吉林大学学报(理学版) ›› 2021, Vol. 59 ›› Issue (2): 319-324.

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基于深度残差网络的车标识别

田强, 贾小宁   

  1. 长春理工大学 理学院, 长春 130022
  • 收稿日期:2020-01-02 出版日期:2021-03-26 发布日期:2021-03-26
  • 通讯作者: 贾小宁 E-mail:jiaxn11@mails.jlu.edu.cn

Vehicle Logo Recognition Based on Deep Residual Network

TIAN Qiang, JIA Xiaoning   

  1. School of Science, Changchun University of Science and Technology, Changchun 130022, China
  • Received:2020-01-02 Online:2021-03-26 Published:2021-03-26

摘要: 针对车标识别准确率的问题, 提出一种基于ResNet-18模型改进残差网络的车标识别算法. 首先, 利用残差网络并对其进行改进, 使用改进的线性修正单元Leaky ReLU激活函数代替原激活函数; 其次, 调整传统的残差网络结构, 将批量标准化和激活函数放在卷积层前, 并减少网络参数以加速网络训练. 实验结果表明, 改进后的残差网络模型识别准确率达99.8%.

关键词: 深度学习, 残差网络, 图像识别, 车标识别

Abstract: Aiming at the problem of accuracy of vehicle logo recognition, we proposed a vehicle logo recognition algorithm based on improved residual network of ResNet-18 model. Firstly, the residual network was used and improved, and the improved linear correction unit Leaky ReLU activation function was used to replace the original activation function. Secondly, we adjusted the traditional residual network structure, put the batch standardization and activation function before the convolution layer, and reduced the network parameters to speed up the network training. The experimental results show that the recognition accuracy of the improved residual network model is 99.8%.

Key words: deep learning, residual network, image recognition, vehicle logo recognition

中图分类号: 

  • TP391.4