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dc.contributor.authorChen, DBen
dc.contributor.authorSamani, Hen
dc.contributor.authorYang, CYen
dc.contributor.authorJan, GEen
dc.date.accessioned2021-11-10T13:32:31Z
dc.identifier.urihttp://hdl.handle.net/10026.1/18308
dc.description.abstract

In this research, a vision system based on pixel-level deep learning image recognition algorithm is proposed. This system can be used to identify whether people entering and exiting a building are wearing a mask, and to calculate the cumulative number of people in a specific space. The system monitors and analyses crowd entering and exiting and as soon as finds that they are not wearing mask or the number of people in a specific space has reached the upper limit, the system sends a warning notification to the administrator. Through advanced detection systems, personnel can be controlled, especially for the prevention of infectious diseases during pandemics. The proposed system could be significant module for artificial intelligent systems developed for pandemics.

dc.language.isoenen
dc.titleCrowd Monitoring for Pandemic using Mask R-CNNen
dc.typeJournal Article
plymouth.journalJournal of Industrial Mechatronicsen
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2021-08-18en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeJournal Article/Reviewen


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