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.
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.
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.
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