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Article

Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

Department of Information Engineering and Computer Science—DISI, University of Trento, 38122 Trento, Italy
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Higinio González Jorge, Luis Miguel González de Santos and Abdessattar Abdelkefi
Drones 2021, 5(4), 127; https://doi.org/10.3390/drones5040127
Received: 17 September 2021 / Revised: 23 October 2021 / Accepted: 25 October 2021 / Published: 29 October 2021
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capabilities in an energy-effective way would be extremely beneficial. In this paper, we propose a practical solution to this problem that exploits deep learning on the edge. The developed system integrates an OpenMV microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The microcontroller hosts a set of machine learning-enabled inference tools that cooperate to control the navigation of the drone and complete a given mission objective. The goal of this approach is to leverage the new opportunistic features of TinyML through OpenMV including offline inference, low latency, energy efficiency, and data security. The approach is successfully validated on a practical application consisting of the onboard detection of people wearing protection masks in a crowded environment. View Full-Text
Keywords: UAVs; energy efficiency; TinyML; microcontrollers; machine learning; deep learning; edge computing UAVs; energy efficiency; TinyML; microcontrollers; machine learning; deep learning; edge computing
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MDPI and ACS Style

Raza, W.; Osman, A.; Ferrini, F.; Natale, F.D. Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs. Drones 2021, 5, 127. https://doi.org/10.3390/drones5040127

AMA Style

Raza W, Osman A, Ferrini F, Natale FD. Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs. Drones. 2021; 5(4):127. https://doi.org/10.3390/drones5040127

Chicago/Turabian Style

Raza, Wamiq, Anas Osman, Francesco Ferrini, and Francesco De Natale. 2021. "Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs" Drones 5, no. 4: 127. https://doi.org/10.3390/drones5040127

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