@InProceedings{MedeirosAraúSilvRama:2021:UsImAv,
               author = "Medeiros, David Silva de and Ara{\'u}jo, Thiago Henrique and 
                         Silva J{\'u}nior, Elias Teodoro da and Ramalho, Geraldo Luis 
                         Bezerra",
          affiliation = "Federal Institute of Education, Science and Technology of 
                         Cear{\'a} and Federal Institute of Education, Science and 
                         Technology of Cear{\'a} and Federal Institute of Education, 
                         Science and Technology of Cear{\'a} and Federal Institute of 
                         Education, Science and Technology of Cear{\'a}",
                title = "Using images to avoid collisions and bypass obstacles in indoor 
                         environments",
            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 = "deep learning, dataset, assistive technology, CNN.",
             abstract = "Convolutional Neural Network (CNN) has contributed a lot to the 
                         advancement of autonomous navigation techniques, and such systems 
                         can be adapted to facilitate the movement of robots and visually 
                         impaired people. This work presents an approach that uses images 
                         to avoid collisions and bypass obstacles in indoor environments. 
                         The constructed dataset uses information from forward and lateral 
                         speeds during walks to determine collisions and obstacle 
                         avoidance. VGG16, ResNet50, and Dronet architectures were used to 
                         evaluate the dataset. Finally, reflections on the dataset 
                         characteristics are added, and the CNNs performance is 
                         presented.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EK4FB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EK4FB",
           targetfile = "
                         
                         Using_images_to_avoid_collisions_and_bypass_obstacles_in_indoor_environments.pdf",
        urlaccessdate = "2024, May 02"
}