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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.
关键词：车辆车型;电磁感应;自适应共振神经网络;主成分
中图分类号：TP274 文献标志码：A
Research on Online Classification of Road Vehicle
Types Based on Electromagnetic Induction
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）
Abstract：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.
Key words：vehicle type;electromagnetic induction; adaptive resonance neural network; principal components
智能交通控制系統通过车辆检测器采集车流量、车速等交通参数，道路车辆的车型统计也是其中重要的交通信息[1].不同车型车辆道路通行能力的当量系数相差数倍[2]，只有实时检测道路上的车辆车型分布状况才能真实反映道路当前的交通状况和通行能力. 近年来，道路的车辆车型识别较多地采用基于摄像和图像识别的方法，但视频等检测技术容易受到天气、昼夜、光线变化等因素影响. 照明和天气变化严重挑战车型识别性能[3-4]. 环形线圈车辆检测器具有不论昼夜全天候工作的优势，且因其低成本、高可靠性和精度[5-6]，广泛应用于道路交通监测. 但在用于车型识别方面也存在较多的问题[7]：一方面车在行进过程中会有许多难以预料的干扰因素存在，如加速、减速或停车都会使感应曲线发生畸变而导致识别错误，若避免在拥堵路段安装使用可大大减少畸变几率;另一方面我国的车辆种类繁多，新车层出不穷，车辆的感应特性并不以车型来显著区别，各种改装车更是车型难辨，而不同用途的车型划分方法也各不相同，诸多因素使得分类器难以保证长期或广泛应用下稳定的识别正确率. 因此，多年来环形线圈的车型识别功能并没有如其车辆检测功能一样得到广泛的应用. 文献[8]指出各种车辆检测器应该在鲁棒性、实时性、精度全面提高才能取得突破.