TY - GEN
T1 - Developing Smart COVID-19 Social Distancing Surveillance Drone using YOLO Implemented in Robot Operating System simulation environment
AU - Somaldo, Pray
AU - Ferdiansyah, Faizal Adila
AU - Jati, Grafika
AU - Jatmiko, Wisnu
N1 - Funding Information:
This work is supported by Publikasi Terindeks Internasional (PUTI) Prosiding Grant 2020 from Universitas Indonesia entitled "Development of Efficient Object Detection and Identification System with Unmanned Aerial Vehicles (UAVs) for Disaster Management" with No NKB- 852/UN2.RST/HKP.05.00/2020. Thanks to JDERobot Programming Robot Intelligence platform for providing simulation environment base (https://jderobot.github.io/).
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - The Novel Coronavirus, termed as COVID-19 outbreak, is faced by almost all countries in the world. It spread through communal interaction between people, especially in densely populated areas. An effort to prevent Covid-19 transmission is social distancing regulation. However, this policy is not obeyed by the public, so the government needs to supervise the movement and people's interaction. The government needs a crowd surveillance system that can detect people's presence, identify the crowd, and give social distancing warnings. Therefore, we propose a drone that has the ability of localization, navigation, people detection, crowd identifier, and social distancing warning. We utilize YOLO-v3 to detect people and define adaptive social distancing detector. In this paper, we implemented a road segmentation on the IRIS PX4 drone in the Robot Operating System and Gazebo simulation. The proposed system also successfully demonstrated people and crowd detection with varying degrees of the crowd. The system obtained crowd detection accuracy is around 90% and expected to be readily implemented on real hardware drones and tested in real environments.
AB - The Novel Coronavirus, termed as COVID-19 outbreak, is faced by almost all countries in the world. It spread through communal interaction between people, especially in densely populated areas. An effort to prevent Covid-19 transmission is social distancing regulation. However, this policy is not obeyed by the public, so the government needs to supervise the movement and people's interaction. The government needs a crowd surveillance system that can detect people's presence, identify the crowd, and give social distancing warnings. Therefore, we propose a drone that has the ability of localization, navigation, people detection, crowd identifier, and social distancing warning. We utilize YOLO-v3 to detect people and define adaptive social distancing detector. In this paper, we implemented a road segmentation on the IRIS PX4 drone in the Robot Operating System and Gazebo simulation. The proposed system also successfully demonstrated people and crowd detection with varying degrees of the crowd. The system obtained crowd detection accuracy is around 90% and expected to be readily implemented on real hardware drones and tested in real environments.
KW - COVID-19
KW - Drone
KW - Robot Operating System
KW - Social Distancing
KW - YOLO
UR - http://www.scopus.com/inward/record.url?scp=85102032608&partnerID=8YFLogxK
U2 - 10.1109/R10-HTC49770.2020.9357040
DO - 10.1109/R10-HTC49770.2020.9357040
M3 - Conference contribution
AN - SCOPUS:85102032608
T3 - IEEE Region 10 Humanitarian Technology Conference, R10-HTC
BT - Proceedings of 2020 IEEE 8th R10 Humanitarian Technology Conference, R10-HTC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE R10 Humanitarian Technology Conference, R10-HTC 2020
Y2 - 1 December 2020 through 3 December 2020
ER -
