The paper addresses a crucial aspect of modern transportation technology by focusing on enhancing the robustness of traffic sign recognition systems. In the context of intelligent transportation systems, accurate traffic sign recognition plays a vital role in ensuring road safety and optimizing traffic management. This study introduces an innovative approach that combines color information with edge magnitude patterns, demonstrating its effectiveness in achieving robust traffic sign recognition. The proposed methodology includes data collection from diverse sources, grouping traffic signs into categories, preprocessing steps for standardization, and the extraction of 22 features using Gray Level Co-occurrence Matrix (GLCM) analysis. These features are then organized into a database and utilized for training a feedforward neural network with two layers for precise traffic sign detection. The project culminates in an overall accuracy rate of 90.2%, underscoring its potential to significantly enhance road safety and traffic regulation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Identification of Indian Traffic Signs Using Artifical Neural Network


    Contributors:


    Publication date :

    2023-11-24


    Size :

    936838 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Artifical intelligence applications to traffic engineering

    Bielli, Maurizio ;Ambrosino, Giorgio ;Boero, Marco | TIBKAT | 1994


    Artifical intelligence applications to traffic engineering

    Bielli, Maurizio ;Ambrosino, Giorgio ;Boero, Marco | SLUB | 1994


    Plant Growth Model Using Artifical Neural Networks

    Bubenheim, David / Zee, Frank | SAE Technical Papers | 1997


    Robust Traffic Signs Classification using Deep Convolutional Neural Network

    Kherraki, Amine / Maqbool, Muaz / Ouazzani, Rajae El | IEEE | 2022