Aplicação da rede convolucional Yolo para análise de fluxo de veículos
Resumo
Este trabalho apresenta a descrição de um protótipo para o processamento de fluxo de imagens provenientes de câmeras de monitoramento de trânsito, com o objetivo de detectar e categorizar veículos, determinando a sua direção e tempo de deslocamento. A taxa de acerto obtida para a fase de detecção foi de 76%, com a determinação correta das direções e tempo de deslocamento para os veículos.
Palavras-chave:
Monitoramento trânsito, Detecção de veículos, Processamento de imagens
Referências
Parque Tecnológico Itaipu, Vila A Inteligente, https://hubiguassu.pti.org.br/vila-a-inteligente, 2021
Marcelo Lacortt and Moacir Kripka and Rosana Maria Luvezute Kripka, Modelos Matemáticos para Otimização do Tráfego Urbano Semaforizado, Trends in Computational and Applied Mathematics, v. 14, n. 3, p. 359-372, nov. 2013. ISSN 2676-0029. Available at: https://tema.sbmac.org.br/tema/article/view/637, doi: https://doi.org/10.5540/tema.2013.014.03.0359.
OpenCV.org, Open Source Computer Vision Library, https://opencv.org, 2022
Charles R. Harris and K. Jarrod Millman and Stéfan J. van der Walt and Ralf Gommers and Pauli Virtanen and David Cournapeau and Eric Wieser and Julian Taylor and Sebastian Berg and Nathaniel J. Smith and Robert Kern and Matti Picus and Stephan Hoyer and Marten H. van Kerkwijk and Matthew Brett and Allan Haldane and Jaime Fernández del Río and Mark Wiebe and Pearu Peterson and Pierre Gérard-Marchant and Kevin Sheppard and Tyler Reddy and Warren Weckesser and Hameer Abbasi and Christoph Gohlke and Travis E. Oliphant, NumPy, https://numpy.org, 2022
John Hunter, Darren Dale, Eric Firing, Michael Droettboom, Matplotlib, https://matplotlib.org, 2022
Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection, https://doi.org/10.48550/arXiv.2004.10934, 2020
Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Tom Duerig and Vittorio Ferrari, The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale, https://doi.org/10.48550/arXiv.1811.00982, 2018
Google LLC, Overview of Open Images V4, https://storage.googleapis.com/openimages/web/factsfigures_v4.html, 2018 7
Marcelo Lacortt and Moacir Kripka and Rosana Maria Luvezute Kripka, Modelos Matemáticos para Otimização do Tráfego Urbano Semaforizado, Trends in Computational and Applied Mathematics, v. 14, n. 3, p. 359-372, nov. 2013. ISSN 2676-0029. Available at: https://tema.sbmac.org.br/tema/article/view/637, doi: https://doi.org/10.5540/tema.2013.014.03.0359.
OpenCV.org, Open Source Computer Vision Library, https://opencv.org, 2022
Charles R. Harris and K. Jarrod Millman and Stéfan J. van der Walt and Ralf Gommers and Pauli Virtanen and David Cournapeau and Eric Wieser and Julian Taylor and Sebastian Berg and Nathaniel J. Smith and Robert Kern and Matti Picus and Stephan Hoyer and Marten H. van Kerkwijk and Matthew Brett and Allan Haldane and Jaime Fernández del Río and Mark Wiebe and Pearu Peterson and Pierre Gérard-Marchant and Kevin Sheppard and Tyler Reddy and Warren Weckesser and Hameer Abbasi and Christoph Gohlke and Travis E. Oliphant, NumPy, https://numpy.org, 2022
John Hunter, Darren Dale, Eric Firing, Michael Droettboom, Matplotlib, https://matplotlib.org, 2022
Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection, https://doi.org/10.48550/arXiv.2004.10934, 2020
Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Tom Duerig and Vittorio Ferrari, The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale, https://doi.org/10.48550/arXiv.1811.00982, 2018
Google LLC, Overview of Open Images V4, https://storage.googleapis.com/openimages/web/factsfigures_v4.html, 2018 7
Publicado
02/11/2022
Como Citar
BARBADO, Larissa; SANTOS, Lucas Medeiros Reinaldet dos; MATRAKAS, Miguel Diogenes; MOREIRA, Jasmine.
Aplicação da rede convolucional Yolo para análise de fluxo de veículos. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 19. , 2022, Evento Híbrido.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 43-49.
DOI: https://doi.org/10.5753/latinoware.2022.228034.
