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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Identification of Indian Traffic Signs Using Artifical Neural Network


    Beteiligte:
    Virupakshaiah, H K (Autor:in) / Prashanth, G K (Autor:in) / Tanushree, C V (Autor:in)


    Erscheinungsdatum :

    24.11.2023


    Format / Umfang :

    936838 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    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