Automatic detection of people with reduced mobility using YOLOv5 and data reduction strategy

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dc.contributor.advisorOrcidhttps://orcid.org/0000-0002-8001-8053
dc.contributor.authorADORNO, P. L. V.
dc.contributor.authorJASENOVSKI, I. M.
dc.contributor.authorSANTIAGO, D. F. DE M.
dc.contributor.authorLeila Bergamasco
dc.date.accessioned2023-09-01T06:04:05Z
dc.date.available2023-09-01T06:04:05Z
dc.date.issued2023-05-29
dc.description.abstract© 2023 Copyright held by the owner/author(s).Context: A portion of the users in the São Paulo Metro are people who have physical limitations and need the help of wheelchairs or other similar devices. In this way, the Metro stations have elevators that allow these users to move between the floors of the station. In order, for the elevator to be used, it is necessary for the user to call the operators of the stations, who, in turn, check if the user who is requesting access to the elevator fits the target audience. Problem: This type of request requires manual validation by station operators, causing interruptions in their work routines and delays in passenger travel. Solution: To implement and evaluate artificial intelligence methods for automatic detection of people in wheelchairs or other auxiliary devices. IS Theory: This project was idealized from the perspective of Customer Focus Theory. Method: The You Only Look Once (YOLOv5) neural network was implemented in the Mobility Aids database. Tests were performed considering the original and modified base, composed of a reduced number of images, aiming to assess whether the accuracy of the model remains even with reduced database data. Summary of Results: The results obtained show an average accuracy of more than 92% with the modified database. Contribution: The results corroborated our methodology and we will be able to test in Sao Paulo subway with real images. In a long term, It is expected that by automating such a task, operators will be less overloaded and passengers with reduced mobility will gain more autonomy.
dc.description.firstpage9
dc.description.lastpage16
dc.identifier.citationADORNO, P. L. V.; JASENOVSKI, I. M.; SANTIAGO, D. F. DE M.; BERGAMASCO, L. C. C. Automatic detection of people with reduced mobility using YOLOv5 and data reduction strategy. ACM International Conference Proceeding Series, p. 9-16, may. 2023.
dc.identifier.doi10.1145/3592813.3592883
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/5086
dc.relation.ispartofACM International Conference Proceeding Series
dc.rightsAcesso Restrito
dc.subject.otherlanguageaccessibility
dc.subject.otherlanguageobject recognition
dc.subject.otherlanguagesmart cities
dc.titleAutomatic detection of people with reduced mobility using YOLOv5 and data reduction strategy
dc.typeArtigo de evento
fei.scopus.citations0
fei.scopus.eid2-s2.0-85165967318
fei.scopus.subjectAccessibility
fei.scopus.subjectAutomatic Detection
fei.scopus.subjectMetro stations
fei.scopus.subjectObjects recognition
fei.scopus.subjectPeople with reduced mobilities
fei.scopus.subjectPhysical limitations
fei.scopus.subjectReduction strategy
fei.scopus.subjectSao Paulo Metro
fei.scopus.subjectStation operators
fei.scopus.subjectTarget audience
fei.scopus.updated2024-12-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85165967318&origin=inward
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