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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PML3RP
Repositorysid.inpe.br/sibgrapi/2017/09.26.14.23
Last Update2017:09.26.14.23.27 (UTC) rafael89.rocha@gmail.com
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.26.14.23.27
Metadata Last Update2022:05.18.22.18.26 (UTC) administrator
Citation KeyRochaSGSSRBDCS:2017:AvTéDe
TitleAvaliação de técnicas de Deep Learning aplicadas à identificação de peças defeituosas em vagões de trem
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size2491 KiB
2. Context
Author 1 Rocha, Rafael L.
 2 Siravenha, Ana Carolina Q.
 3 Gomes, Ana C. S.
 4 Serejo, Gerson L.
 5 Silva, Alexandre F. B.
 6 Rodrigues, Luciano M.
 7 Braga, Júlio
 8 Dias, Giovanni
 9 Carvalho, Schubert R.
10 Souza, Cleidson R. B. de
Affiliation 1 Instituto Tecnológico Vale (ITV), Belém, Pará, Brasil
 2 Instituto Tecnológico Vale (ITV), Belém, Pará, Brasil
 3 Instituto SENAI de Inovação em Tecnologias Minerais ISI/SENAI, Belém, Pará, Brasil
 4 Instituto Tecnológico Vale (ITV), Belém, Pará, Brasil
 5 Instituto SENAI de Inovação em Tecnologias Minerais ISI/SENAI, Belém, Pará, Brasil
 6 Instituto Tecnológico Vale (ITV), Belém, Pará, Brasil
 7 Vale S.A. São Luís. MA, Brasil
 8 Vale S.A. São Luís. MA, Brasil
 9 Instituto Tecnológico Vale (ITV), Belém, Pará, Brasil
10 Instituto Tecnológico Vale (ITV), Belém, Pará, Brasil e Universidade Federal do Pará, Belém, Pará, Brasil
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressrafael89.rocha@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeIndustry Application Paper
History (UTC)2017-09-26 14:23:27 :: rafael89.rocha@gmail.com -> administrator ::
2022-05-18 22:18:26 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsDeep learning
Convolutional neural network
Image classification
Inspection
Wagon components
AbstractInspecting 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.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Avaliação de técnicas...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 26/09/2017 11:23 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PML3RP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PML3RP
Languagept
Target Fileworkshopsibgrapi2017.pdf
User Grouprafael89.rocha@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 6
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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