We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.


    Access

    Download


    Export, share and cite



    Title :

    VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH


    Contributors:


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Travel Mutual Information Classification Based on Convolution Neural Network

    Hou, Xiaoyu / Hu, Siyu | British Library Conference Proceedings | 2022




    Co-Channel Multi-Signal Modulation Classification Based on Convolution Neural Network

    Yin, Zhendong / Zhang, Rui / Wu, Zhilu et al. | IEEE | 2019