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

arXiv: https://arxiv.org/abs/2111.15481 · HTML: https://mlsanity.com/paper/2111.15481/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints](https://mlsanity.com/paper/2011.14858/) (2020)
- [Deep Learning at the Edge](https://mlsanity.com/paper/1910.10231/) (2019)
- [Measuring what Really Matters: Optimizing Neural Networks for TinyML](https://mlsanity.com/paper/2104.10645/) (2021)
- [Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements and Challenges](https://mlsanity.com/paper/2007.12874/) (2020)
- [Openmv: A Python powered, extensible machine vision camera](https://mlsanity.com/paper/1711.10464/) (2017)
- [Trajectory Optimization of Flying Energy Sources using Q-Learning to Recharge Hotspot UAVs](https://mlsanity.com/paper/2003.12258/) (2020)

## Built on by (most cited first, 2 of 2)

- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation](https://mlsanity.com/paper/2112.09775/) (2021)

## Abstract (arXiv)

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.