@InProceedings{NassuJrMaCaWaZa:2018:ImStRe,
               author = "Nassu, Bogdan Tomoyuki and Jr. , Lourival Lippmann and Marchesi, 
                         Bruno and Canestraro, Amanda and Wagner, Rafael and Zarnicinski, 
                         Vanderlei",
          affiliation = "{Federal University of Technology - Parana} and {Institutos 
                         Lactec} and {Institutos Lactec} and {Institutos Lactec} and 
                         {Institutos Lactec} and {Companhia Paranaense de Energia}",
                title = "Image-based state recognition for disconnect switches in electric 
                         power distribution substations",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "computer vision, image understanding, disconnect switches, 
                         electric power distribution substation automation.",
             abstract = "Knowing 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.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00062",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00062",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RNK4NP",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RNK4NP",
           targetfile = "PID5544421.pdf",
        urlaccessdate = "2024, Apr. 28"
}