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Reference TypeConference Proceedings
Identifier8JMKD3MGPAW/3RNK4NP
Repositorysid.inpe.br/sibgrapi/2018/08.30.16.20
Metadatasid.inpe.br/sibgrapi/2018/08.30.16.20.55
Sitesibgrapi.sid.inpe.br
Citation KeyNassuJrMaCaWaZa:2018:ImStRe
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
TitleImage-based state recognition for disconnect switches in electric power distribution substations
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Year2018
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
Book TitleProceedings
DateOct. 29 - Nov. 1, 2018
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationFoz do Iguaçu, PR, Brazil
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.
Languageen
Tertiary TypeFull Paper
FormatOn-line
Size9769 KiB
Number of Files1
Target FilePID5544421.pdf
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History2018-08-30 16:20:55 :: btnassu@utfpr.edu.br -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
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