PDF(Pubmed)
Abstract：
BACKGROUND: Wearing a helmet reduces the risk of head injuries substantially in the event of a motorcycle crash. Countries around the world are committed to promoting helmet use, but the progress has been slow and uneven. There is an urgent need for large-scale data collection for situation assessment and intervention evaluation.
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.
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.
摘要：
背景：在摩托车撞车的情况下，戴头盔大大降低了头部受伤的风险。世界各国都致力于推动头盔的使用，但是进展缓慢且不平衡。迫切需要大规模数据收集，以进行情况评估和干预评估。
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。
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。
