基于可能性聚类和卷积神经网络的道路交通标识识别算法

doi:10.13232/j.cnki.jnju.2019.02.009

南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (2): 238–250.doi: 10.13232/j.cnki.jnju.2019.02.009

基于可能性聚类和卷积神经网络的道路交通标识识别算法

狄　岚¹*，何锐波¹，梁久祯²

1.江南大学数字媒体学院，无锡，214122；2.常州大学信息科学与工程学院，常州，213164

接受日期:2019-01-19 出版日期:2019-04-01 发布日期:2019-03-31
通讯作者: 狄　岚 E-mail:dilan@jiangnan.edu.cn
基金资助:
江苏省研究生科研与实践创新计划(KYCX18_1872)

Road traffic identification based on probability clustering and convolutional neural network

Di Lan¹*，He Ruibo¹，Liang Jiuzhen²

1.School of Digital Media，Jiangnan University，Wuxi，214122，China； 2．School of Information Science and Engineering，Changzhou University，Changzhou，213164，China

Accepted:2019-01-19 Online:2019-04-01 Published:2019-03-31
Contact: Di Lan E-mail:dilan@jiangnan.edu.cn

摘要/Abstract

摘要： 为解决图像采集中噪声和复杂背景对图片的影响以及深度神经网络的高耗时问题，基于可能性聚类算法与卷积神经网络，提出一种道路交通标识识别算法. 该方法运用了图像分割技术，并结合卷积神经网络模型对道路交通标识进行更准确的识别. 首先，通过色彩增强、图像分割、特征提取、数据增强和归一化等批量预处理操作，形成一个完整的数据集；然后，结合Squeeze－and－Excitation思想和残差网络结构，充分训练出MRESE(My Residual－Squeeze and Excitation)卷积神经网络模型；最后，将优化的网络模型用于道路交通标志的识别. 实验结果表明，该方法使训练时间缩短了5%左右，识别精度可达99.02%.

关键词: 道路交通标识识别, 卷积神经网络, Squeeze－and－Excitation网络, 残差连接

Abstract: In order to solve the influence of noise and complex background on image acquisition and the problem of high time consuming when we use the deep learning neural network，this paper proposes a road traffic identification algorithm combining probability clustering and deep learning neural network. The proposed method is for more accurate identification of road traffic signs using not only image segmentation technology but also the convolution neural network model. Firstly，a complete data set consists by batch preprocessing operations such as color enhancement，image segmentation，feature extraction，data enhancement and normalization. Then，we sufficiently trained our MRESE(My Residual－Squeeze and Excitation)convolution neural network model which combined the ideas of squeeze－and－excitation and residual network structure. Finally，the optimized network model is used to identify the road traffic signs. The experimental results show that the proposed method reduces the training time by about 5%，and the recognition accuracy can be reached 99.02%.

Key words: road traffic identification, convolutional neural network, Squeeze－and－Excitation Networks, residual connection

中图分类号:

TP391.4

狄　岚, 何锐波, 梁久祯. 基于可能性聚类和卷积神经网络的道路交通标识识别算法[J]. 南京大学学报(自然科学版), 2019, 55(2): 238–250.

Di Lan, He Ruibo, Liang Jiuzhen. Road traffic identification based on probability clustering and convolutional neural network[J]. Journal of Nanjing University(Natural Sciences), 2019, 55(2): 238–250.

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基于可能性聚类和卷积神经网络的道路交通标识识别算法

Road traffic identification based on probability clustering and convolutional neural network

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