发表机构
University of Cyprus; KIOS Research and Innovation Centre of Excellence (KIOS CoE)（塞浦路斯大学; KIOS卓越研究与创新中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该综述聚焦无人机交通监测的视觉车辆检测，梳理相关进展，明确兼容交通控制、实时处理等挑战，提出未来需优化检测模型、边缘处理等方向以提升城市交通管理响应性。
AI 中文摘要
在智能交通系统（ITS）中，基于无人机（UAV）的监控为交通监控提供了一种创新解决方案，具有覆盖范围广、能实时收集数据的优势。与固定地面基础设施相比，无人机可响应动态交通，但也带来诸多挑战，如不同高度下的车辆检测、补偿运动引起的图像变化、高效处理高分辨率图像等。深度学习在提升检测准确率方面成效显著，但实际部署时，需谨慎评估准确率、延迟以及与现有交通系统的协调性。本综述梳理了基于无人机的交通监测领域的最新进展，重点关注适用于各类城市环境交通分析的深度神经网络模型。文献中明确的三大主要挑战为：确保与交通控制系统兼容、实现实时处理以优化交通流、在不同环境条件下维持鲁棒的检测性能。现有解决方案往往缺乏利用无人机采集的数据来有效应对事件、管理交通的综合框架。未来研究应聚焦于最优检测模型、边缘处理及自适应控制集成，以提升城市交通管理的响应性。
英文摘要
In Intelligent Transportation System (ITS), unmanned aerial vehicle (UAV)-based surveillance offers an innovative solution to traffic surveillance with wide coverage and real-time data collection capabilities. In comparison to fixed ground-based infrastructure, UAVs are able to respond to dynamic traffic but present challenges such as vehicle detection at varying altitudes, compensation for motion-induced image variations and efficient processing of high-resolution images. Deep learning has been largely beneficial on improving the detection accuracy; however, for practical deployment, a critical assessment of the accuracy, latency, and harmonization with current transportation systems needs to be carefully considered. This survey reviews recent advancements in the UAV-based traffic monitoring, with a primary focus being deep neural network models for traffic analytics in various urban settings. Three main challenges identified in the literature are ensuring compatibility with traffic control systems, achieving real-time processing to optimize traffic flow, and maintaining robust detection in different environmental conditions. Existing solutions often lack comprehensive frameworks for utilizing UAV captured data to respond to incidents and manage traffic effectively. Future research should focus on optimal detection models, edge processing, and adaptive control integration to improve the responsiveness of urban traffic management.
Journal ref2026 IEEE Transactions on Intelligent Transportation Systems (T-ITS)
