| 基于深度学习网络模型的车辆类型识别方法研究 |
| |
| 引用本文： | 石鑫,赵池航,林盛梅,李彦伟,薛善光,钱子晨.基于深度学习网络模型的车辆类型识别方法研究[J].筑路机械与施工机械化,2020(4):67-73,78. |
| |
| 作者姓名： | 石鑫 赵池航 林盛梅 李彦伟 薛善光 钱子晨 |
| |
| 作者单位： | 河北交通职业技术学院土木工程系;东南大学交通学院;河北省交通规划设计院公路建设与养护技术、材料及装备交通运输行业研发中心 |
| |
| 基金项目： | 河北省重点研发计划项目(19270802D)。 |
| |
| 摘 要： | 为了将有效地识别车辆类型用于智慧交通系统,本文在分析Inception V3模型的基础上,提出了一种基于迁移学习理论的车型分类深度学习模型。该模型首先在Inception V3模型的基础上去除最后的全连接层,并加入参数优化层,然后采用Dropout和全局平均池化层。理论分析和试验结果表明,该模型的性能优于基于VGG-16的车型分类模型、基于Xception的车型分类模型和基于Resnet50的车型分类模型,其训练精度优于96.48%、测试精度优于83.86%。
|
| 关 键 词： | 智慧交通 车辆识别 深度学习模型 迁移学习 |
Research on Vehicle Type Recognition Method Based on Deep Learning Network Model |
| |
| SHI Xin,ZHAO Chi-hang,LIN Sheng-mei,LI Yan-wei,XUE Shan-guang,QIAN Zi-chen.Research on Vehicle Type Recognition Method Based on Deep Learning Network Model[J].Road Machinery & Construction Mechanization,2020(4):67-73,78. |
| |
| Authors: | SHI Xin ZHAO Chi-hang LIN Sheng-mei LI Yan-wei XUE Shan-guang QIAN Zi-chen |
| |
| Institution: | (Department of Civil Engineering,Hebei Jiaotong Vocational and Technical College,Shijiazhuang 050011,Hebei,China;School of Transportation,Southeast University,Nanjing 211189,Jiangsu,China;Research and Development Center of Transport Industry for Technologies,Materials and Equipment of Highway Construction and Maintenance,Hebei Provincial Communications Planning and Design Institute,Shijiazhuang 050011,Hebei,China) |
| |
| Abstract: | In order to incorporate the recognition of vehicle type into the intelligent transportation system,a deep learning model for vehicle classification based on transfer learning theory was proposed in terms of the analysis of the Inception V3 model.The model removes the last fully connected layer based on the Inception V3 model,and adds a parameter optimization layer,and then uses Dropout and the global average pooling layer.Theoretical analysis and experimental results show that the performance of the model is better than the vehicle classification models based on VGG-16,Xception and Resnet50,with a training accuracy above 96.48%and a testing accuracy above 83.86%. |
| |
| Keywords: | intelligent transportation vehicle recognition deep learning model transfer learning |
| 本文献已被 CNKI 维普 等数据库收录！ |
|
