Paper Info
Title
DEEP LEARNING BASED REAL-TIME FACIAL MASK DETECTION AND CROWD MONITORING
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 %, reaching a mAP of 96 %. In addition, the accuracy of the random sampling and identification in actual scenes has reached 92.1 %. The developed deep learning vision system has an enhanced identification ability for the verification and analysis of actual scenes and has a great application potential.
Year
DOI
Venue
2021
10.31577/cai_2021_6_1263
COMPUTING AND INFORMATICS
Keywords
DocType
Volume
Mask R-CNN, object detection, people flow control, deep learning, transfer learning
Journal
40
Issue
ISSN
Citations 
6
1335-9150
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chan-Yun Yang100.34
Hooman Samani200.68
Nana Ji300.34
Chunxu Li400.34
Ding-Bang Chen500.34
Man Qi600.34