In the context of road traffic safety, intelligent driver assistance systems are particularly important. During everyday driving, drivers are influenced by factors such as weather and fatigue, which can prevent them from quickly and accurately reacting to current traffic sign information. Therefore, to enable faster and more precise recognition, this paper designs an efficient traffic sign detection method. Image enhancement technology, particularly the Retinex algorithm, is introduced to improve the visual effect of images and enhance the separability of features. The introduction of the ConvNeXt V2 network further improves the detection speed and accuracy of the YOLOv8 model. Additionally, a self-collected dataset was supplemented to lay a good foundation for subsequent training.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic sign recognition system based on YOLOv8-ConvNeXt


    Beteiligte:
    Hu, Liang (Herausgeber:in) / Qin, Lijuan (Autor:in) / Tang, Xiaoyu (Autor:in) / Fan, Chubin (Autor:in)

    Kongress:

    International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2024) ; 2024 ; Shenyang, China


    Erschienen in:

    Proc. SPIE ; 13555


    Erscheinungsdatum :

    18.04.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Enhanced Traffic Sign Recognition Using Advanced YOLOv8 Model

    Choudhary, Nishant / Sharma, Rishabh / Upadhyay, Deepak et al. | IEEE | 2024


    Traffic Sign Detection Using YOLOv8

    Kumar, Rahul / Gupta, Aniket / D, Rajeswari | IEEE | 2024


    Research on traffic sign recognition based on the YOLOv8 algorithm

    Qi, Yinpeng / Ni, Hongxia / Feng, Siliang et al. | SPIE | 2024


    YOLO-TSR: A Novel YOLOv8-Based Network for Robust Traffic Sign Recognition

    Farhat, Wajdi / Rhaiem, Olfa Ben / Faiedh, Hassene et al. | Transportation Research Record | 2025


    Traffic sign detection and identification method based on YOLOv8

    ZHANG KAIYU / LIU NAN | Europäisches Patentamt | 2023

    Freier Zugriff