%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.16.23.57
%2 sid.inpe.br/sibgrapi/2021/09.16.23.57.12
%T Using images to avoid collisions and bypass obstacles in indoor environments
%D 2021
%A Medeiros, David Silva de,
%A Araújo, Thiago Henrique,
%A Silva Júnior, Elias Teodoro da,
%A Ramalho, Geraldo Luis Bezerra,
%@affiliation Federal Institute of Education, Science and Technology of Ceará
%@affiliation Federal Institute of Education, Science and Technology of Ceará
%@affiliation Federal Institute of Education, Science and Technology of Ceará
%@affiliation Federal Institute of Education, Science and Technology of Ceará
%E Paiva, Afonso,
%E Menotti, David,
%E Baranoski, Gladimir V. G.,
%E Proença, Hugo Pedro,
%E Junior, Antonio Lopes Apolinario,
%E Papa, João Paulo,
%E Pagliosa, Paulo,
%E dos Santos, Thiago Oliveira,
%E e Sá, Asla Medeiros,
%E da Silveira, Thiago Lopes Trugillo,
%E Brazil, Emilio Vital,
%E Ponti, Moacir A.,
%E Fernandes, Leandro A. F.,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K deep learning, dataset, assistive technology, CNN.
%X 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.
%@language en
%3 Using_images_to_avoid_collisions_and_bypass_obstacles_in_indoor_environments.pdf