Traffic congestion becomes high in urban areas and using public and private transportation services. The image of traffic signs gets affected by fog, and the detection of traffic signs has become difficult. To solve this issue, the machine learning technique has been used. Convolution neural network helps to solve real-time problems; hence, it can be used in the study for detecting traffic signs under foggy condition. The study results revealed that the model network has accuracy of 99.8%, and the proposed algorithm detects a traffic sign under foggy conditions in 2 s per frame.


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    Titel :

    Real-Time Traffic Sign Detection Under Foggy Condition


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Aurelia, Sagaya (Herausgeber:in) / Hiremath, Somashekhar S. (Herausgeber:in) / Subramanian, Karthikeyan (Herausgeber:in) / Biswas, Saroj Kr. (Herausgeber:in) / Anthony, Renit (Autor:in) / Biswas, Jayanta (Autor:in)


    Erscheinungsdatum :

    2022-03-31


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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