Um Sistema Integrado para Monitoramento Automático do Uso de EPIs na Indústria Alimentícia com Detecção Visual e Atuação Embarcada em Tempo Real
Resumo
A verificação do uso de Equipamentos de Proteção Individual (EPIs) é fundamental em ambientes do setor alimentício. Este trabalho apresenta um sistema integrado de monitoramento em tempo real que combina detecção automática de EPIs com comunicação assíncrona e atuação embarcada. A solução foi avaliada experimentalmente em ambiente laboratorial adaptado, com características operacionais semelhantes a linhas de envase. O sistema alcançou mAP@50 de aproximadamente 90% e tempo médio de resposta de 140 ms, demonstrando viabilidade técnica para aplicações de monitoramento contínuo.Referências
Al-Azani, S., Luqman, H., Alfarraj, M., Sidig, A. A. I., Khan, A. H., and Al-Hamed, D. (2024). Real-time monitoring of personal protective equipment compliance in surveillance cameras. IEEE Access, 12:121882–121895.
Ali, L., Alnajjar, F., Parambil, M. M. A., Younes, M. I., Abdelhalim, Z. I., and Aljassmi, H. (2022). Development of yolov5-based real-time smart monitoring system for increasing lab safety awareness in educational institutions. Sensors, 22(22):8820.
Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal.
Barro-Torres, S., Fernández-Caramés, T. M., Pérez-Iglesias, H. J., and Escudero, C. J. (2012). Real-time personal protective equipment monitoring system. Computer Communications, 36(1):42–50.
Bavaresco, F. et al. (2021). Internet of things and occupational well-being in industry 4.0: A systematic mapping study and taxonomy. Computers & Industrial Engineering, 161:107670.
Hu, P., Dhelim, S., Ning, H., and Qiu, T. (2017). Survey on fog computing: architecture, key technologies, applications and open issues. Journal of network and computer applications, 98:27–42.
Lo, J.-H., Lin, L.-K., and Hung, C.-C. (2022). Real-time personal protective equipment compliance detection based on deep learning algorithm. Sustainability, 15(1):391.
Lopes, B. R. d. S., Vigoder, H. C., Medeiros, M. d. G. G. d. A., et al. (2022). Use of personal protective equipment and its implications in food hygiene: literature review.
Protik, A. A., Rafi, A. H., and Siddique, S. (2021). Real-time personal protective equipment (ppe) detection using yolov4 and tensorflow. In 2021 IEEE region 10 symposium (TENSYMP), pages 1–6. IEEE.
Redmon, J. et al. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788, Las Vegas. IEEE.
Yang, X., Yu, Y., Shirowzhan, S., Li, H., et al. (2020). Automated ppe-tool pair check system for construction safety using smart iot. Journal of Building Engineering, 32:101721.
Zhou, H. A., Wolfsschläger, D., Florides, C., et al. (2025). Generative ai in industrial machine vision: A review. Journal of Intelligent Manufacturing.
Zorzenon, R., Lizarelli, F. L., and Braatz, D. (2022). The impact of the internet of things on health and safety performance at work: An empirical study of brazilian companies. International Journal of Production Economics, 243:108331.
Ali, L., Alnajjar, F., Parambil, M. M. A., Younes, M. I., Abdelhalim, Z. I., and Aljassmi, H. (2022). Development of yolov5-based real-time smart monitoring system for increasing lab safety awareness in educational institutions. Sensors, 22(22):8820.
Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal.
Barro-Torres, S., Fernández-Caramés, T. M., Pérez-Iglesias, H. J., and Escudero, C. J. (2012). Real-time personal protective equipment monitoring system. Computer Communications, 36(1):42–50.
Bavaresco, F. et al. (2021). Internet of things and occupational well-being in industry 4.0: A systematic mapping study and taxonomy. Computers & Industrial Engineering, 161:107670.
Hu, P., Dhelim, S., Ning, H., and Qiu, T. (2017). Survey on fog computing: architecture, key technologies, applications and open issues. Journal of network and computer applications, 98:27–42.
Lo, J.-H., Lin, L.-K., and Hung, C.-C. (2022). Real-time personal protective equipment compliance detection based on deep learning algorithm. Sustainability, 15(1):391.
Lopes, B. R. d. S., Vigoder, H. C., Medeiros, M. d. G. G. d. A., et al. (2022). Use of personal protective equipment and its implications in food hygiene: literature review.
Protik, A. A., Rafi, A. H., and Siddique, S. (2021). Real-time personal protective equipment (ppe) detection using yolov4 and tensorflow. In 2021 IEEE region 10 symposium (TENSYMP), pages 1–6. IEEE.
Redmon, J. et al. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788, Las Vegas. IEEE.
Yang, X., Yu, Y., Shirowzhan, S., Li, H., et al. (2020). Automated ppe-tool pair check system for construction safety using smart iot. Journal of Building Engineering, 32:101721.
Zhou, H. A., Wolfsschläger, D., Florides, C., et al. (2025). Generative ai in industrial machine vision: A review. Journal of Intelligent Manufacturing.
Zorzenon, R., Lizarelli, F. L., and Braatz, D. (2022). The impact of the internet of things on health and safety performance at work: An empirical study of brazilian companies. International Journal of Production Economics, 243:108331.
Publicado
25/05/2026
Como Citar
LIMA, Glauber Giordano De Morais; GUIMARÃES, Fabrício Moura; SANTOS, Caique Nascimento dos; MACHADO, Warlles Carlos Costa; SILVA NETO, José Soares da; SILVA, Melina da Conceição Macêdo da; ARAÚJO, Arlino Henrique Magalhães de.
Um Sistema Integrado para Monitoramento Automático do Uso de EPIs na Indústria Alimentícia com Detecção Visual e Atuação Embarcada em Tempo Real. In: TRILHA DE INDÚSTRIA E INOVAÇÃO EM SI - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 22. , 2026, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 86-90.
DOI: https://doi.org/10.5753/sbsi_estendido.2026.249025.
