An Edge-Controlled Outdoor Autonomous UAV for Colorwise Safety Helmet Detection and Counting of Workers in Construction Sites

Susanta Sharma, Allumallu Veera Venkata Susmitha, Lan Da Van, Yu Chee Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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%.

Original languageEnglish
Title of host publication2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413688
DOIs
StatePublished - 2021
Event94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
Duration: 27 Sep 202130 Sep 2021

Publication series

NameIEEE Vehicular Technology Conference
Volume2021-September
ISSN (Print)1550-2252

Conference

Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Country/TerritoryUnited States
CityVirtual, Online
Period27/09/2130/09/21

Keywords

  • Autonomous flying
  • computer vision
  • deep learning
  • edge computing
  • UA V (Unmanned Arial Vehicle)

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