TY - GEN
T1 - An Edge-Controlled Outdoor Autonomous UAV for Colorwise Safety Helmet Detection and Counting of Workers in Construction Sites
AU - Sharma, Susanta
AU - Venkata Susmitha, Allumallu Veera
AU - Van, Lan Da
AU - Tseng, Yu Chee
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this paper, an edge-computed and controlled outdoor autonomous UA V system is proposed to monitor the safety helmet wearing of workers in construction sites. Detection and counting of the workers with safety helmets of specified colors and those without safety helmets is the main focus of this work. Five standard safety helmet colors including blue, orange, red, white, and yellow are considered. The novelties of the work are 1) the design of a modularized software architecture running on an Android smartphone as an edge device for outdoor autonomous UA V navigation, 2) the implementation of realtime colorwise detection and counting of workers with and without safety helmets from UAV's first-person view (FPV), 3) the implementation of a simple upper-side cropping and hue, saturation, value (HSV) filtering method for color decision. The resulting average safety helmet detection accuracy for 10 different cases is 81.02%.
AB - In this paper, an edge-computed and controlled outdoor autonomous UA V system is proposed to monitor the safety helmet wearing of workers in construction sites. Detection and counting of the workers with safety helmets of specified colors and those without safety helmets is the main focus of this work. Five standard safety helmet colors including blue, orange, red, white, and yellow are considered. The novelties of the work are 1) the design of a modularized software architecture running on an Android smartphone as an edge device for outdoor autonomous UA V navigation, 2) the implementation of realtime colorwise detection and counting of workers with and without safety helmets from UAV's first-person view (FPV), 3) the implementation of a simple upper-side cropping and hue, saturation, value (HSV) filtering method for color decision. The resulting average safety helmet detection accuracy for 10 different cases is 81.02%.
KW - Autonomous flying
KW - UA V (Unmanned Arial Vehicle)
KW - computer vision
KW - deep learning
KW - edge computing
UR - http://www.scopus.com/inward/record.url?scp=85123006557&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Fall52928.2021.9625393
DO - 10.1109/VTC2021-Fall52928.2021.9625393
M3 - Conference contribution
AN - SCOPUS:85123006557
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Y2 - 27 September 2021 through 30 September 2021
ER -