基于电磁感应的道路车辆车型在线分类方法研究  

Research on Online Classification of Road Vehicle Types Based on Electromagnetic Induction

作　　者：叶青[1] 刘剑雄 刘铮[1] 陈众[1] 李靓 YE Qing;LIU Jianxiong;LIU Zheng;CHEN Zhong;LI Liang(College of Electrical&Information Engineering,Information Processing and Robotics Research Institute,Changsha University of Science&Technology,Changsha 410114,China)

机构地区：[1]长沙理工大学电气与信息工程学院信息处理与机器人研究所

出　　处：《湖南大学学报（自然科学版）》2019年第12期41-49,共9页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(61971071);国家重点研发计划项目(2018YFB1308200);湖南省教育厅科研基金重点资助项目(17C0046,12A006);湖南省重点研发计划项目(2018GK2022)~~

摘　　要：广泛应用于道路车辆检测的环形线圈车辆检测器对于车辆车型的实时分类正确率较低,主要原因是面对各种车辆电磁感应特性的复杂多变和未知车型的新车辆层出不穷问题,其模式固定的分类模型难以胜任.基于通过环形线圈时车辆电磁感应特性波形提出一种新的车辆车型实时判别方法:运用主分量分析法提取特征,采用自适应共振神经网络聚类建立车辆类别模式,动态划分各车型包含的类别模式;以半监督学习方式在线增加未知车型的新车辆模式,算法自适应新车辆的车型识别.7种车型的道路现场实时车型识别实验平均正确率为91.3%,加入新模式自动识别后提高至92.5%;Alexnet多层卷积神经网络算法的对比实验中,训练集和测试集正确率分别为99.5%和87.1%,相差较大.实验结果验证了本文方法在道路车辆模式不断变化情况下实现车型识别的可行性.The vehicle classification correct rate of loop induction detector widely used on road is not high.The main reason is the classifier of fixed classification rules cannot cope with the changes of vehicle's complicated models and new vehicle's type classing.Based on the electromagnetic induction characteristic waveform of the vehicle passing through the loop,a new real-time discriminant method for vehicle classification was proposed.The principal component analysis method was used to extract the features.The adaptive resonance neural network algorithm was applied to cluster classification modes,these were dynamically divided into vehicle types then.For new vehicles of unknown vehicle type,new classification modes were added online by semi-supervised learning to adapt to the recognition of new vehicle type.The average correct rate of road real-time vehicle identification experiments of 7 models was 91.3%,and it was increased to 92.5%after adding new mode automatic recognition.In the comparative experiment with Alexnet multi-layer convolutional neural network algorithm,the correct rate of training set and test set were 99.5%and 87.1%respectively,which signified the existence of big differences.The experimental results verified the feasibility of the proposed method to solve the road vehicle identification problem of the change of vehicle mode.

关 键 词：车辆车型 电磁感应 自适应共振神经网络 主成分 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]