This paper proposes a vehicle state classification model utilizing drones for achieving highly reliable autonomous driving vehicles. The model offers a wide field of view, providing improved data for the autonomous driving assistance system by considering the relationships with existing vehicles in the video. The object detection model, based on Convolutional Neural Networks, is trained using image data collected by drones. The YOLOv5 model is employed for detecting vehicles on the road. Detected vehicles are cropped into five candidate regions based on proximity, which serve as input data for the vehicle state classification model. The proposed model introduces a depthwise CSP block to effectively learn high-density layers and facilitate efficient weight learning. Additionally, a method that leverages deformable residual block to set receptive fields relying on features extracted from the fixed receptive field of the conventional convolutional kernel is proposed, enabling faster identification of critical features. Experiments are conducted using directly collected drone data to evaluate the performance of the trained model and compare it with a model based on dense layers. The approach aims to provide a more reliable and efficient vehicle state model for a robust autonomous driving system.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    DE-VSNet: Dense Efficient Vehicle State Classification Using Drone Traffic Dataset


    Contributors:


    Publication date :

    2023-08-09


    Size :

    2324506 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Low Computational Vehicle Lane Changing Prediction Using Drone Traffic Dataset

    Lee, Youlkyeong / Tang, Qing / Choi, Jehwan et al. | IEEE | 2022



    CQSkyEyeX: A Drone Dataset of Vehicle Trajectory on Chinese Expressways

    Xu, Jin / Pan, Cunshu / Dai, Zhenhua et al. | Springer Verlag | 2024


    TRAFFIC CONTROL SYSTEM USING DRONE

    SHIN SOO YOUNG / KIM JI HUN / WOO JUN HYUK et al. | European Patent Office | 2024

    Free access

    Traffic Control Drone

    KIM JIN YOU / YUN DONG HYEOK / JENG IN SUNG et al. | European Patent Office | 2023

    Free access