基于YOLO的道路车辆拥堵分析模型

doi:10.11772/j.issn.1001-9081.2018071656

计算机应用 ›› 2019, Vol. 39 ›› Issue (1): 93-97.DOI: 10.11772/j.issn.1001-9081.2018071656

• 2018年全国开放式分布与并行计算学术年会(DPCS 2018)论文 • 上一篇下一篇

基于YOLO的道路车辆拥堵分析模型

张家晨, 陈庆奎

上海理工大学光电信息与计算机工程学院, 上海 200093

收稿日期:2018-07-19 修回日期:2018-08-24 出版日期:2019-01-10 发布日期:2019-01-21
通讯作者: 陈庆奎
作者简介:张家晨(1993-),男,四川成都人,硕士研究生,主要研究方向:人工智能、GPU并行计算;陈庆奎(1966-),男,黑龙江哈尔滨人,教授,博士生导师,博士,CCF会员,主要研究方向:网络计算、物联网、并行计算、GPU集群。
基金资助:
国家自然科学基金资助项目（61572325，60970012）；高等学校博士学科点专项科研博导基金资助项目（20113120110008）；上海重点科技攻关项目（14511107902，16DZ1203603）；上海市工程中心建设项目（GCZX14014）；上海智能家居大规模物联共性技术工程中心项目（GCZX14014）；上海市一流学科建设项目（XTKX2012）；沪江基金研究基地专项（C14001）。

Road vehicle congestion analysis model based on YOLO

ZHANG Jiachen, CHEN Qingkui

College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2018-07-19 Revised:2018-08-24 Online:2019-01-10 Published:2019-01-21
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61572325, 60970012), the Research Fund for the Doctoral Program of Higher Education of China (20113120110008), the Key Science and Technology Research Project of Shanghai (14511107902, 16DZ1203603), the Construction Project of Shanghai Engineering Center (GCZX14014), the Project of Shanghai Smart Home Large-scale Common Technology Engineering Center (GCZX14014), the Shanghai Top-class Discipline Construction Project (XTKX2012), the Hujiang Foundation of China (C14001).

摘要/Abstract

摘要： 针对当前交通运行出现的拥堵问题，提出一种新型的道路状态判断模型。首先，模型基于YOLOv3目标检测算法，然后结合图片对应的特征值矩阵，通过相邻帧之间的特征矩阵作差并将差值逐项求和得到的结果与预设值进行比较来判断当前道路是处于拥堵状态还是正常通行状态，其次再将当前计算出的道路状态与前两次计算出的道路状态进行比较，最后运用模型里的状态统计法来统计道路某状态（拥堵或通畅）的持续时间。该模型能够同时对一条道路的三个车道进行状态统计分析，经过实验，模型对单条车道状态判断的平均准确率能达到80%以上，并且白天与夜晚的道路均适用。

关键词: 交通拥堵, 拥堵检测, 车道分析, 拥堵时间, 高峰时段

Abstract: To solve traffic congestion problems, a new road condition judgment model was proposed. Firstly, the model was based on YOLOv3 target detection algorithm. Then, according to the eigenvalue matrix corresponding to the picture, the difference between adjacent frames was made by the eigenvalue matrix, and the difference value was compared with preset value to determine whether the current road was in a congested state or a normal traffic state. Secondly, the current calculated road state was compared with previous two calculated road states. Finally, the state statistics method in the model was used to calculate the duration of a state (congestion or patency) of road. The proposed model could analyze the states of three lanes of a road at the same time. Through experiments, the average accuracy of model to judge the state of single lane could reach 80% or more, and it was applicable to both day and night roads.

Key words: traffic congestion, congestion detection, lane analysis, congestion time, rush hour

中图分类号:

TP393
TP18

张家晨, 陈庆奎. 基于YOLO的道路车辆拥堵分析模型[J]. 计算机应用, 2019, 39(1): 93-97.

ZHANG Jiachen, CHEN Qingkui. Road vehicle congestion analysis model based on YOLO[J]. Journal of Computer Applications, 2019, 39(1): 93-97.

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基于YOLO的道路车辆拥堵分析模型

Road vehicle congestion analysis model based on YOLO

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