@InProceedings{RochaSGSSRBDCS:2017:AvTéDe,
author = "Rocha, Rafael L. and Siravenha, Ana Carolina Q. and Gomes, Ana C.
S. and Serejo, Gerson L. and Silva, Alexandre F. B. and Rodrigues,
Luciano M. and Braga, J{\'u}lio and Dias, Giovanni and Carvalho,
Schubert R. and Souza, Cleidson R. B. de",
affiliation = "Instituto Tecnol{\'o}gico Vale (ITV), Bel{\'e}m, Par{\'a},
Brasil and Instituto Tecnol{\'o}gico Vale (ITV), Bel{\'e}m,
Par{\'a}, Brasil and Instituto SENAI de Inova{\c{c}}{\~a}o em
Tecnologias Minerais ISI/SENAI, Bel{\'e}m, Par{\'a}, Brasil and
Instituto Tecnol{\'o}gico Vale (ITV), Bel{\'e}m, Par{\'a},
Brasil and Instituto SENAI de Inova{\c{c}}{\~a}o em Tecnologias
Minerais ISI/SENAI, Bel{\'e}m, Par{\'a}, Brasil and Instituto
Tecnol{\'o}gico Vale (ITV), Bel{\'e}m, Par{\'a}, Brasil and
Vale S.A. S{\~a}o Lu{\'{\i}}s. MA, Brasil and Vale S.A.
S{\~a}o Lu{\'{\i}}s. MA, Brasil and Instituto Tecnol{\'o}gico
Vale (ITV), Bel{\'e}m, Par{\'a}, Brasil and Instituto
Tecnol{\'o}gico Vale (ITV), Bel{\'e}m, Par{\'a}, Brasil e
Universidade Federal do Par{\'a}, Bel{\'e}m, Par{\'a}, Brasil",
title = "Avalia{\c{c}}{\~a}o de t{\'e}cnicas de Deep Learning aplicadas
{\`a} identifica{\c{c}}{\~a}o de pe{\c{c}}as defeituosas em
vag{\~o}es de trem",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Deep learning, Convolutional neural network, Image classification,
Inspection, Wagon components.",
abstract = "Inspecting objects is an important task in many areas and is often
used in industry to ensure product quality, allowing problem
correction and disposal of damaged products. Inspection is also
widely used in railway maintenance, where every day, hundreds of
wagons are inspected visually in a process dependent on personal
interpretation. This article describes an inspection approach of
wagon components using deep learning techniques that comprises the
stages of the component detection and the identification of its
condition. In this work, the analyzed component is the shear pad
which is responsible for supporting the truck. Object detection is
done by a cascade detector and the classification task among three
possible states (undamaged, absent and damaged) is done by
convolutional neural networks. Our results are very encouraging,
especially when observing the performance of the AlexNet
network.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
language = "pt",
ibi = "8JMKD3MGPAW/3PML3RP",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PML3RP",
targetfile = "workshopsibgrapi2017.pdf",
urlaccessdate = "2024, May 02"
}