Autonomous vehicles are one of the most discussed technologies right now since they have the potential to completely transform the mobility industry. There is a lot of research being done to make it more efficient. Autonomous vehicle models have to overcome obstacles such as lane detection, traffic light recognition, traffic sign detection, navigation, etc. The performance of autonomous cars depends on a variety of factors, and detection of traffic signs is one of them, as the safety of driving is heavily reliant on the identification of traffic signals. Traffic signs provide critical information about vehicle safety, such as traffic conditions, warning messages, road lights, and driving routes. This problem can be solved and safe driving can be ensured with a solid AI model for traffic sign detection and recognition. There are multiple ways through which this issue can be solved using various methods such as machine learning and deep learning. The main aim of the paper is to compare the different techniques such as the Local Binary Pattern (LBP), Convolutional Neural Network (CNN), and Transfer Learning (TL).


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    Title :

    Traffic Sign Detection using Transfer learning and a Comparison Between Different Techniques


    Contributors:


    Publication date :

    2022-08-26


    Size :

    1110766 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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