  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.
