Objectives: The primary objective was to characterize the range of Knowledge, Attitude, and Practice (KAP) of Helmet use in children amongst parents to prevent head injuries and death. Methods: This is a cross-section...Objectives: The primary objective was to characterize the range of Knowledge, Attitude, and Practice (KAP) of Helmet use in children amongst parents to prevent head injuries and death. Methods: This is a cross-sectional study, done by online survey using a snowball sampling technique, the number of included responses were 386 parents (Male and female) living in Riyadh Aged 21 - 60 years old or above. Results: The study showed that there is a difference in Parents’ belief in the importance of helmet use while riding a Bicycle vs Motorcycle/Quad bike and that was affected by parents’ education level, almost all the people who answered the survey (76.7%) agree that it is important for their children to wear a helmet when riding both a Bicycle and a Motorcycle or Quadbike with a cumulative percentage of (93.8%). And almost all agreed on multiple approaches to help increase helmet use be it by forcing rental shops to give out helmets, forcing sellers to recommend the use of helmets, increasing awareness campaigns, and imposing fines for not wearing helmets. Conclusions: This study is the first to explore Family helmet use while riding Bicycles and Motorcycles/Quad bikes. Although Parent’s belief in the importance of helmet use for their children was high, it is clear that the level of practice is low. With that the risk of head injuries might be high, our findings suggest that safety interventions for increasing pediatric helmet use are needed to increase helmet use and reduce the risk of head injury and hospitalization.展开更多
针对摩托车头盔的传统检测方法准确率低、泛化能力差和目标检测网络参数量大难以在嵌入式设备运行的问题,提出改进的YOLOv2的MNXt-ECA-D-YOLOv2目标检测算法模型。首先引入Mobile Ne Xt网络替换YOLOv2原始骨干网络,其次在Mobile Ne Xt...针对摩托车头盔的传统检测方法准确率低、泛化能力差和目标检测网络参数量大难以在嵌入式设备运行的问题,提出改进的YOLOv2的MNXt-ECA-D-YOLOv2目标检测算法模型。首先引入Mobile Ne Xt网络替换YOLOv2原始骨干网络,其次在Mobile Ne Xt的沙漏块中引入密集连接结构同时在网络中引入有效通道注意力机制,然后在不同深度网络层应用不同的激活函数,最后在网络输出卷积层之前增加Drop Block模块。采用K-means聚类算法重新设计了自制数据集的先验框尺寸。实验结果表明,改进后的模型相比原始YOLOv2,在AP50指标上提高了3.53%,模型大小减少77.44%,检测速度提高了近4倍。通过对比实验可知,改进后的YOLOv2模型在保持较高的精度下模型更小,在CPU中的推理速度更快,具有一定的应用价值。展开更多
文摘Objectives: The primary objective was to characterize the range of Knowledge, Attitude, and Practice (KAP) of Helmet use in children amongst parents to prevent head injuries and death. Methods: This is a cross-sectional study, done by online survey using a snowball sampling technique, the number of included responses were 386 parents (Male and female) living in Riyadh Aged 21 - 60 years old or above. Results: The study showed that there is a difference in Parents’ belief in the importance of helmet use while riding a Bicycle vs Motorcycle/Quad bike and that was affected by parents’ education level, almost all the people who answered the survey (76.7%) agree that it is important for their children to wear a helmet when riding both a Bicycle and a Motorcycle or Quadbike with a cumulative percentage of (93.8%). And almost all agreed on multiple approaches to help increase helmet use be it by forcing rental shops to give out helmets, forcing sellers to recommend the use of helmets, increasing awareness campaigns, and imposing fines for not wearing helmets. Conclusions: This study is the first to explore Family helmet use while riding Bicycles and Motorcycles/Quad bikes. Although Parent’s belief in the importance of helmet use for their children was high, it is clear that the level of practice is low. With that the risk of head injuries might be high, our findings suggest that safety interventions for increasing pediatric helmet use are needed to increase helmet use and reduce the risk of head injury and hospitalization.
文摘针对摩托车头盔的传统检测方法准确率低、泛化能力差和目标检测网络参数量大难以在嵌入式设备运行的问题,提出改进的YOLOv2的MNXt-ECA-D-YOLOv2目标检测算法模型。首先引入Mobile Ne Xt网络替换YOLOv2原始骨干网络,其次在Mobile Ne Xt的沙漏块中引入密集连接结构同时在网络中引入有效通道注意力机制,然后在不同深度网络层应用不同的激活函数,最后在网络输出卷积层之前增加Drop Block模块。采用K-means聚类算法重新设计了自制数据集的先验框尺寸。实验结果表明,改进后的模型相比原始YOLOv2,在AP50指标上提高了3.53%,模型大小减少77.44%,检测速度提高了近4倍。通过对比实验可知,改进后的YOLOv2模型在保持较高的精度下模型更小,在CPU中的推理速度更快,具有一定的应用价值。
