Open AccessTraffic Sign Recognition with a small convolutional neural network
Author(s) -
Wenhui Li,
Daihui Li,
Shangyou Zeng
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/688/4/044034
Subject(s) - traffic sign recognition , convolutional neural network , traffic sign , computer science , benchmark (surveying) , artificial intelligence , support vector machine , pattern recognition (psychology) , feature extraction , identification (biology) , feature (linguistics) , matching (statistics) , sign (mathematics) , mathematics , mathematical analysis , linguistics , philosophy , botany , statistics , geodesy , biology , geography
Traffic sign recognition is an important part of the intelligent transportation system and has important application prospects in driverless vehicles and driver assistance systems[1]. In the image recognition of traffic signs, according to the image features of traffic signs, the common methods include traditional template matching method[2], SVM method[3], random forest[4] and the best Convolutional Neural Networks (CNN) method. In this paper, a new CNN is proposed. Feature extraction, compared with the traditional CNN method, has higher accuracy, fewer parameters, smaller models and easier training, which is evaluated on the German Traffic Sign Recognition Benchmark (GTSRB) and the Belgium Traffic Sign Dataset (BTSD). The results show that this method is superior to the traditional CNN method in traffic identification.