With the continuous growth of car ownership, intelligent transportation has penetrated into our daily life. As an important part of intelligent transportation, car detection has also been developed rapidly. It plays a vital role on the planning of urban public transportation and brings great convenience for citizens to commute. Due to the extremely complex urban conditions, car detection encounters many difficulties. By analyzing a large amount of vehicle color difference data, it draws the conclusion that illumination is the main factor of affecting detection and recognition. Based on the convolutional neural network framework, this paper focuses on low-light enhancement and car recognition, aiming to realize the task of car recognition in more complex low-light situations. Notably, in those scenarios, the YOLOv4 model with a basic training set can recognize automobiles well.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vehicle detection in dark weather based on YOLO and MSRCR method


    Beteiligte:
    Hu, Haiyun (Autor:in) / Gong, Xiangwu (Autor:in)

    Kongress:

    Second International Conference on Optics and Image Processing (ICOIP 2022) ; 2022 ; Taian,China


    Erschienen in:

    Proc. SPIE ; 12328


    Erscheinungsdatum :

    10.09.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Hybrid Approach for Detection in Foggy Environments for Self Driving Cars: YOLO and MSRCR Techniques

    Mahaadev, Maharaj / Ghosh, Debjani / Dogra, Madhavik et al. | IEEE | 2023



    YOLO-RAW: Advancing UAV Detection With Robustness to Adverse Weather Conditions

    Munir, Adnan / Siddiqui, Abdul Jabbar / Hossain, M. Shamim et al. | IEEE | 2025


    Improved Vehicle Detection and Tracking Using YOLO and CSRT

    Amitha, I. C. / Narayanan, N. K. | Springer Verlag | 2021


    Traffic jam detection method based on YOLO

    ZHU MINGFU / NI SHUIPING / ZHU ZHIDAN et al. | Europäisches Patentamt | 2024

    Freier Zugriff