@InProceedings{DuarteCoDiBoDuDr:2020:ThNoIn,
               author = "Duarte, Marta and Coch, Victor and Dias, Jovania and Botelho, 
                         Silvia and Duarte, Nelson and Drews Jr, Paulo",
          affiliation = "Federal University of Rio Grande (FURG), Brazil and Federal 
                         University of Rio Grande (FURG), Brazil and Federal University of 
                         Rio Grande (FURG), Brazil and Federal University of Rio Grande 
                         (FURG), Brazil and Federal University of Rio Grande (FURG), Brazil 
                         and Federal University of Rio Grande (FURG), Brazil",
                title = "Thermographic Non-Invasive Inspection Modelling of Fertilizer 
                         Pipelines Using Neural Networks",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "thermal image, pipeline inspection, neural networks, fertilizer.",
             abstract = "Industry pipeline fault, like blockage can create major problems 
                         for engineers and financial loss for the company. The blockage 
                         detection is necessary for smooth functioning of an industry and 
                         safety of the environment. This work presents a model for 
                         non-invasive inspection of pipes. It proposes the use of a neural 
                         network to identify the obstruction stage in fertilizer industry, 
                         using external thermal images obtained from the pipelines. A 
                         dataset capable of mapping the external thermal behavior in 
                         profile of the internal deposit is developed. The Multilayer 
                         Perceptron neural network was able to learn the thermal pixel 
                         mapping in a deposit profile, obtaining satisfactory results.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00045",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00045",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43BD8EH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43BD8EH",
           targetfile = "Paper ID 120.pdf",
        urlaccessdate = "2024, Apr. 28"
}