Investigating deep learning applications in computer vision for effective facial mask detection during the global covid-19 crisis
DOI:
https://doi.org/10.15675/gepros.2988Palavras-chave:
Computer Vision, Convolutional Neural Network, Face Mask DetectionResumo
Purpose: This study aims to scrutinize the integration of deep learning algorithms within the sphere of computer vision, with a concentrated focus on proficiently detecting face mask usage amidst the global COVID-19 pandemic.
Theoretical Framework: The research is grounded in the theoretical underpinnings of deep learning, a branch of artificial intelligence, and its application in computer vision. It explores the advancements in machine learning algorithms capable of complex image processing and pattern recognition, essential for identifying face mask usage in various settings.
Methodology/Approach: The research adopts a methodological approach involving the design and development of a deep learning model. This model is trained on a diverse dataset encompassing images of individuals with and without face masks. Python, along with libraries such as OpenCV, Keras, and TensorFlow, forms the backbone of the implementation, facilitating the processing and analysis of image data.
Findings: The study's findings reveal that the developed model demonstrates a high degree of accuracy, with a 99% success rate in test image predictions, showcasing the effectiveness of deep learning in image recognition tasks. This underscores the model's proficiency in identifying face mask usage, a critical factor in controlling the spread of airborne viruses like COVID-19.
Research, Practical & Social Implications: This research contributes significantly to the field of computer vision, offering practical applications in public health monitoring and societal well-being. The model's ability to accurately detect face mask usage paves the way for enhanced pandemic management strategies and reinforces the role of technology in public health initiatives.
Originality/Value: This study innovates within existing research by applying deep learning in computer vision for addressing the COVID-19 crisis. It uniquely focuses on developing technological solutions for efficient and cost-effective monitoring of face mask usage, emphasizing prevention.
Keywords: Computer Vision, Convolutional Neural Network, Face Mask Detection.
Referências
Apostolopoulos, Ioannis D. & Mpesiana, Tzani A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, v. 43, p. 635-640. https://doi.org/10.1007/s13246-020-00865-4 DOI: https://doi.org/10.1007/s13246-020-00865-4
Arora, Divya, Garg, Mehak & Gupta, Megha. (2020). Diving deep in deep convolutional neural network. In: 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, p. 749-751. https://doi.org/10.1109/ICACCCN51052.2020.9362907. DOI: https://doi.org/10.1109/ICACCCN51052.2020.9362907
Bradski, Gary & Kaehler, Adrian. (2008). Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc. ISBN: 978-0-596-51613-0
Dekhtiar, Jonathan et al. (2018). Deep learning for big data applications in CAD and PLM–Research review, opportunities, and case study. Computers in Industry, v. 100, p. 227-243. https://doi.org/10.1016/j.compind.2018.04.005 DOI: https://doi.org/10.1016/j.compind.2018.04.005
Faes, Livia et al. (2020). A clinician's guide to artificial intelligence: how to critically appraise machine learning studies. Translational Vision Science & Tecnology, v. 9, n. 2, p. 7-7. https://doi.org/10.1167/tvst.9.2.7 DOI: https://doi.org/10.1167/tvst.9.2.7
Faizah, Arbiati et al. (2021). Implementation of the convolutional neural network method to detect the use of masks. International Journal of Informatics and Information Systems, v. 4, n. 1, p. 30-37. https://doi.org/10.47738/ijiis.v4i1.75 DOI: https://doi.org/10.47738/ijiis.v4i1.75
He, Kaiming et al. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. p. 770-778. http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_ Learning_CVPR_2016_paper.html DOI: https://doi.org/10.1109/CVPR.2016.90
Huang, G. et al. (2016) Densely connected convolutional networks. Arxiv website. arxiv. org/abs/1608.06993. Published August, v. 24. http://openaccess.thecvf.com/ content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html DOI: https://doi.org/10.1109/CVPR.2017.243
