com/1424-8220/20/20/5762pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-2167-1973pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-9238-8839pt_BR
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