An Automated Face-mask Detection System using YOLOv5 for Preventing Spread of COVID-19


  • Md Asifuzzaman Jishan
  • Md Shahabub Alam
  • Imran Rashid Mazumder
  • Khan Raqib Mahmud
  • Abul Kalam Al Azad



Mask detection, Object detection, Image classification, YOLOv5, COVID-19, Pandemic


Object detection systems based on deep learning have been immensely successful in
complex object detection tasks images and have shown potential in a wide range of real-life applications
including the COVID-19 pandemic. One of the key challenges in containing and mitigating the infection
among the population is to ensure and enforce the proper use of face masks. The objective of this paper
is to detect the proper use of facial masks among the urban population in a megacity. In this study, we
trained and validated a new dataset to detect images such as ‘with mask’, ‘without mask’, and ‘mask
not in position’ using YOLOv5. The dataset is comprised of 6550 images with the three classes. The
dataset attained a commendable performance accuracy of 95% on mAP. This study can be implemented
for automated scanning for monitoring the proper use of face masks in different settings of public spaces.


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How to Cite

Jishan, M. A., Alam, M. S., Mazumder, I. R., Mahmud, K. R., & Al Azad, A. K. (2023). An Automated Face-mask Detection System using YOLOv5 for Preventing Spread of COVID-19. International Journal of Computing, 22(1), 58-68.