Traffic Sign Recognition Method Based on Multi-layer Feature CNN and Extreme Learning Machine

被引：0

作者：

Sun W. ^{[1
,2
]}

Du H.-J. ^{[1
]}

Zhang X.-R. ^{[2
,3
]}

Zhao Y.-Z. ^{[1
]}

Yang C.-F. ^{[1
]}

机构：

[1] School of Information and Control, Nanjing University of Information Science & Technology, Nanjing

[2] Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing

[3] School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2018年 / 47卷 / 03期

关键词：

Extreme learning machine; Multi-layer features; Multi-scale pooling; Traffic sign recognition;

D O I：

10.3969/j.issn.1001-0548.2018.03.004

中图分类号：

学科分类号：

摘要：

The traditional neural network only uses the end-layer feature and needs massive and time-consuming computation in the traffic sign recognition, thereby resulting in an inaccurate and non-real-time classification. To solve the problem, a traffic sign recognition (TSR) method based on multi-layer feature expression and extreme learning machine (ELM) is proposed. Firstly, the multi-layer features of traffic signs are extracted using the convolutional neural network (CNN). Then, the multi-scale pooling operation is used to combine the extracted feature vectors of each layer to form a multi-scale multi-attribute traffic sign feature vector. Finally, the extreme learning machine (ELM) classifier is used to realize the classification of traffic signs. Experimental results show that the proposed method can effectively improve the accuracy and it has strong generalization ability and real-time performance in TSR. © 2018, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.

引用

页码：343 / 349

页数：6

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