Traffic signs are essential for controlling traffic upon the road, enforcing driver discipline, and averting accidents, property damage, and deaths. An integral component of any ITS (Intelligent Transportation System) is road sign management with automated detection and recognition. The sign could have a large variety of colors and sizes. Additionally, changes may result from the signs' translation, rotation, and scaling. Transportation officials can optimize traffic control by modifying ramp metering, adjusting signal timings, or providing congestion-aware route guidance based on these traffic flow predictions. The skip connections are created by using dilated convolutions with various filter sizes to broaden the feature extractors' receptive fields. It was suggested to make an early effort to create a hierarchical, spatially invariant model for image classification. A novel road extraction framework is proposed to address these issues by extracting complete and continuous road networks using a worldwide Context-aware and Batch-independent Network. The precision attained by the suggested model is superior to that of the conventional CNN. Furthermore, the suggested model demonstrates resilient performance in various environmental circumstances, rendering it appropriate for practical implementations and advancing intelligent transportation systems development, ultimately advocating for safer roadways.


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    Titel :

    Enhancing the Traffic Sign Detection to Improve Convolutional Neural Network


    Beteiligte:
    Sungheetha, Akey (Autor:in) / V, Vijeya kaveri (Autor:in) / V, Sruthi Sri (Autor:in) / S, Srineveda R (Autor:in) / K, Sasikal Rani (Autor:in) / R, Rajesh Sharma (Autor:in) / Antony, Judeson (Autor:in)


    Erscheinungsdatum :

    24.07.2024


    Format / Umfang :

    337424 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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