Islam, Md Mahbubul, Tasnim, Nusrat, & Baek, Joong-Hwan. (2020). Human gender classification using transfer learning via Pareto frontier CNN networks. Inventions, v. 5, n. 2, p. 16. https://doi.org/10.3390/inventions5020016 DOI: https://doi.org/10.3390/inventions5020016
Kodali, Ravi Kishore, & Dhanekula, Rekha. (2021). Face mask detection using deep learning. In: International Conference on Computer Communication and Informatics (ICCCI). IEEE, p. 1-5. https://doi.org/10.1109/ ICCCI50826.2021.9402670 DOI: https://doi.org/10.1109/ICCCI50826.2021.9402670
Kohavi, Ron & Provost, Foster. (1998). Glossary of Terms: special Issue on Applications of Machine Learning and the Knowledge Discovery Process. Journal of Machine Learning, 30 (2–3) (1998), pp. 271-274. http://ai.stanford.edu/~ ronnyk/glossary.html DOI: https://doi.org/10.1023/A:1007442505281
Militante, Sammy V. & Dionisio, Nanette V. (2020). Real-time facemask recognition with alarm system using deep learning. In: 11th IEEE Control and System Graduate Research Colloquium (ICSGRC). IEEE, p. 106-110. https://doi.org/10.1109/ICSGRC49013.2020.9232610 DOI: https://doi.org/10.1109/ICSGRC49013.2020.9232610
O'shea, Keiron & Nash, Ryan. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458. https://doi.org/10.48550/arXiv.1511.08458
Sethi, Shilpa; Kathuria, Mamta & Kaushik, Trilok. (2021). Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread. Journal of biomedical informatics, v. 120, p. 103848. https://doi.org/10.1016/j.jbi.2021.103848 DOI: https://doi.org/10.1016/j.jbi.2021.103848
Sokolova, Marina & Lapalme, Guy. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, v. 45, n. 4, p. 427-437. https://doi.org/10.1016/j.ipm.2009.03.002 DOI: https://doi.org/10.1016/j.ipm.2009.03.002
Suresh, K., Palangappa, M. B. & Bhuvan, S. (2021). Face mask detection by using optimistic convolutional neural network. In: 6th International Conference on Inventive Computation Technologies (ICICT). IEEE. p. 1084-1089. https://doi.org/10.1109/ICICT50816.2021.9358653 DOI: https://doi.org/10.1109/ICICT50816.2021.9358653
Suryadevara, Chaitanya Krishna. (2020). Real-time face mask detection with computer vision and deep learning. International Journal of Innovations in Engineering Research and Technology, v. 7, n. 12. https://ssrn.com/abstract=4591986
Suryadevara, Chaitanya Krishna. (2021). Enhancing safety: face mask detection using computer vision and deep learning. International Journal of Innovations in Engineering Research and Technology, v. 8, n. 8. https://ssrn.com/abstract=4592001
Tian, Xiao & Chen, Chao. (2019). Modulation pattern recognition based on Resnet50 neural network. In: 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP). IEEE. p. 34-38. https://doi.org/10.1109/ICICSP48821.2019.8958555 DOI: https://doi.org/10.1109/ICICSP48821.2019.8958555
Yadav, Shashi et al. (2020). Deep learning based safe social distancing and face mask detection in public areas for covid-19 safety guidelines adherence. International Journal for Research in Applied Science and Engineering Technology, v. 8, n. 7, p. 1368-1375. https://doi.org/10.22214/ijraset.2020.30560 DOI: https://doi.org/10.22214/ijraset.2020.30560
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2023 Wiliam Regone, Luciel Henrique de Oliveira
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
O(s) autor(es) do artigo autorizam a publicação do texto na revista e garantem que a contribuição é original e inédita, não estando em processo de avaliação em outra(s) revista(s). As opiniões, ideias e conceitos emitidos nos textos são de inteira responsabilidade do(s) autor(es), não sendo a revista responsável por tais conteúdos.
Os editores da revista reservam o direito de efetuar ajustes textuais e de adequação às normas da publicação, caso necessário.
Os autores mantêm os direitos autorais sobre o trabalho e concedem à revista o direito de primeira publicação, sendo o trabalho simultaneamente licenciado sob a Attribution 4.0 International (CC BY 4.0), o que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.
Os autores têm autorização para firmar contratos adicionais, separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista.
