METHODS: This study proposes a scalable, low-cost algorithm to estimate helmet-wearing rates. Applying the state-of-the-art deep learning technique for object detection to images acquired from Google Street View, the algorithm has the potential to provide accurate estimates at the global level.
RESULTS: Trained on a sample of 3995 images, the algorithm achieved high accuracy. The out-of-sample prediction results for all three object classes (helmets, drivers, and passengers) reveal a precision of 0.927, a recall value of 0.922, and a mean average precision at 50 (mAP50) of 0.956.
CONCLUSIONS: The remarkable model performance suggests the algorithm\'s capacity to generate accurate estimates of helmet-wearing rates from an image source with global coverage. The significant enhancement in the availability of helmet usage data resulting from this approach could bolster progress tracking and facilitate evidence-based policymaking for helmet wearing globally.
方法:这项研究提出了一种可扩展的,估计头盔佩戴率的低成本算法。将最先进的深度学习技术应用于从Google街景获取的图像进行对象检测,该算法有可能在全球范围内提供准确的估计。
结果:在3995张图像样本上进行了培训,该算法取得了较高的精度。所有三个对象类别的样本外预测结果(头盔,司机,和乘客)显示的精度为0.927,召回值为0.922,50时的平均精度(mAP50)为0.956。
结论:出色的模型性能表明,该算法能够从覆盖全球的图像源中准确估计头盔佩戴率。这种方法导致的头盔使用数据的可用性显着提高,可以加强进度跟踪,并促进全球头盔佩戴的循证决策。