自主面具检测使用单射击多盒检测器,ResNet-50通过使用深神经网络通过面部匹配来检索身份
S Vignesh Baalaji1, S Sandhya1, S A Sajidha1
1Present Address: School of Compter Science and Engineering, Vellore Institute of Technology, Chennai, India.
概括
本研究引入了一种用于实时面具检测和使用计算机视觉检索身份的自动化系统. 该系统准确地识别了不戴口罩的个人,有助于公共卫生合规.
科学领域:
- 计算机视觉和人工智能的人工智能
- 公共卫生技术 公共卫生技术
背景情况:
- 随着COVID-19的流行,人们需要采取有效的公共卫生措施,包括使用面罩.
- 手动监控面罩遵守是劳动密集型和不切实际的大规模执法.
- 计算机视觉为公共卫生任务的自动化实时监控提供了一个潜在的解决方案.
研究的目的:
- 开发一种自主系统,实时检测不戴口罩的个人.
- 实施一个身份检索机制,用于不合规的个人使用计算机视觉.
- 通过高效准确的自动化监测系统,加强公共卫生执法.
主要方法:
- 微调预先训练的ResNet-50模型,用于对面具和非面具面部进行二进制分类.
- 使用基于单击多盒探测器 (SSD) 的Caffe模型进行实时面部检测.
- 使用基于VGG-Face的深神经网络进行准确的面部匹配和身份检索.
主要成果:
- 面罩检测分类器实现了99.74%的高精度.
- 身份检索模型表现出强的性能,准确率为98.24%.
- 集成系统成功地检测到没有口罩的个人,并从数据库中检索他们的身份.
结论:
- 拟议的计算机视觉系统为实时面罩监控提供了有效和准确的解决方案.
- 这项技术可以显著减少人们在执行公共卫生指南方面的努力.
- 该系统提供了一个强有力的机制,用于识别和管理不遵守面罩任务的情况.
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