Roadside traffic signs are crucial to our safety while we are travelling. Similar to human drivers, classification of traffic signs is a crucial component in self-driving car applications. Self-driving cars must use camera data to make judgments based on what they observe. Self-driving cars must be able to recognize things and classify traffic signs in order for us to know when they cease yielding and if they will go at a speed of 30 or 50 kilometers per hour. To categorize traffic signs, utilize the traffic sign classification system. so that they can be informed and warned in advance to prevent rule violations. The suggested approach substantially addresses certain drawbacks of the current classification systems, such as inaccurate predictions, hardware cost, and maintenance. The suggested method uses LE-NET architecture to provide a categorization of traffic signs. This will enable autonomous vehicles to decide depending on what they perceive.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Sign Classification using Le-Net Architecture


    Contributors:


    Publication date :

    2023-01-27


    Size :

    466238 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic Sign Classification using Deep Learning

    Pothineni, Ramya Sree / Inampudi, Srinivas / Gudavalli, Lakshmi Yesaswini et al. | IEEE | 2023


    Traffic Sign Recognition Application Using Yolov5 Architecture

    Snegireva, Daria / Perkova, Anastasiia | IEEE | 2021


    Traffic sign detection model transmitted according to traffic sign classification information

    LU NAN / SHEN QIDONG / CAI XUFENG et al. | European Patent Office | 2024

    Free access

    Traffic Sign Detection and Classification

    Sharma, Vinayak / Kumar, Vimal / Aditya, Harshvardhan | IEEE | 2023


    Traffic Sign Classification for Road Safety using CNN

    P, Haree Krishna P / Ravindran, Sindhu / Vijean, Vikneswaran | IEEE | 2024