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一种基于YOLO的新方法,用于从实时视频中保持社交距离
Mehmet Şirin Gündüz1, Gültekin Işık1
1Department of Computer Engineering, Igdir University, Igdir, Turkey.
Neural computing & applications
|June 5, 2023
概括
这项研究引入了一个实时计算机视觉系统,用于监控室内空间的物理距离遵守. 该方法使用深度学习来检测人员和测量距离,帮助公共卫生工作.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 公共卫生技术 公共卫生技术
背景情况:
- COVID-19 传播突出了有效的身体距离措施的必要性.
- 在室内环境中,在个人之间保持至少一米的距离是关键的预防措施.
- 当前的监控方法可能缺乏实时能力,以确保合规.
研究的目的:
- 提出和评估实时方法,监测室内环境中的物理距离遵守.
- 利用计算机视觉和深度学习进行自动检测和距离估计.
- 评估不同YOLO模型在这个特定应用中的性能.
主要方法:
- 使用了YOLO (你只看一次) 对象检测模型,一个卷积神经网络.
- 在微软COCO数据集上预先训练了YOLO模型用于人身检测.
- 实施实时分析以估计检测到的个体之间的物理距离.
- 使用精度,每秒 (FPS) 和平均平均精度 (mAP) 评估性能.
主要成果:
- YOLO v3显示了高精度 (87.07%) 和平均精度 (mAP) (89.91%).
- YOLO v5s实现了最高的率 (FPS) 18.71,表明实时处理效率.
- 该研究成功验证了关于远程监控的深度学习方法.
结论:
- 拟议的计算机视觉和深度学习方法对于实时监控物理距离是有效的.
- YOLO模型为公共空间的自动化合规检查提供了可行的解决方案.
- 这些发现为在未来的公共卫生监测场景中应用类似技术提供了基础.
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