低成本无人机上的实时视觉智能：一种用于跟踪、扫描和导航的模块化方法
Real-Time Visual Intelligence on Low-Cost UAVs: A Modular Approach for Tracking, Scanning, and Navigation
AI总结：
基于DJI Tello平台，采用模块化架构集成面部检测、识别和单目深度估计，通过轻量神经网络实现实时跟踪、扫描和导航，验证了AI在受限硬件上的可行性。
摘要：
自主无人机正迅速改变现代战争和民用应用。本文介绍了一种集成智能无人机系统的开发，旨在作为个人助理。利用DJI Tello无人机平台，我们实现了一种模块化架构，集成了三种核心人工智能功能：面部检测、面部识别和单目深度估计。基于Web的界面实现了无缝的无人机控制和实时视频监控，而基于Python的服务器处理视觉数据并使用针对嵌入式系统优化的轻量级神经网络执行推理流水线。与现有的商业解决方案不同，该系统强调可访问性、低成本硬件和开源技术。该系统在真实世界条件下展示了稳健的性能，包括人员跟踪、室内扫描和使用虚拟传感器的自主线路跟随。该项目验证了先进AI技术在实时机器人系统中的应用性，并说明了在受限硬件上部署它们的可行性，为未来在军事、救援和监视任务中的自主无人机研究奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
