Contamination risk estimation model for respiratory diseases in monitored environments using YOLOv5
Resumo
As soluções de monitoramento de ambientes, a partir de imagens de câmeras, cresceram principalmente com a integração com modelos de aprendizado de máquina. Com a pandemia do COVID-19, várias medidas de prevenção e combate ao coronavírus foram adotadas, sendo o distanciamento social e o uso de máscaras fatores importantes no controle da propagação da doença. Este artigo apresenta uma nova abordagem baseada em redes neurais convolucionais focadas em doenças transmitidas pelo ar, que avalia a taxa de infecção do espaço monitorado, em tempo real. Para isso, através do ambiente monitorado por vídeo, são (i) identificadas pessoas utilizando máscara de forma correta ou incorreta; (ii) identificadas pessoas sem máscara; (iii) medidas as distâncias entre as pessoas. A partir dessas informações, é possível inferir (iv) um índice para medir as chances de infecção em um espaço monitorado ao longo do tempo, utilizando YOLOv5 neste ambiente de desenvolvimento.
Palavras-chave:
YOLO, software engineering, computational intelligence, computer graphics and image processing, contamination risk
Referências
WORLD HEALTH ORGANIZATION et al. WHO-convened global study of origins of SARS-CoV-2: China Part. 2021.
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].
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].
Publicado
31/07/2022
Como Citar
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 YOLOv5. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (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.
