@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 = "2025, Feb. 16"
}