Abstract

During the Covid pandemic, the importance of wearing mask has been noted globally. Additionally, crowded human clusters facilitated the transmission of the virus, which brings up the need for new systems for monitoring such situations. To address such issues, this research proposes an object recognition visual system based on deep learning to monitor the mask-wearing in a certain space and the control of the number of people indoors as an important tool during an epidemic. This research mainly investigates two types of identification. The first is to monitor whether people entering the site wear a mask at the entrance and exit of the field, and the second is to count the number of people entering a specific area. Experimental results show that by utilising the visual sensor, it is possible to detect and identify the people who frequently enter and exit in real-time. An advanced transfer learning approach has been employed to achieve the best discrimination performance. The actual training results prove that the migration learning Mask R-CNN algorithm produced by this method and the original Mask R-CNN algorithm have increased the mAP by 3

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Cite this article

@article{cai-5691,
  author  = {Chan-Yun Yang and Hooman Samani and Nana Ji and Chunxu Li and Ding-Bang Chen and Man Qi},
  title   = {Deep Learning Based Real-Time Facial Mask Detection and Crowd Monitoring},
  journal = {Computing and Informatics},
  volume  = {40},
  number  = {6},
  pages   = {1263--1294},
  year    = {2021},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2021_6_1263}
}
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