Traffic sign recognition and detection are critical in expert systems for effectively recognizing traffic signs along the road, such as left hair pin bend, parking lot, minimum speed, no waiting speed. Today's India needs traffic recognition to alert people on various signs of traffic, prevent accidents, and protect drivers along the roadway. This case of the traffic sign recognition scenario is proceeding using various techniques like deep learning, machine learning, artificial intelligence, etc. This study discusses how deep learning techniques are used to predict traffic signs. The various algorithms used for implementation are CNN and Keras. The performance of implemented algorithms calculated on accuracy as well as the ability to identify and classify traffic signs used along the roadway in real time.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Signs Recognition using CNN and Keras


    Contributors:
    Nagesh, Puvvada (author) / Akhil, L. (author) / Rishi, K. (author) / Bhargav, T. Sai (author)


    Publication date :

    2023-03-14


    Size :

    662198 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic Signs Recognition

    Prakash, Kolla Bhanu | Wiley | 2022


    Traffic Signs Recognition using R-CNN

    Varshini, E. Amrutha / Likitha, J. / Aswini, N. et al. | IEEE | 2022


    Traffic Signs, Visibility and Recognition

    Sprenger, A. / Schneider, W. / Derkum, H. et al. | British Library Conference Proceedings | 1999



    Traffic signs recognition with deep learning

    Yasmina, Djebbara / Karima, Rebai / Ouahiba, Azouaoui | IEEE | 2018