@InProceedings{SganderlaMaurSantPere:2021:DeClOb,
               author = "Sganderla, Guilherme Rodrigues and Mauricio, Claudio Roberto 
                         Marquetto and Santos, Val{\'e}ria Nunes dos and Peres, Fabiana 
                         Frata Frata",
          affiliation = "{Universidade Estadual do Oeste do Paran{\'a}} and {Universidade 
                         Estadual do Oeste do Paran{\'a}} and {Funda{\c{c}}{\~a}o Parque 
                         Tecnol{\'o}gico Itaipu} and {Universidade Estadual do Oeste do 
                         Paran{\'a}}",
                title = "Detec{\c{c}}{\~a}o e Classifica{\c{c}}{\~a}o de Objetos 
                         Presentes em Imagens A{\'e}reas de Drones de Ambientes Urbanos",
            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 = "Drone, Detec{\c{c}}{\~a}o de Objetos, YOLOv5.",
             abstract = "Through large data sets, it is possible to train and instruct a 
                         machine with skills to perform tasks previously performed only by 
                         humans. This possibility has become increasingly real with the use 
                         of Deep Learning and powerful algorithms that have been developed 
                         over time. Among them is YOLO, a Convolutional Neural Network 
                         algorithm that allows several uses, including the detection and 
                         classification of objects contained in images of urban 
                         environments, such as people and vehicles, allowing the 
                         identification and location of objects within the images. This 
                         work presents a model for detecting and classifying common object 
                         classes in urban environments - People, Small Vehicles, 
                         Medium-Vehicles and Large-Vehicles). For this project we used a 
                         combination of 3 datasets of aerial drone images of urban 
                         environments (Stanford Drone Dataset, Vision Meets Drone, The 
                         Unmanned Aerial Vehicle Benchmark Object Detection and Tracking). 
                         The result obtained from the initial training of this YOLO 
                         algorithm was an average accuracy of 67.2%.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "pt",
                  ibi = "8JMKD3MGPEW34M/45E3ET5",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45E3ET5",
           targetfile = "SIBGRAPI_2021_GUILHERME(1).pdf",
        urlaccessdate = "2024, May 06"
}