Use este identificador para citar ou linkar para este item: http://hdl.handle.net/1843/56958
Tipo: Artigo de Periódico
Título: Automatic system for visual detection of dirt buildup on conveyor belts using convolutional neural networks
Autor(es): André A. Santos
Filipe A. S. Rocha
Agnaldo J. da R. Reis
Frederico Gadelha Guimarães
Resumo: Conveyor 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.
Assunto: Engenharia elétrica
Aprendizado do computador
Idioma: eng
País: Brasil
Editor: Universidade Federal de Minas Gerais
Sigla da Instituição: UFMG
Departamento: ENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA
ENGENHARIA - ESCOLA DE ENGENHARIA
Tipo de Acesso: Acesso Aberto
Identificador DOI: https://doi.org/10.3390/s20205762
URI: http://hdl.handle.net/1843/56958
Data do documento: 12-Out-2020
metadata.dc.url.externa: https://www.mdpi.com/1424-8220/20/20/5762
metadata.dc.relation.ispartof: Sensors
Aparece nas coleções:Artigo de Periódico

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