Open AccessTraffic sign recognition based on weighted ELM and AdaBoost
Author(s) -
Xu Yan,
Wang Quanwei,
Wei Zhenyu,
Ma Shuo
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.2299
Subject(s) - adaboost , extreme learning machine , artificial intelligence , pattern recognition (psychology) , classifier (uml) , computer science , benchmark (surveying) , ensemble learning , traffic sign recognition , multiclass classification , support vector machine , machine learning , artificial neural network , sign (mathematics) , mathematics , traffic sign , mathematical analysis , geodesy , geography
A novel multiclass AdaBoost‐based extreme learning machine (ELM) ensemble algorithm is proposed, in which the weighted ELM is selected as the basic weak classifier because of its much faster learning speed and much better generalisation performance than traditional support vector machines. AdaBoost acts as an ensemble learning method of a number of weighted ELMs. Then, an ensemble strong classifier is constructed by the weighted majority vote of all the weighted ELMs. Compared with the existing ELM methods, the proposed algorithm solves the problem of how to train the weighted samples by ELM in multiclass classification directly. Experiments on the German Traffic Sign Recognition Benchmark database demonstrate that the proposed algorithm can achieve a high recognition accuracy of 99.12% with a relatively lower computational complexity than many state‐of‐the‐art algorithms.