Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/56958
Type: Artigo de Periódico
Title: Automatic system for visual detection of dirt buildup on conveyor belts using convolutional neural networks
Authors: André A. Santos
Filipe A. S. Rocha
Agnaldo J. da R. Reis
Frederico Gadelha Guimarães
Abstract: 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.
Subject: Engenharia elétrica
Aprendizado do computador
language: eng
metadata.dc.publisher.country: Brasil
Publisher: Universidade Federal de Minas Gerais
Publisher Initials: UFMG
metadata.dc.publisher.department: ENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA
ENGENHARIA - ESCOLA DE ENGENHARIA
Rights: Acesso Aberto
metadata.dc.identifier.doi: https://doi.org/10.3390/s20205762
URI: http://hdl.handle.net/1843/56958
Issue Date: 12-Oct-2020
metadata.dc.url.externa: https://www.mdpi.com/1424-8220/20/20/5762
metadata.dc.relation.ispartof: Sensors
Appears in Collections:Artigo de Periódico



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.