Contamination risk estimation model for respiratory diseases in monitored environments using YOLOv6
Abstract
Monitoring solutions for environments, from camera images, have grown mainly with the integration with machine learning models. With the COVID-19 pandemic, several measures to prevent and combat the coronavirus have been adopted, with social distancing and the use of masking being major factors in controlling the spread of the disease. This paper presents a new approach based on convolutional neural networks focused on airborne diseases, which assesses the infection rate of the monitored space, in real time. For this, through the video monitored environment, are (i) identified people with masks used correctly or incorrectly; (ii) identified people without masks; (iii) measured the distances between people. From this information, it is possible to infer (iv) an index to measure the chances of infection in a monitored space over time, using YOLOv5 in this development environment.
Keywords:
YOLO, software engineering, computational intelligence, computer graphics and image processing, contamination risk
References
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GÜNER, HATICE RAHMET; HASANOGLU, Imran; AKTAS, Firdevs. COVID-19: Prevention and control measures in community. Turkish Journal of Medical Sciences, v. 50, n. SI-1, p. 571-577, 2020.
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YANG, Guanhao et al. Face Mask Recognition System with YOLOV5 Based on Image Recognition. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 2020. p. 1398-1404.
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Official YOLOv5 PyTorch page: https://pytorch.org/hub/ultralytics_yolov5/. Accessed on: 24 of Dec.2021.
S. Meivel, K. Indira Devi, S. Uma Maheswari, and J. Vijaya Menaka, “Real time data analysis of face mask detection and social distance measurement using Matlab,” Mater. Today Proc., no. xxxx, 2021.
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A. Cabani, K. Hammoudi, H. Benhabiles, and M. Melkemi, “MaskedFace-Net – A dataset of correctly/incorrectly masked face images in the context of COVID-19,” Smart Heal., vol. 19, pp. 1–5, 2021, doi: 10.1016/j.smhl.2020.100144.
M. F. Catapan, “ANÁLISE ANTROPOMÉTRICA DA CABEÇA HUMANA PARA DIMENSIONAMENTO DE CAPACETES BALÍSTICOS,” 2014.
HARRICHANDRA, Amelia; IERARDI, A. Michael; PAVILONIS, Brian. An estimation of airborne SARS-CoV-2 infection transmission risk in New York City nail salons. Toxicology and industrial health, v. 36, n. 9, p. 634-643, 2020.
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RILEY, E. C.; MURPHY, G.; RILEY, R. L. Airborne spread of measles in a suburban elementary school. American journal of epidemiology, v. 107, n. 5, p. 421-432, 1978.
DHARMADHIKARI, Ashwin S. et al. Surgical face masks worn by patients with multidrug-resistant tuberculosis: impact on infectivity of air on a hospital ward. American journal of respiratory and critical care medicine, v. 185, n. 10, p. 1104-1109, 2012.
Image Dataset repository used in this project: [link].
Repository page of the code for this project: [link].
GÜNER, HATICE RAHMET; HASANOGLU, Imran; AKTAS, Firdevs. COVID-19: Prevention and control measures in community. Turkish Journal of Medical Sciences, v. 50, n. SI-1, p. 571-577, 2020.
Advice for the public: Coronavirus disease (COVID-19). World Health Organization, 2021. Available in: [link]. Accessed on: 22 of Dec. 2021.
WHO Generals and Directors Speeches. Opening Remarks at the Media Briefing on COVID-19; WHO Generals and Directors Speeches: 4 November 2021.
YANG, Guanhao et al. Face Mask Recognition System with YOLOV5 Based on Image Recognition. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 2020. p. 1398-1404.
D. K. Reddy N, G. Jeevan Kumar, and G. R. Krishna, “Social Distance Monitoring And Face Mask Detection System For Covid-19 Pandemic,” Turkish J. Comput. Math. Educ., vol. 12, no. 12, pp. 2200–2206, 2021.
Official YOLOv5 PyTorch page: https://pytorch.org/hub/ultralytics_yolov5/. Accessed on: 24 of Dec.2021.
S. Meivel, K. Indira Devi, S. Uma Maheswari, and J. Vijaya Menaka, “Real time data analysis of face mask detection and social distance measurement using Matlab,” Mater. Today Proc., no. xxxx, 2021.
S. Bhutada, N. Sahithi, M. Mounika, and M. Revathi, “SOCIAL DISTANCING AND MASK DETECTOR BASED ON,” vol. II, no. May, pp. 81–87, 2021.
P. Somaldo, F. A. Ferdiansyah, G. Jati, and W. Jatmiko, “Developing Smart COVID-19 Social Distancing Surveillance Drone using YOLO Implemented in Robot Operating System simulation environment,” IEEE Reg. 10 Humanit. Technol. Conf. R10-HTC, vol. 2020-December, 2020, doi: 10.1109/R10-HTC49770.2020.9357040.
M. Rezaei and M. Azarmi, “Deepsocial: Social distancing monitoring and infection risk assessment in covid-19 pandemic,” Appl. Sci., vol. 10, no. 21, pp. 1–29, 2020, doi: 10.3390/app10217514.
A. Cabani, K. Hammoudi, H. Benhabiles, and M. Melkemi, “MaskedFace-Net – A dataset of correctly/incorrectly masked face images in the context of COVID-19,” Smart Heal., vol. 19, pp. 1–5, 2021, doi: 10.1016/j.smhl.2020.100144.
M. F. Catapan, “ANÁLISE ANTROPOMÉTRICA DA CABEÇA HUMANA PARA DIMENSIONAMENTO DE CAPACETES BALÍSTICOS,” 2014.
HARRICHANDRA, Amelia; IERARDI, A. Michael; PAVILONIS, Brian. An estimation of airborne SARS-CoV-2 infection transmission risk in New York City nail salons. Toxicology and industrial health, v. 36, n. 9, p. 634-643, 2020.
Riley EC, Murphy G and Riley RL (1978) Airborne spread of measles in a suburban elementary school. American Journal of Epidemiology 107: 421–432.
RILEY, E. C.; MURPHY, G.; RILEY, R. L. Airborne spread of measles in a suburban elementary school. American journal of epidemiology, v. 107, n. 5, p. 421-432, 1978.
DHARMADHIKARI, Ashwin S. et al. Surgical face masks worn by patients with multidrug-resistant tuberculosis: impact on infectivity of air on a hospital ward. American journal of respiratory and critical care medicine, v. 185, n. 10, p. 1104-1109, 2012.
Image Dataset repository used in this project: [link].
Repository page of the code for this project: [link].
Published
2022-07-31
How to Cite
MOURA, Flávio Rafael Trindade; SILVA, Diego de Lemos Brito da; CARDOSO, Diego Lisboa; LEITE, Karla Tereza Figueiredo; MELLO JUNIOR, Harold Dias de; COSTA, Fernando Augusto Ribeiro; SERUFFO, Marcos César da Rocha.
Contamination risk estimation model for respiratory diseases in monitored environments using YOLOv6. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 49. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 1-12.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2022.222225.
