A vehicle make and model recognition (VMMR) system is a common requirement in the field of intelligent transportation systems (ITS). However, it is a challenging task because of the subtle differences between vehicle categories. In this paper, we propose a hierarchical scheme for VMMR. Specifically, the scheme consists of (1) a feature extraction framework called weighted mask hierarchical bilinear pooling (WMHBP) based on hierarchical bilinear pooling (HBP) which weakens the influence of invalid background regions by generating a weighted mask while extracting features from discriminative regions to form a more robust feature descriptor; (2) a hierarchical loss function that can learn the appearance differences between vehicle brands, and enhance vehicle recognition accuracy; (3) collection of vehicle images from the Internet and classification of images with hierarchical labels to augment data for solving the problem of insufficient data and low picture resolution and improving the model’s generalization ability and robustness. We evaluate the proposed framework for accuracy and real-time performance and the experiment results indicate a recognition accuracy of 95.1% and an FPS (frames per second) of 107 for the framework for the Stanford Cars public dataset, which demonstrates the superiority of the method and its availability for ITS.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Hierarchical Scheme for Vehicle Make and Model Recognition


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2021-07-07




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Vehicle make and model recognition using bag of expressions

    Jamil, Adeel Ahmad / Hussain, Fawad / Yousaf, Muhammad Haroon et al. | BASE | 2020

    Free access

    Cross-Granularity Network for Vehicle Make and Model Recognition

    Tan, Shi Hao / Chuah, Joon Huang / Chow, Chee-Onn et al. | IEEE | 2025


    Deep Learning Based Approaches for Vehicle Make and Model Recognition

    Devamane, Shridhar B. / Rao, Trupthi | TIBKAT | 2023


    DeepCar 5.0: Vehicle Make and Model Recognition Under Challenging Conditions

    Amirkhani, Abdollah / Barshooi, Amir Hossein | IEEE | 2023