基于卷积神经网络的车辆分类与检测技术研究  

Research on Vehicle Classification and Detection Technology based on Convolutional Neural Network

作　　者：解丹 陈立潮 曹玲玲 张艳丽 XIE Dan;CHEN Lichao;CAO Lingling;ZHANG Yanli(Department of Information Technology Application Innovation and Big Data,Shanxi Jinzhong Institute of Technology,Jinzhong 030600,China;Department of Big Data,Jinzhong College of Information,Jinzhong 030600,China)

机构地区：[1]山西晋中理工学院信创与大数据学院,山西晋中030600 [2]晋中信息学院大数据学院,山西晋中030600

出　　处：《软件工程》2023年第4期10-13,共4页Software Engineering

摘　　要：车辆分类与检测在智能交通系统、道路交通规划、安全预警、无人驾驶等领域发挥着越来越重要的作用。随着图形处理器(Graphics Processing Unit,GPU)运算能力的增强以及数据量的剧增,以卷积神经网络为主的深度学习成为研究热点。基于LeNet、AlexNet、VGG、GoogLet、ResNet等卷积神经网络,介绍并分析了6种车辆分类方法、8种车辆检测方法及4个用于评估这些方法的数据集,并对车辆分类及检测的研究方向进行了展望。Vehicle classification and detection plays an increasingly important role in intelligent transportation systems,road traffic planning,safety warning,unmanned driving and so on.Deep learning based on convolutional neural networks has become a research focus with the enhancement of GPU computing power and the increase in data volume.Based on convolutional neural networks such as LeNet,AlexNet,VGG,GoogLet,ResNet,this paper introduces and analyzes 6 vehicle classification methods,8 vehicle detection methods and 4 datasets used to evaluate these methods.In addition,the research direction of vehicle classification and detection is prospected in this paper.

关 键 词：卷积神经网络 车辆分类 车辆检测 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]