Detecção de uso de máscaras em ambientes fechados com MobileNetV2 e Single Shot Multibox Detector
Abstract
This paper presents a deep learning application for detecting people wearing facial masks in indoor environments. With the advent of Covid 19 pandemic, the necessity of taking precautions to slow and control the pandemic growth is essential, between those precautions, wearing facial masks is vital to keep contagion rates as low as possible. The system is capable of detecting the correct use of facial masks in indoor environments using MobileNetV2 as feature extractor and SSD as the object detector, along with TensorFlow, Keras, OpenCV and Python technologies. The results show reasonable performance with low training time, hardware and data density.
References
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Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., e Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. Ieee.
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., e Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303-338.
Goodfellow, I., Bengio, Y., e Courville, A. (2016). Deep Learning. MIT Press. Disponível em: <http://www.deeplearningbook.org>. Acecsso em: Jun, 2022.
Islam, M. S., Haque Moon, E., Shaikat, M. A., e Jahangir Alam, M. (2020). A novel approach to detect face mask using cnn. In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pages 800-806.
Jiang, M. e Fan, X. (2020). Retinamask: A face mask detector. CoRR, abs/2005.03950.
Li, Y., Huang, H., Xie, Q., Yao, L., e Chen, Q. (2018). Research on a surface defect detection algorithm based on mobilenet-ssd. Applied Sciences, 8(9).
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., e Dollár, P. (2014). Microsoft coco: Common objects in context.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., e Berg, A. C. (2016). SSD: Single shot MultiBox detector. In Computer Vision - ECCV 2016, pages 21-37. Springer International Publishing.
Mask Dataset (2020). Mask dataset. https://makeml.app/datasets/mask.
OMS (2020). Recomendações sobre o uso de máscaras no contexto da covid-19. Disponível em: [link]. Acesso em: Ago, 2021.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., e Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4510-4520.
Simonyan, K. e Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition.
Zaki, S. Z. M., Asyraf Zulkifley, M., Mohd Stofa, M., Kamari, N. A. M., e Ayuni Mohamed, N. (2020). Classification of tomato leaf diseases using MobileNet v2. IAES Int. J. Artif. Intell. (IJ-AI), 9(2):290.
Published
2022-09-28
How to Cite
FERREIRA, Paulo Fernando Neres; GOMES JUNIOR, Daniel Lima; SANTOS, Alex Martins.
Detecção de uso de máscaras em ambientes fechados com MobileNetV2 e Single Shot Multibox Detector. In: REGIONAL SCHOOL ON COMPUTING OF CEARÁ, MARANHÃO, AND PIAUÍ (ERCEMAPI), 10. , 2022, São Luís/MA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 61-68.
DOI: https://doi.org/10.5753/ercemapi.2022.225942.
