Traffic Sign Detection and Recognition Using Adaptive Threshold Segmentation with Fuzzy Neural Network Classification

Abstract

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. In this paper, a new traffic sign detection and recognition approach is presented by using Fuzzy Neural Network (FNN) and it is including three stages. The first stage segments the images to extract ROIs. The segmentation is usually performed based on Adaptive thresholding to overcome the color segmentation problems. The second one detects traffic shapes. Given that the geometric form of traffic signs is limited to triangular, circular, rectangular and octagonal forms, the geometric information is used to identify traffic shapes from ROIs provided by the first stage. The third stage recognizes the traffic signs based on the information including included in their pictograms. Moreover, in this work, six types of features are extracted. These features were provided to the FNN classifier to perform the recognition. As a classifier, FNN, Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark dataset. The results obtained are satisfactory when compared to the state-of-the-art methods.