@InProceedings{YasudaCappMartGrip:2021:AuViIn,
               author = "Yasuda, Yuri Del Vigna and Cappabianco, Fabio Augusto Menocci and 
                         Martins, Luiz Eduardo Galv{\~a}o and Gripp, Jorge Augusto de 
                         Bonfim",
          affiliation = "{Universidade Federal de S{\~a}o Paulo} and {Universidade Federal 
                         de S{\~a}o Paulo} and {Universidade Federal de S{\~a}o Paulo} 
                         and {Autaza Technology}",
                title = "Automated Visual Inspection of Aircraft Exterior Using Deep 
                         Learning",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Aircraft visual inspection, Systematic literature review, Defect 
                         detection, Deep learning.",
             abstract = "Aircraft visual inspections, or General Visual Inspections (GVIs), 
                         aim at finding damages or anomalies on the exterior and interior 
                         surfaces of the aircraft, which might compromise its operation, 
                         structure, or safety when flying. Visual inspection is part of the 
                         activities of aircraft Maintenance, Repair and Overhaul (MRO). 
                         Specialists perform quality inspections to identify problems and 
                         determine the type and importance that they will report. This 
                         process is time-consuming, subjective, and varies according to 
                         each individual. The time that an aircraft stays grounded without 
                         flight clearance means financial losses. The main goal of this 
                         work is to advance the state-of-the-art of defect detection on 
                         aircraft exterior with deep learning and computer vision. We 
                         investigate improvements to the accuracy of dent detection. 
                         Besides, we investigate new classes of identified defects, such as 
                         scratches. We also plan to demonstrate that it is possible to 
                         develop a complete system to automate the visual inspection of 
                         aircraft exterior using images of the aircraft acquired by drones. 
                         We will use deep neural networks for the detection and 
                         segmentation of defective regions. This system will aid in the 
                         elimination of subjectivity caused by human errors and shorten the 
                         time required to inspect an aircraft, bringing benefits to its 
                         safety, maintenance, and operation.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EKAQE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EKAQE",
           targetfile = "Aircraft_Visual_Inspection__Work_In_Progress.pdf",
        urlaccessdate = "2024, May 06"
}