| 利用无人机多源影像检测车辆速度 |
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| 引用本文： | 姜尚洁,罗斌,贺鹏,杨国鹏,顾亚平,刘军,张云,张良培.利用无人机多源影像检测车辆速度[J].测绘学报,2018,47(9):1228-1237. |
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| 作者姓名： | 姜尚洁 罗斌 贺鹏 杨国鹏 顾亚平 刘军 张云 张良培 |
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| 作者单位： | 1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079;2. 解放军 95899部队, 北京 100085;3. 淮安市水利勘测设计研究院有限公司, 江苏 淮安 223000 |
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| 摘 要： | 交通在人民生活和社会经济中有着举足轻重的作用。车辆速度检测是智能交通管理系统的重要组成部分。本文提出了一种基于无人机（UAV）多源影像数据进行车辆速度检测的方法，首先，搭建小型无人机多源数据采集平台，获取可见光影像与热红外影像。然后，针对采集的多源数据，采用深度学习框架YOLO（you only look once）进行车辆检测。最后，基于卡尔曼滤波进行车辆跟踪，并根据跟踪结果计算车辆速度。本文利用无人机平台增加监测车辆的灵活性，同时综合使用多源数据，不仅提高车辆检测精度，还可以不依赖光照条件跟踪车辆。试验结果表明，本文方法具有有效性和稳健性，为道路监控管理部门提供一种高效率、机动灵活的监测模式。
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| 关 键 词： | 无人机 车辆速度检测 深度学习 热红外影像 |
| 收稿时间： | 2017-09-07 |
| 修稿时间： | 2018-04-28 |
Vehicle Speed Detection by Multi-source Images from UAV |
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| JIANG Shangjie,LUO Bin,HE Peng,YANG Guopeng,GU Yaping,LIU Jun,ZHANG Yun,ZHANG Liangpei.Vehicle Speed Detection by Multi-source Images from UAV[J].Acta Geodaetica et Cartographica Sinica,2018,47(9):1228-1237. |
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| Authors: | JIANG Shangjie LUO Bin HE Peng YANG Guopeng GU Yaping LIU Jun ZHANG Yun ZHANG Liangpei |
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| Institution: | 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;2. 95899 Troops, People's Liberation Army, Beijing 100085, China;3. Huai'an Surveying and Design Institute of Water Resources Co., Ltd., Huai'an 223000, China |
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| Abstract: | Traffic plays a vital role in people's life and social economy.Vehicle speed detection is an important part of intelligent transportation system.This paper focus on the vehicle speed detection based on multi-source data from autonomous unmanned aerial vehicle (UAV).Firstly,we build a multi-source data acquisition system on UAV for visible image and thermal infrared image.Secondly,we utilize "You only look once" (YOLO),which is a deep learning framework for vehicle detection.Finally,we track the vehicle based on Kalman filter and calculated the vehicle speed according to the result of vehicle tracking.This paper adopts the UAV platform to increase the flexibility.While the use of multi-source data improves the accuracy of the vehicle detection and tracks the vehicle in different illumination.The result of experiments shows that the strategy is effective and robust,which provides an efficient and flexible monitoring mode for traffic management department. |
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