Motorcycle accidents pose significant risks, particularly when riders and passengers do not wear helmets. This study evaluates the efficacy of an advanced vision-language foundation model, OWLv2, in detecting and classifying various helmet-wearing statuses of motorcycle occupants using video data. We extend the dataset provided by the CVPR AI City Challenge and employ a cascaded model approach for detection and classification tasks, integrating OWLv2 and CNN models. The results highlight the potential of zero-shot learning to address challenges arising from incomplete and biased training datasets, demonstrating the usage of such models in detecting motorcycles, helmet usage, and occupant positions under varied conditions. We have achieved an average precision of 0.5324 for helmet detection and provided precision-recall curves detailing the detection and classification performance. Despite limitations such as low-resolution data and poor visibility, our research shows promising advancements in automated vehicle safety and traffic safety enforcement systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Evaluating Vision-Language Models for Zero-Shot Detection, Classification, and Association of Motorcycles, Passengers, and Helmets


    Contributors:
    Choi, Lucas (author) / Greer, Ross (author)


    Publication date :

    2024-10-07


    Size :

    4999568 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Anforderungen an Helme für Motorradfahrer zur Motorradsicherheit.
    Demands on helmets for active safety of motorcycles

    Schueler, Florian | ELBA - Federal Highway Research Institute (BASt) | 2011

    Free access

    Helmets

    Online Contents | 1994


    Motorcycles

    Online Contents | 2001


    Motorcycles

    Online Contents | 2010