基于LeNet-5的卷积神经图像识别算法

张万征; 胡志坤; 李小龙

doi:10.3788/YJYXS20203505.0486

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基于LeNet-5的卷积神经图像识别算法

图像处理 | 更新时间：2020-08-12

- 基于LeNet-5的卷积神经图像识别算法
- Convolutional neural image recognition algorithm based on LeNet-5
- 液晶与显示 2020年35卷第5期页码：486-490
- 作者机构：
  
  智洋创新科技股份有限公司, 山东淄博 255086
- 作者简介：
  
  [ "张万征(1978-), 男, 山东淄博人, 学士, 高级工程师, 2001年于山东工程学院(现山东理工大学)获得学士学位, 主要从事电力物联网、电力设备在线监测、隐患人工智能识别和智能运维等方面的研究。E-mail:ZHwanzheng10@163.com" ]
- 基金信息：
- DOI：10.3788/YJYXS20203505.0486
  中图分类号： TP391.41
- 收稿日期：2019-10-25，
  
  修回日期：2020-01-06，
  
  录用日期：2020-1-6，
  
  纸质出版日期：2020-05-05
- 稿件说明：
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张万征, 胡志坤, 李小龙. 基于LeNet-5的卷积神经图像识别算法[J]. 液晶与显示, 2020,35(5):486-490.

Wan-zheng ZHANG, Zhi-kun HU, Xiao-long LI. Convolutional neural image recognition algorithm based on LeNet-5[J]. Chinese journal of liquid crystals and displays, 2020, 35(5): 486-490.
张万征, 胡志坤, 李小龙. 基于LeNet-5的卷积神经图像识别算法[J]. 液晶与显示, 2020,35(5):486-490. DOI： 10.3788/YJYXS20203505.0486.

Wan-zheng ZHANG, Zhi-kun HU, Xiao-long LI. Convolutional neural image recognition algorithm based on LeNet-5[J]. Chinese journal of liquid crystals and displays, 2020, 35(5): 486-490. DOI： 10.3788/YJYXS20203505.0486.

摘要

为了提升道路交通标志的识别准确率以及实施性能，本文提出一种改进的LeNet-5卷积神经网络结构对交通标志图像进行训练。首先在检测阶段，采用基于颜色的轻量级分割算法和Hough变换算法提取交通标志的目标区域，并控制算法复杂度使该识别系统基本满足实时性要求，再利用LeNet-5对交通标志进行分类识别。在实际的校园道路在线识别试验中进行检测，结果表明，18个交通标志通过驾驶均在本文的算法中成功识别，其运行速率达到16.9 Hz，基本满足交通标志识别稳定、实时等性能要求。

Abstract

In order to improve the recognition accuracy and implementation performance of road traffic signs

an improved LeNet-5 convolutional neural network structure is proposed to train 18 kinds of traffic sign images. Firstly

in the detection phase

the light-weight segmentation algorithm based on color and Hough transform algorithm is used to extract the target area of traffic signs

and the complexity of the control algorithm makes the recognition system basically meet the real-time requirements

and then LeNet-5 is used to classify and recognize traffic signs. The results show that the traffic signs are successfully identified by driving in the algorithm of this paper

and the running speed reaches 16.9 Hz

which basically meets the performance requirements of traffic sign recognition

such as stability

real-time and so on.

关键词

Keywords

references

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