The demand for employing Artificial Intelligent and data driven decision making in recognition of traffic signs in autonomous vehicles, self-driving cars, and not only in self-driving cars sometimes the driver who is driving also cannot recognize and follow the traffic signs is expediting. It is essential for autonomous vehicles to understand and follow all the traffic rules. The dynamic environment on which autonomous vehicles operate is risky due to insufficient training data. So by using this traffic signs recognizer, the driver will receive the information about the traffic sign coming ahead which can potentially lead to the minimization of the road accidents. Hence, to improve the accuracy in deep learning technology is employed. The primary reason that the method of deep learning is widely accepted in that the model can autonomously learn the deep characteristics of the image from the samples. In the current work, convolution neural network is employed to create a deep learning architecture that can identify traffic signs with close to 98% accuracy on the test set.


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    Titel :

    Classification and Recognition of Traffic Signs Using Deep Learning


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Bhateja, Vikrant (Herausgeber:in) / Khin Wee, Lai (Herausgeber:in) / Lin, Jerry Chun-Wei (Herausgeber:in) / Satapathy, Suresh Chandra (Herausgeber:in) / Rajesh, T. M. (Herausgeber:in) / Sirisha, D. (Autor:in) / Srilatha, Y. (Autor:in) / Sowjanya, N. V. S. (Autor:in)


    Erscheinungsdatum :

    06.07.2022


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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