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一个有效的基于堆叠的自编码器的深度可分离的卷积神经网络模型,用于面罩检测.

Sundaravadivazhagan Balasubaramanian1, Robin Cyriac1, Sahana Roshan1

  • 1University of Technology and Applied Sciences - Al Mussanah, Department of Information Technology, Al Muladdah, 314, South Al Batinah, Oman.

Array (New York, N.Y.)
|June 9, 2023
PubMed
概括

本研究介绍了使用主要组件分析 (PCA) 和深度可分离卷积神经网络 (DWSC-NN) 进行准确的面具检测的深度学习方法. 该系统有效地识别个人是否戴着口罩,以及是否正确佩戴口罩,从而达到高准确度.

关键词:
在 COVID-19 疫情中,深度学习是一种深度学习.深度可分离的卷积神经网络.机器学习是机器学习.主要组件分析的主要组件分析.堆叠的自动编码器编码器

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 公共卫生 公共卫生

背景情况:

  • 由于COVID-19大流行,需要广泛采用公共卫生措施,包括戴口罩.
  • 有效的查系统对于公共空间至关重要,以确保遵守口罩要求.
  • 现有的面部检测模型往往缺乏与尺寸缩小技术的整合.

研究的目的:

  • 开发一个深度学习模型,以准确检测口罩的使用和正确佩戴.
  • 解决公共区域自动查系统的需求.
  • 通过整合缩小维度来改进现有的面部检测方法.

主要方法:

  • 实现一个堆叠的自动编码器 (SAE) 技术.
  • 整合主要组件分析 (PCA) 以减少特征.
  • 使用深度可分离的卷积神经网络 (DWSC-NN) 进行图像分析.

主要成果:

  • 在面具检测方面获得了94.16%的高精度得分.
  • 获得了96.009%的F1得分,这表明在识别蒙面人员方面表现出色.
  • PCA有效地减少了不相关的特征,提高了检测率.

结论:

  • 提出的深度学习方法在检测口罩使用方面表现出显著的有效性.
  • PCA和DWSC-NN的组合为自动化口罩查提供了一个强大的解决方案.
  • 这种方法通过使可靠的口罩合规性监测成为可能,有助于公共卫生安全.