[1]WU Xiru,XUE Ganggang.CNN-based image clustering algorithm for fast recognition of traffic signs[J].CAAI Transactions on Intelligent Systems,2019,14(4):670-678.[doi:10.11992/tis.201806026]
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CNN-based image clustering algorithm for fast recognition of traffic signs

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 4 Page number: 670-678 Column: 学术论文—机器感知与模式识别 Public date: 2019-07-02
Title:
CNN-based image clustering algorithm for fast recognition of traffic signs
Author(s):
WU Xiru1; 2;  XUE Ganggang1; 2
1. College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China;
2. Guangxi Key Laboratory of Automatic Detection, Guilin University of Electronic Technology, Guilin 541004, China
Keywords:
traffic sign; pattern recognition; image preprocessing; image clustering; sample optimization; deep learning; convolutional neural network; intelligent vehicle
CLC:
TP391.4
DOI:
10.11992/tis.201806026
Abstract:
To improve the accuracy and real-time performance of traffic signs recognition, a convolutional neural network (CNN)-based image clustering algorithm is proposed for fast recognition of traffic signs. First, The image clustering algorithm was used to optimize the original dataset; second, the overall quality of the dataset was further improved through various image preprocessing operations; finally, a nine-layer CNN structure was constructed. The CNN model was eventually obtained after several sample trainings, and an image was inputted into the CNN model for automatic recognition. The validity of the algorithm was proved based on the German traffic sign recognition benchmark (GTSRB) and Belgium traffic sign dataset (BTSD). The recognition time of a single picture was only 0.2 s, and the recognition accuracy was higher than 98.5%. The results confirm fast recognition and high accuracy rate of the proposed algorithm. It provides theoretical basis and technical support for the reliability and security of intelligent vehicle driving.
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