@InProceedings{OliveiraCaCaSoCâQu:2022:PuDaFa,
               author = "Oliveira, Frederico Santos de and Carvalho, Marcelo de and Campos, 
                         Pedro Henrique Tancredo and Soares, Anderson da Silva and 
                         C{\^a}ndido J{\'u}nior, Arnaldo and Quirino, Ana Cl{\'a}udia 
                         Rodrigues da Silva",
          affiliation = "{Universidade Federal de Mato Grosso (UFMT)} and Eletrobras-Furnas 
                         and Eletrobras-Furnas and {Universidade Federal de Goi{\'a}s 
                         (UFG)} and {Universidade Estadual Paulista (UNESP)} and 
                         Eletrobras-Furnas",
                title = "PTL-AI Furnas Dataset: A Public Dataset for Fault Detection in 
                         Power Transmission Lines Using Aerial Images",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "object detection, power transmission lines, fault detection.",
             abstract = "We present a new images dataset called PTL-AI Furnas Dataset as a 
                         new benchmark for fault detection in power transmission lines. 
                         This dataset has 6,295 images, with resolution 1280×720, extracted 
                         from the maintenance process of the energy transmission lines at 
                         Furnas company. It contains annotations of 17,808 components 
                         classified as baliser, bird nest, insulator, spacer and 
                         stockbridge. Furnas is a company that generates or transmits 
                         electricity to 51% of households in Brazil and more than 40% of 
                         the nations electricity passes through their grid enabling 
                         generating the dataset in different backgrounds and climatic 
                         conditions. We performed experiments using data augmentation 
                         techniques to train Faster R-CNN, Single-Shot Detects (SSD) and 
                         YoloV5 models. The benchmark result was obtained using the metrics 
                         of Mean Average Precision (mAP) and the Mean Average Recall (mAR) 
                         with values mAP=91.9% and mAR=89.7%. The PTL-AI Furnas Dataset is 
                         publicly available at https://github.com/freds0/PTL-AI Furnas 
                         Dataset.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
                  doi = "10.1109/SIBGRAPI55357.2022.9991806",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991806",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47LTDJ5",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47LTDJ5",
           targetfile = "oliveira-33_inpe.pdf",
        urlaccessdate = "2024, May 19"
}