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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RNK4NP
Repositorysid.inpe.br/sibgrapi/2018/08.30.16.20
Last Update2018:08.30.16.20.55 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.30.16.20.55
Metadata Last Update2022:06.14.00.09.13 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00062
Citation KeyNassuJrMaCaWaZa:2018:ImStRe
TitleImage-based state recognition for disconnect switches in electric power distribution substations
FormatOn-line
Year2018
Access Date2024, Apr. 27
Number of Files1
Size9769 KiB
2. Context
Author1 Nassu, Bogdan Tomoyuki
2 Jr. , Lourival Lippmann
3 Marchesi, Bruno
4 Canestraro, Amanda
5 Wagner, Rafael
6 Zarnicinski, Vanderlei
Affiliation1 Federal University of Technology - Parana
2 Institutos Lactec
3 Institutos Lactec
4 Institutos Lactec
5 Institutos Lactec
6 Companhia Paranaense de Energia
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressbtnassu@utfpr.edu.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-08-30 16:20:55 :: btnassu@utfpr.edu.br -> administrator ::
2022-06-14 00:09:13 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordscomputer vision
image understanding
disconnect switches
electric power distribution substation automation
AbstractKnowing the state of the disconnect switches in a power distribution substation is important to avoid accidents, damaged equipment, and service interruptions. This information is usually provided by human operators, who can commit errors because of the cluttered environment, bad weather or lighting conditions, or lack of attention. In this paper, we introduce an approach for determining the state of each switch in a substation, based on images captured by regular pan-tilt-zoom surveillance cameras. The proposed approach includes noise reduction, image registration using phase correlation, and classification using a convolutional neural network and a support vector machine fed with gradient-based descriptors. By combining information given in an initial labeling stage with image processing techniques to reduce variations in viewpoint, our approach achieved 100% accuracy on experiments performed at a real substation over multiple days. We also show how modifications to the standard phase correlation image registration algorithm can make it more robust to lighting variations, and how SIFT (Scale-Invariant Feature Transform) descriptors can be made more robust in scenarios where the relevant objects may be brighter or darker than the background.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Image-based state recognition...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Image-based state recognition...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RNK4NP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RNK4NP
Languageen
Target FilePID5544421.pdf
User Groupbtnassu@utfpr.edu.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume


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