Loading...
Publication
Human detector smart sensor for autonomous disinfection mobile robot
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 2.71 MB | Adobe PDF |
Advisor(s)
Abstract(s)
The COVID-19 virus outbreak led to the need of developing
smart disinfection systems, not only to protect the people that usually
frequent public spaces but also to protect those who have to subject
themselves to the contaminated areas. In this paper it is developed a
human detector smart sensor for autonomous disinfection mobile robot
that use Ultra Violet C type light for the disinfection task and stops
the disinfection system when a human is detected around the robot in
all directions. UVC light is dangerous for humans and thus the need
for a human detection system that will protect them by disabling the
disinfection process, as soon as a person is detected. This system uses
a Raspberry Pi Camera with a Single Shot Detector (SSD) Mobilenet
neural network to identify and detect persons. It also has a FLIR 3.5
Thermal camera that measures temperatures that are used to detect
humans when within a certain range of temperatures. The normal human
skin temperature is the reference value for the range definition. The
results show that the fusion of both sensors data improves the system
performance, compared to when the sensors are used individually. One
of the tests performed proves that the system is able to distinguish a
person in a picture from a real person by fusing the thermal camera and
the visible light camera data. The detection results validate the proposed
system.
Description
Keywords
Smart sensor Human detection Neural network
Pedagogical Context
Citation
Mendonça, Hugo; Lima, José; Costa, Paulo; Moreira, António Paulo; Santos, Filipe (2021). Human detector smart sensor for autonomous disinfection mobile robot. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 171-186. ISBN 978-3-030-91884-2
Publisher
Springer Nature