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http://dx.doi.org/10.6109/jkiice.2022.26.11.1630

Damaged Traffic Sign Recognition using Hopfield Networks and Fuzzy Max-Min Neural Network  

Kim, Kwang Baek (Department of Artificial Intelligence, Silla University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.11, 2022 , pp. 1630-1636 More about this Journal
Abstract
The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.
Keywords
Traffic sign; Hopfield network; Fuzzy Max-Min neural network; Training pattern;
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