基于无人机实时视频流的端到端人体检测框架，解决高空目标尺度变化问题。
End-to-End Real-Time Drone-Based Person Detection Framework Using Deep Learning
- 采用YOLOv8-nano架构，结合飞行实验优化多高度检测性能。
- 在16-25米高度下精度达57.4%，帧率稳定超41 FPS，最高达50 FPS。
- 适合需实时空中监控的安防、搜救等场景应用。
近年来，无人机（UAV）在安全监控、搜索救援（SAR）、边境监视等领域迅速发展。现有监测框架在目标因高度变化导致尺度剧烈波动时，常难以保持检测一致性，造成关键信息缺失。为此，本文提出一种基于无线实时无人机视频流的端到端检测流水线。基于YOLOv8-nano架构，在多个飞行高度上开展大量飞行实验以评估检测性能。模型在VisDrone2019数据集上训练，获得57.4%精度、41%召回率、44.8% mAP和20.3% mAP50:95。在真实环境测试中发现，当飞行高度介于16至25米时，算法实现近乎完全的检测可靠性，检测帧率持续高于41 FPS，峰值达到50 FPS。本工作旨在通过无线传输实现在空中平台的实时人体检测，有效应对目标尺度变化与高精度定位双重挑战。
原文摘要 · Abstract (English)
In recent years, Unmanned Aerial Vehicles (UAVs) or drones have gained rapid response in terms of security, search and rescue (SAR), border surveillance, etc. Existing monitoring frameworks often struggle to maintain detection consistency when targets undergo significant scale variations due to altitude changes, leading to critical information gaps. To address this issue, this work proposes an integrated real-time detection pipeline for detecting targets through the wireless live drone video feed. Build upon YOLOv8-nano architecture, extensive flight experiments were conducted to determine the detection performance across multiple flight altitudes. Trained on VisDrone2019 dataset, the results of YOLOv8-nano model achieves 57.4%, 41%, 44.8% and 20.3% in precision, recall, mAP and mAP50:95 respectively. While demonstrating on real environment, this analysis revealed that the algorithm achieves near-total detection reliability at altitudes between 16 and 25 meters with the detection frame rate consistently maintained above 41 FPS and reaching a peak of 50 FPS. However, the goal of this work is to enable real-time person detection from an aerial platform via wireless transmission. This approach effectively addresses the dual challenges of identifying targets at varying scales and ensuring near-to-accurate localization during aerial observation.
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