基于OpenCV-Python的高速公路车辆识别与计数功能研究  

Research on Highway Vehicle Identification and Counting Function Based on OpenCV-Python

作　　者：朱景昊 曹立波[1,2] 陈凯 戴丽华 朱李平 陶强 Zhu Jinghao;Cao Libo;Chen Kai;Dai Lihua;Zhu Liping;Tao Qiang(Hunan University,Changsha 410082;Changsha Lizhong Automobile Design and Development Limited Company,Changsha 410205)

机构地区：[1]湖南大学,长沙410082 [2]长沙立中汽车设计开发股份有限公司,长沙410205

出　　处：《汽车工程师》2023年第6期14-19,共6页Automotive Engineer

摘　　要：为实现传统图像处理方法在智能交通系统中的广泛应用,针对高速公路监控视频中车辆的识别与计数方法进行研究,采用Python编程语言,基于OpenCV库,通过灰度化、去噪、背景减除和形态学运算等一系列传统图像处理方法完成了车辆的识别与计数功能设计,并将该方案与基于YOLOv3模型和简单在线和实时跟踪(SORT)算法相结合的方案进行对比。结果表明,传统图像处理方法可以完成运动目标检测任务,但相对于基于深度学习的方案存在准确度不高和通用性不强的问题,为此提出了将传统图像处理与深度学习相结合的研究建议。In order to realize wide application of traditional image processing methods in intelligent transportation systems,the paper studied the identification and counting methods of vehicles in highway surveillance video.Using Python programming language and based on the OpenCV library,the design of the identification and counting function of vehicles was completed through a series of traditional image processing methods such as grayscale,denoising,background subtraction and morphological operations.This solution was compared with the solution based on the combination of the YOLOv3 model and the Simple Online and Realtime Tracking(SORT)algorithm.The results show that traditional image processing methods can detect moving target,but there are problems of low accuracy and low versatility compared with the solution based on deep learning.The paper therefore proposed the research suggestion of combining traditional image processing and deep learning.

关 键 词：智能交通系统 图像处理 OPENCV 车辆识别 背景减除法 

分 类 号：U463.6[机械工程—车辆工程] TP391.41[交通运输工程—载运工具运用工程]