Vehicle Type Recognition based on Multi-branch and Multi-Layer Features

摘要：

　　To improve the accuracy and reduce the computational complexity of neural networks for vehicle type recognition,this paper proposes a novel method based on Multi-branch and Multi-layer features.First of all,each car-face image is divided into multiple sub-images according to texture featurescharacteristic.Secondly,global and local features are extracted using several convolutional neural networks(CNN)in different layers then connected to a fully-connected layer.Finally,Softmax classifier is used for vehicle type recognition.Experimental results show that valid global and local features in both top and bottom layers are extracted by the proposed method.Furthermore,convergent efficiency and accuracy of recognition are improved.

关键词： Vehicle type recognition convolution neural network global features local features joint features

作者: Chaocun Chen Xiaodong Cai Qinlu Zhao Lu Lv Hongxin Shu

作者单位: School of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,China China Comservice Public Information Industry Co.ltd

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 2038-2041

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）