摘要
车标分类是车辆识别的方法之一。车标分类广泛应用于政府大楼、校园、道路等场所的安全防护。本研究的主要目的是利用支持向量机(SVM)分类方法对车标进行识别。针对车标识别问题,设计了一种基于大间隔分布的Pin-SVM(LMD-Pin-SVM)模型。首先,利用间隔均值和方差定义间隔分布,然后将最优间隔分布引入到Pin-SVM模型中,建立LMD-Pin-SVM线性模型。此外,利用核技术将线性LMD-Pin-SVM模型扩展到非线性情况,并应用于车标识别中。实验结果显示,所提出的模型在泛化性能上优于其他算法。LMD-Pin-SVM模型可以有效地抑制噪声对分类模型的不利影响,提高不同车标图像的分类正确率。
Vehicle logo classification is one of the methods of vehicle recognition.Vehicle logo classification is widely used in the safety protection of government buildings,campus,roads and other places.Aiming at the vehicle logo classification,a Pin-SVM based on large margin distribution(LMD-Pin-SVM)is proposed.Firstly,the margin distribution is expressed with margin mean and margin variance.Then introducing the optimal margin distribution into Pin-SVM and the linear LMD-Pin-SVM is built.In addition,the linear LMD-Pin-SVM is extended to the nonlinear case with kernel trick.And the nonlinear LMD-Pin-SVM is applied to vehicle recognition.All experiments show that the proposed method is superior to the state-of-the-art methods in generalization performance.Our LMD-Pin-SVM can effectively suppress the adverse effects of noise on the classification model and improve the classification accuracy of different vehicle logo images.
作者
赵玉田
Zhao Yutian(Yantai Automobile Engineering Professional College,Yantai 264000,China)
出处
《电子测量技术》
北大核心
2021年第7期55-60,共6页
Electronic Measurement Technology
关键词
车标
分类
间隔分布
Pin-SVM
vehicle logo
classification
margin distribution
Pin-SVM
