Avaliação do Paralelismo em Python para Otimizar uma Abordagem de Identificação de Máscaras Faciais utilizando Redes Neurais Artificiais
Abstract
The Covid-19 pandemic has required great efforts from the entire scientific community. However, pandemic control has only been possible with social isolation and the use of facial masks. Despite this, cases of agglomerations and people who refuse to use the masks are shown daily, making it difficult to control the disease. This article presents an approach using Artificial Neural Networks (ANN) to identify people who do not use face masks in an image database. The results obtained demonstrate that although the parallelism used in the Python language with the Numpy library is punctual, it positively impacts the developed approach's execution time.
Keywords:
Avaliação, Medição e Predição de Desempenho.
References
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Ejaz, M. S., Islam, M. R., Sifatullah, M., and Sarker, A. (2019). Implementation of principal component analysis on masked and non-masked face recognition. In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pages 1–5. IEEE.
Haddad, D. B., de Assis, L. S., Tarrataca, L., Gomes, A. d. S., Ceddia, M. B., Oliveira, R. F., Junior, J. R. d. P., and Brandao, D. N. (2018). Brazilian soil bulk density prediction based on a committee of neural regressors. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
Loey, M., Manogaran, G., Taha, M. H. N., and Khalifa, N. E. M. (2020). A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemic. Measurement, 167:108288.
Oliphant, T. E. (2006). A guide to NumPy, volume 1. Trelgol Publishing USA.
QIN, B. and LI, D. (2020). Identifying facemask-wearing condition using image superresolution with classification network to prevent covid-19.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826.
Ullah, F. U. M., Ullah, A., Muhammad, K., Haq, I. U., and Baik, S. W. (2019). Violence detection using spatiotemporal features with 3d convolutional neural network. Sensors, 19(11):2472.
Published
2020-11-30
How to Cite
STEINBRUCH, Natan; SANTOS, Vinícius ; VILLELA, Nicholas ; CAMARGO, Gabriel Renato ; XAVIER, Andre ; BRANDÃO, Diego.
Avaliação do Paralelismo em Python para Otimizar uma Abordagem de Identificação de Máscaras Faciais utilizando Redes Neurais Artificiais. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM RIO DE JANEIRO (ERAD-RJ), 6. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 21-25.
DOI: https://doi.org/10.5753/eradrj.2020.14511.
