基于YOLO和嵌入式系统的车流量检测

马永杰; 宋晓凤

doi:10.3788/YJYXS20193406.0613

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基于YOLO和嵌入式系统的车流量检测

图像处理 | 更新时间：2020-07-05

- 基于YOLO和嵌入式系统的车流量检测
- Vehicle flow detection based on YOLO and embedded system
- 液晶与显示 2019年34卷第6期页码：613-618
- 作者机构：
  
  西北师范大学物理与电子工程学院, 甘肃兰州 730070
- 作者简介：
  
  [ "马永杰(1967-), 男, 甘肃灵台人, 博士, 教授, 硕士生导师, 2011年于兰州交通大学获得博士学位, 主要从事进化算法、图像处理、测控技术及应用方面的研究。E-mail:myjmyj@nwnu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(No.41461078);西北师范大学科研能力提升团队项目(No.NWNU-LKQN-1706);National Natural Science Foundation of China(No.41461078);Northwest Normal University Research Capability Improvement Team Project(No.NWNU-LKQN-1706)
- DOI：10.3788/YJYXS20193406.0613
  中图分类号： TP29
- 收稿日期：2018-11-16，
  
  录用日期：2019-2-26，
  
  纸质出版日期：2019-06-05
- 稿件说明：
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马永杰, 宋晓凤. 基于YOLO和嵌入式系统的车流量检测[J]. 液晶与显示, 2019,34(6):613-618.

Yong-jie MA, Xiao-feng SONG. Vehicle flow detection based on YOLO and embedded system[J]. Chinese journal of liquid crystals and displays, 2019, 34(6): 613-618.
马永杰, 宋晓凤. 基于YOLO和嵌入式系统的车流量检测[J]. 液晶与显示, 2019,34(6):613-618. DOI： 10.3788/YJYXS20193406.0613.

Yong-jie MA, Xiao-feng SONG. Vehicle flow detection based on YOLO and embedded system[J]. Chinese journal of liquid crystals and displays, 2019, 34(6): 613-618. DOI： 10.3788/YJYXS20193406.0613.

摘要

城市道路普遍存在机动车、非机动车、行人的人车混行路段，车辆目标的准确识别与统计成为视频方法检测混合交通流量的关键问题。本文提出了基于深度学习YOLO（You Only Look Once）的车流量检测算法。用YOLO v2检测道路上移动的目标，对检出目标中的车辆目标进行识别与筛选，设置感兴趣区域，在车辆目标经过感兴趣区域时计数，并用核相关滤波器跟踪车辆，避免车辆重复计数；在ARM上利用该算法实现了混合交通视频中的车流量检测。测试结果表明，该方法中车辆的检测、跟踪、计数结果良好，可应用于混合交通中的车流量检测。

Abstract

For the mixed traffic of motor vehicles

non-motor vehicles and pedestrians in urban roads

the accurate identification and statistics of vehicle targets become the key issues in video detection of mixed traffic flow. A traffic flow detection algorithm based on deep learning algorithm YOLO (You Only Look Once) was proposed in this paper. YOLO v2 was used to detect moving targets on the road

the region of interest was set up

the vehicle targets passed through the region of interest were counted

and the vehicles were tracked with the kernel correlation filter to avoid repeated counting. The traffic flow detection in mixed traffic video was realized by using this algorithm on ARM. The test results show that the results of vehicle detection

tracking and counting by this method are good and can be applied to the detection of vehicle flow in mixed traffic.

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references

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