计算机技术与发展2024，Vol.34Issue(9)：209-214,6.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0185
基于YOLO模型的车流量实时采集系统研究
Research on Real-time Traffic Flow Collection System Based on YOLO Model
摘要
Abstract
For a modern city,reasonable traffic planning is the key to efficient operation of a city.As the key information of traffic planning,urban vehicle flow information originally needs manual identification,acquisition and verification of extraction methods,with the vigorous development of computer vision technology,will eventually withdraw from the stage of history.In order to improve the accuracy and timeliness of urban vehicle flow information,a real-time vehicle flow acquisition system based on YOLO model is designed by using the existing computer technology.Based on the YOLO visual detection model,the system uses DeepSORT algorithm to track and identify the detected target vehicles,judge the running status of vehicles,realize the traffic flow statistics of the current road section,visually display the recorded traffic flow information and output data.The system can effectively replace the traditional labor-consuming rigid work,and realize automatic data collection and rapid monitoring of road traffic conditions.The system is simple and interactive,and provides accurate and real-time information data for urban traffic management and traffic planning.关键词
目标检测/目标跟踪算法/数据处理/YOLO模型/车流量/实时采集Key words
target detection/target tracking algorithm/data processing/YOLO model/traffic flow/real-time collection分类
信息技术与安全科学引用本文复制引用
王金环,李宝敏..基于YOLO模型的车流量实时采集系统研究[J].计算机技术与发展,2024,34(9):209-214,6.基金项目
陕西省自然科学基金项目(2018JM703702) （2018JM703702）
陕西省"十四五"教育科学规划课题(SGH22Y1824) （SGH22Y1824）
