Author:
Li Wenhui,Li Daihui,Zeng Shangyou
Abstract
Abstract
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.
Reference19 articles.
1. A Road Traffic Signal Recognition System Based on Template Matching Employing Tree Classifier;Varan,2007
2. Traffic Sign Recognition with Color-based Method, Shape-arc Estimation and SVM;Soendoro,2011
3. A comparative study of classification methods for traffic signs recognition;Wahyono,2014
Cited by
19 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献