Optimized Method for Iranian Road Signs Detection and recognition system

TL;DR

This paper presents a real-time, robust method for detecting and recognizing Iranian road speed signs using edge detection, morphological operations, and SVM classifiers, achieving high accuracy and efficiency.

Contribution

The study introduces a novel detection and recognition approach combining geometric and color features with SVMs, optimized for Iranian road signs.

Findings

Detection accuracy of 98.66%

Recognition accuracy of 100%

Method is robust to noise and distance variations

Abstract

Road sign recognition is one of the core technologies in Intelligent Transport Systems. In the current study, a robust and real-time method is presented to identify and detect the roads speed signs in road image in different situations. In our proposed method, first, the connected components are created in the main image using the edge detection and mathematical morphology and the location of the road signs extracted by the geometric and color data; then the letters are segmented and recognized by Multiclass Support Vector Machine (SVMs) classifiers. Regarding that the geometric and color features ate properly used in detection the location of the road signs, so it is not sensitive to the distance and noise and has higher speed and efficiency. In the result part, the proposed approach is applied on Iranian road speed sign database and the detection and recognition accuracy rate achieved 98.66% and 100% respectively.