drones Article Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs Wamiq Raza * ,† , Anas Osman † , Francesco Ferrini and Francesco De Natale   Citation: 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/drones 5040127 Academic Editors: Higinio González Jorge, Luis Miguel González de Santos and Abdessattar Abdelkefi Received: 17 September 2021 Accepted: 25 October 2021 Published: 29 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Information Engineering and Computer Science—DISI, University of Trento, 38122 Trento, Italy; anas.osman@studenti.unitn.it (A.O.); francesco.ferrini@studenti.unitn.it (F.F.); francesco.denatale@unitn.it (F.D.N.) * Correspondence: wamiq.raza@studenti.unitn.it † These authors contributed equally to this work. Abstract: In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dra- matically. 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 ad- vanced 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. Keywords: UAVs; energy efficiency; TinyML; microcontrollers; machine learning; deep learning; edge computing 1. Introduction Drones, in the form of both Remotely Piloted Aerial Systems (RPAS) and unmanned aerial vehicles (UAV), are increasingly being used to revolutionize many existing applica- tions. The Internet of Things (IoT) is becoming more ubiquitous every day, thanks to the widespread adoption and integration of mobile robots into IoT ecosystems. As the world becomes more dependent on technology, there is a growing need for autonomous systems that support the activities and mitigate the risks for human operators [1]. In this context, UAVs are becoming increasingly popular in a range of civil and military applications such as smart agriculture [2], defense [3], construction site monitoring [4], and environmental monitoring [5]. These aerial vehicles are subject to numerous limitations such as safety, energy, weight, and space requirements. Electrically powered UAVs, which represent the majority of micro aerial vehicles, show a severe limitation in the duration of batteries, which are necessarily small due to design constraints. This problem affects both the flight duration and the capability of performing fast maneuvers (e.g., to avoid obstacles) due to the slow power response of the battery. Therefore, despite their unique capabilities and virtually unlimited opportunities, the practical application of UAVs still suffers from significant restrictions [6]. Recent advances in embedded systems through IoT devices could open new and interesting possibilities in this domain. Edge computing brings new insights into existing IoT environments by solving many critical challenges. Deep learning (DL) at the edge presents significant advantages with respect to its distributed counterpart: it allows the Drones 2021, 5, 127. https://doi.org/10.3390/drones5040127 https://www.mdpi.com/journal/drones