@InProceedings{VieiraeSilvaFCSTSSSL:2021:DaMuPo,
               author = "Vieira e Silva, Andr{\'e} Luiz Buarque and Felix, Heitor de 
                         Castro and Chaves, Thiago de Menezes and Sim{\~o}es, Francisco 
                         Paulo Magalh{\~a}es and Teichrieb, Veronica and dos Santos, 
                         Michel Mozinho and Santiago, Hemir da Cunha and Sgotti, Virginia 
                         Ad{\'e}lia Cordeiro and Lott Neto, Henrique Baptista Duffles 
                         Teixeira",
          affiliation = "Voxar Labs, Centro de Inform{\'a}tica, Universidade Federal de 
                         Pernambuco, Brazil   and Voxar Labs, Centro de Inform{\'a}tica, 
                         Universidade Federal de Pernambuco, Brazil   and Voxar Labs, 
                         Centro de Inform{\'a}tica, Universidade Federal de Pernambuco, 
                         Brazil   and Departamento de Computa{\c{c}}{\~a}o, Universidade 
                         Federal Rural de Pernambuco, Brazil   and Voxar Labs, Centro de 
                         Inform{\'a}tica, Universidade Federal de Pernambuco, Brazil   and 
                         In Forma Software, Brazil   and In Forma Software, Brazil   and In 
                         Forma Software, Brazil   and Sistema de Transmiss{\~a}o Nordeste, 
                         Brazil",
                title = "STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in 
                         High-Resolution UAV Images",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "object detection, image dataset, inspection, power lines, deep 
                         learning, computer vision, uav.",
             abstract = "Many power line companies are using UAVs to perform their 
                         inspection processes instead of putting their workers at risk by 
                         making them climb high voltage power line towers, for instance. A 
                         crucial task for the inspection is to detect and classify assets 
                         in the power transmission lines. However, public data related to 
                         power line assets are scarce, preventing a faster evolution of 
                         this area. This work proposes the STN Power Line Assets Dataset, 
                         containing high-resolution and real-world images of multiple 
                         high-voltage power line components. It has 2,409 annotated objects 
                         divided into five classes: transmission tower, insulator, spacer, 
                         tower plate, and Stockbridge damper, which vary in size 
                         (resolution), orientation, illumination, angulation, and 
                         background. This work also presents an evaluation with popular 
                         deep object detection methods and MS-PAD, a new pipeline for 
                         detecting power line assets in hi-res UAV images. The latter 
                         outperforms the other methods achieving 89.2% mAP, showing 
                         considerable room for improvement. The STN PLAD dataset is 
                         publicly available at https://github.com/andreluizbvs/PLAD.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00037",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00037",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45C7QNL",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45C7QNL",
           targetfile = "52.pdf",
        urlaccessdate = "2024, May 06"
}