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.


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    Titel :

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


    Beteiligte:
    Choi, Lucas (Autor:in) / Greer, Ross (Autor:in)


    Erscheinungsdatum :

    07.10.2024


    Format / Umfang :

    4999568 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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

    Schueler, Florian | ELBA - Bundesanstalt für Straßenwesen (BASt) | 2011

    Freier Zugriff

    Helmets

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    Motorcycles

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    Motorcycles

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