计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 275-280.doi: 10.11896/jsjkx.200900149

• 智能计算 • 上一篇    下一篇

基于深度学习的无人机航拍车流量监测

牛康力, 谌雨章, 张龚平, 谭前程, 王绎冲, 罗美琪   

  1. 湖北大学计算机与信息工程学院 武汉430062
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 谌雨章(hubucyz@foxmail.com)
  • 作者简介:perfnkl@stu.hubu.edu.cn
  • 基金资助:
    教育部产学合作协同育人项目(201802153126);湖北省自然科学基金面上项目(2019CFB733);大学生创新创业训练计划项目(湖北省级S201910512024,国家级202010512020);湖北大学楚才学院科研立项(20192222011)

Vehicle Flow Measuring of UVA Based on Deep Learning

NIU Kang-li, CHEN Yu-zhang, ZHANG Gong-ping, TAN Qian-cheng, WANG Yi-chong, LUO Mei-qi   

  1. School of Computer and Information Engineering,Hubei University,Wuhan 430062,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:NIU Kang-li,born in 2000,postgra-duate.His main research interests include artificial intelligence and image processing.
    CHEN Yu-zhang,born in 1984,Ph.D,associate professor.His main research interests include laser and LED in water,night vision or underwater scatte-ring medium radiation transmission theory and computer simulation,image
    acquisition and restoration and reconstruction algorithms,image processing algorithms embedded including the research of Android development.
  • Supported by:
    Ministry of Education Industry University Cooperation Collaborative Education Project(201802153126),General Project of Natural Science Foundation of Hubei Province (2019CFB733),Innovation and Entrepreneurship Training Program for College Students (Hubei Provincial S201910512024,National Level 202010512020) and Research Project of Chucai College of Hubei University(20192222011).

摘要: 随着智慧城市概念的普及,交通道路智能化管理已成为学者关注的热点。针对道路的车流量统计问题,文中基于深度学习方法,提出了基于残差网络的无人机航拍车流量监测算法,该算法引入了全连接的多尺度残差学习分块(FMRB),在解决梯度弥散现象的同时使得图像特征能够被更好地提取和学习。现有的车辆检测算法准确率较低,且大多数仅能对车辆进行检测,不能对车流量进行统计。文章结合视频帧估计方法,实现了车流量的实时监测与统计。在车辆检测性能上将所提算法与SSD,YOLOv2,YOLOv3算法进行对比,结果表明,在自建数据集训练的条件下,所提算法引入多尺度残差学习分块(FMRB)对遥感图像进行车辆识别,能够取得更高的识别精度;在实地车流量监测中,所得结果误检率小于1%,具有较强的实用效果。

关键词: 残差网络, 车辆识别, 车流量监测, 深度学习, 智能交通

Abstract: With the popularization of the concept of smart city,the intelligent management of traffic road has become the focus of scholars.In order to solve the problem of road traffic statistics,this paper proposes a residual network based UAV aerial traffic flow measuring algorithm based on residual network.The fully connected multi-scale residual learning block (FMRB) is introduced into the method network to solve the gradient dispersion phenomenon and make the image features better extracted and learned.At present,the accuracy of the existing vehicle detection algorithms is low,and most of them can only detect the vehicle,and can not count the traffic flow.In this paper,combined with video frame estimation method,real-time monitoring and statistics of traffic flow is realized.Compared with SSD,YOLOv2 and YOLOv3 algorithms in vehicle detection performance,the results show that,under the condition of self built data set training,this method introduces multi-scale residual learning block (FMRB) for vehicle recognition of remote sensing image,and can achieve higher recognition accuracy.In the field traffic flow monitoring,the error detection rate is less than 1%,which has strong practical effect.

Key words: Deep learning, Intelligent transportation, Residual network, Traffic flow measuring, Vehicle recongnition

中图分类号: 

  • TN911.73
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