Abstract Object detection using deep learning over the years became one of the most popular methods for implementation in autonomous systems. Autonomous vehicle requires very reliable and accurate identification and recognition of surrounding objects in real traffic environments to achieve decent detection results. In this paper, special type of Artificial Neural Network (ANN) named Convolutional Neural Network (CNN) was used for identification and recognition of surrounding objects in real traffic. The new model based on CNN was trained and developed to be able to identify and recognize 4 different classes of objects: cars, traffic lights, persons and bicycles. The developed model has shown 94.6% accuracy of object identification and recognizing on the test set.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Identification and Recognition of Vehicle Environment Using Artificial Neural Networks


    Contributors:


    Publication date :

    2018-09-04


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Artificial neural networks based vehicle license plate recognition

    Kocer, H. Erdinc / Cevik, K. Kursat | BASE | 2011

    Free access

    Artificial neural networks based vehicle license plate recognition

    Kocer H.E. / Cevik K.K. | BASE | 2011

    Free access

    Artificial neural networks based vehicle license plate recognition

    Kocer, H. Erdinc / Cevik, K. Kursat | BASE | 2011

    Free access

    Aircraft System Identification Using Artificial Neural Networks

    Kirkpatrick, K. / May, J. / Valasek, J. et al. | British Library Conference Proceedings | 2013


    Aircraft System Identification Using Artificial Neural Networks

    Kirkpatrick, Kenton / May, James / Valasek, John | AIAA | 2013