Using the roadside fixed camera to identify and calculate the three-dimensional coordinates of vehicles in the scene is of great significance for ensuring vehicle safety and realizing vehicle intelligent network connection and automatic driving. This paper proposes a monocular camera vehicle detection algorithm based on YOLOv7 to realize vehicle identification and frame selection in the scene. In the process of three-dimensional coordinate calculation, this paper abandons the common depth estimation method based on deep learning, but adopts the coordinate calculation method based on camera calibration, which greatly improves the target depth calculation speed and can well realize the real-time positioning of vehicles in motion.


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    Titel :

    Research on monocular vehicle detection and 3D coordinate calculation based on YOLOv7


    Beteiligte:
    Zhang, Jie (Herausgeber:in) / Sun, Ning (Herausgeber:in) / Xu, Yaoqing (Autor:in) / Dai, Zongmiao (Autor:in) / Wu, Peizhang (Autor:in) / Guo, Jianpo (Autor:in) / Wang, Weiru (Autor:in)

    Kongress:

    Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024) ; 2024 ; Beijing, China


    Erschienen in:

    Proc. SPIE ; 13181 ; 131815I


    Erscheinungsdatum :

    19.07.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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