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

TL;DR

This paper presents an energy-efficient deep learning system on a microcontroller integrated into a UAV, enabling onboard detection tasks that extend flight time and enhance decision-making capabilities.

Contribution

It introduces a practical TinyML-based solution on an OpenMV microcontroller for UAV navigation and detection tasks, demonstrating energy-efficient onboard inference.

Findings

Successful onboard mask detection in crowded environments

Extended UAV flight time due to low power consumption

Real-time inference with low latency on microcontroller

Abstract

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.