用低成本无人机实现人脸检测、识别与深度估计的实时智能系统
Real-Time Visual Intelligence on Low-Cost UAVs: A Modular Approach for Tracking, Scanning, and Navigation
- 基于轻量模型构建模块化架构，支持单目视觉深度估计
- 在真实场景中实现人物追踪、室内扫描与虚拟传感器导航
- 开源且适配嵌入式设备，适合军事救援等低资源应用
自主无人机正迅速改变现代战争与民用领域。本文基于DJI Tello平台开发了一套集成智能无人机系统，作为个人助手使用。系统采用模块化设计，融合三项核心人工智能功能：人脸检测、人脸识别与单目视觉深度估计。通过基于Web的界面实现无人机远程控制与实时视频监控，利用基于Python的服务器处理视觉数据，并在嵌入式系统上运行轻量化神经网络模型执行推理。相比现有商业方案，本系统强调可及性、低成本硬件与开源技术。实验表明，该系统在真实环境中具备鲁棒性能，支持人物追踪、室内扫描及基于虚拟传感器的自主路径跟随。本研究验证了先进AI技术在实时机器人系统中的适用性，展示了其在资源受限硬件上的可行性，为未来军事、救援与监控领域的自主无人机研究提供基础。
原文摘要 · Abstract (English)
Autonomous drones are rapidly transforming modern warfare and civil applications alike. This paper presents the development of an integrated intelligent drone system designed to serve as a personal assistant. Leveraging the DJI Tello drone platform, we implemented a modular architecture that integrates three core artificial intelligence functionalities: facial detection, facial recognition, and depth estimation from monocular vision. A web-based interface enables seamless drone control and real-time video monitoring, while a Python-based server processes visual data and executes inference pipelines using lightweight neural models optimized for embedded systems. Unlike existing commercial solutions, this system emphasizes accessibility, low-cost hardware, and open-source technologies. The system demonstrates robust performance in real-world conditions, including person tracking, indoor scanning, and autonomous line following using virtual sensors. This project validates the applicability of advanced AI techniques in real-time robotic systems and illustrates the feasibility of deploying them on constrained hardware, providing a foundation for future research in autonomous UAVs for military, rescue, and surveillance missions.
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