# E3-UAV: An Edge-based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles

arXiv: https://arxiv.org/abs/2308.04774 · HTML: https://mlsanity.com/paper/2308.04774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 71 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 27 of 27)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios](https://mlsanity.com/paper/2108.11539/) (2021)
- [Vision Meets Drones: A Challenge](https://mlsanity.com/paper/1804.07437/) (2018)
- [AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance](https://mlsanity.com/paper/2001.11737/) (2020)
- [Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset](https://mlsanity.com/paper/2012.14036/) (2020)
- [SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems](https://mlsanity.com/paper/1909.09709/) (2019)
- [SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications](https://mlsanity.com/paper/1907.11093/) (2019)
- [Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection](https://mlsanity.com/paper/2011.03822/) (2020)
- [DAC-SDC Low Power Object Detection Challenge for UAV Applications](https://mlsanity.com/paper/1809.00110/) (2018)
- [Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2007.15105/) (2020)
- [SyNet: An Ensemble Network for Object Detection in UAV Images](https://mlsanity.com/paper/2012.12991/) (2020)
- [DroNet: Efficient convolutional neural network detector for real-time UAV applications](https://mlsanity.com/paper/1807.06789/) (2018)
- [Energy Efficient UAV Communication with Energy Harvesting](https://mlsanity.com/paper/1911.00351/) (2019)
- [Trajectory Design for Energy Minimization in UAV-enabled Wireless Communications with Latency Constraints](https://mlsanity.com/paper/1910.08612/) (2019)
- [ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery](https://mlsanity.com/paper/1811.06318/) (2018)

## Built on by (most cited first, 4 of 4)

- [Real-Time AIoT for AAV Antenna Interference Detection via Edge-Cloud Collaboration](https://mlsanity.com/paper/2412.03055/) (2024)
- [Energy-Efficient Fast Object Detection on Edge Devices for IoT Systems](https://mlsanity.com/paper/2602.09515/) (2026)
- [ED3R: Energy-Aware Distributed Disaster Detection via Cooperative Agents in Robotic Systems](https://mlsanity.com/paper/2606.17739/) (2026)
- [D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems](https://mlsanity.com/paper/2609.07350/) (2026)

## Abstract (arXiv)

Motivated by the advances in deep learning techniques, the application of Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E3-UAV, an edge-based energy-efficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further.