评估视觉-语言模型在摩托车、乘客和头盔的零样本检测、分类与关联中的应用
Evaluating Vision-Language Models for Zero-Shot Detection, Classification, and Association of Motorcycles, Passengers, and Helmets
AI总结：
本文扩展 CVPR AI City Challenge 数据集，采用 OWLv2 与 CNN 级联的零样本方法检测摩托车、乘员位置及头盔佩戴状态，头盔检测平均精度达 0.5324。
摘要：
摩托车事故带来重大风险，尤其是在骑手和乘客未佩戴头盔时。本研究评估了先进视觉-语言基础模型 OWLv2 在利用视频数据检测并分类摩托车乘员各种头盔佩戴状态方面的有效性。我们扩展了 CVPR AI City Challenge 提供的数据集，并采用级联模型方法处理检测和分类任务，整合了 OWLv2 与 CNN 模型。结果凸显了零样本学习在应对不完整且有偏的训练数据集所带来挑战方面的潜力，展示了此类模型在不同条件下检测摩托车、头盔使用情况和乘员位置的用途。我们在头盔检测上取得了 0.5324 的平均精度，并提供了详述检测与分类性能的精确率-召回率曲线。尽管存在低分辨率数据和能见度差等局限，本研究仍展示了自动化车辆安全与交通安全执法系统中具有前景的进展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
