# A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications

arXiv: https://arxiv.org/abs/2502.15737 · HTML: https://mlsanity.com/paper/2502.15737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Real-Time Flying Object Detection with YOLOv8](https://mlsanity.com/paper/2305.09972/) (2023)
- [DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices](https://mlsanity.com/paper/2108.09457/) (2021)
- [Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks](https://mlsanity.com/paper/2203.05025/) (2022)
- [Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles](https://mlsanity.com/paper/2205.14942/) (2022)
- [Learning Vision-based Flight in Drone Swarms by Imitation](https://mlsanity.com/paper/1908.02999/) (2019)
- [DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices](https://mlsanity.com/paper/2202.01699/) (2022)
- [Reducing the Side-Effects of Oscillations in Training of Quantized YOLO Networks](https://mlsanity.com/paper/2311.05109/) (2023)
- [Placement and Allocation of Communications Resources in Slicing-aware Flying Networks](https://mlsanity.com/paper/2112.07048/) (2021)

## Built on by (most cited first, 3 of 3)

- [Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered](https://mlsanity.com/paper/2510.08464/) (2025)
- [Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines](https://mlsanity.com/paper/2608.11770/) (2026)
- [EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression](https://mlsanity.com/paper/2609.11058/) (2026)

## Abstract (arXiv)

Advancements in embedded systems and Artificial Intelligence (AI) have enhanced the capabilities of Unmanned Aircraft Vehicles (UAVs) in computer vision. However, the integration of AI techniques o-nboard drones is constrained by their processing capabilities. In this sense, this study evaluates the deployment of object detection models (YOLOv8n and YOLOv8s) on both resource-constrained edge devices and cloud environments. The objective is to carry out a comparative performance analysis using a representative real-time UAV image processing pipeline. Specifically, the NVIDIA Jetson Orin Nano, Orin NX, and Raspberry Pi 5 (RPI5) devices have been tested to measure their detection accuracy, inference speed, and energy consumption, and the effects of post-training quantization (PTQ). The results show that YOLOv8n surpasses YOLOv8s in its inference speed, achieving 52 FPS on the Jetson Orin NX and 65 fps with INT8 quantization. Conversely, the RPI5 failed to satisfy the real-time processing needs in spite of its suitability for low-energy consumption applications. An analysis of both the cloud-based and edge-based end-to-end processing times showed that increased communication latencies hindered real-time applications, revealing trade-offs between edge (low latency) and cloud processing (quick processing). Overall, these findings contribute to providing recommendations and optimization strategies for the deployment of AI models on UAVs.