Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world, and one significant solution to these problems can be the proper implementation of the intelligent transport system (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents and reduce incidents or adverse weather. Road and traffic sign recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey toward building semiautonomous/autonomous driving systems. Traditionally, Mobileye had developed its first commercially deployed traffic sign recognition system with Continental AG for the BMW-7 series vehicles, but this technology has not been used much. The purpose of this work focusses on implementing an approach to solve the problem of traffic sign classification by developing a convolutional neural network (CNN) classifier using the GTSRB — German Traffic Sign Recognition Benchmark dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.


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    Title :

    Intelligent Transport System: Classification of Traffic Signs Using Deep Neural Networks in Real Time


    Additional title:

    Lect.Notes Mechanical Engineering



    Conference:

    International Conference on Advanced Production and Industrial Engineering ; 2019 ; Delhi, India December 21, 2019 - December 22, 2019



    Publication date :

    2021-01-14


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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