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


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.

Palavras-chave: smart cities, object recognition, accessibility


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ADORNO, Pedro Lucas Vieira; JASENOVSKI, Igor Mateus; SANTIAGO, Diogo Felipe de Melo; BERGAMASCO, Leila. Automatic detection of people with reduced mobility using YOLOv5 and data reduction strategy. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 19. , 2023, Maceió/AL. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 .