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

In this paper, an edge-computed and controlled outdoor autonomous UAV 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 UAV navigation, 2) the implementation of real-time 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%.