神经网络在面具检测中的贡献,以减少COVID-19的传播
Maminiaina Alphonse Rafidison1, Andry Harivony Rakotomihamina1, Sabine Harisoa Jacques Rafanantenana1
1Telecommunication-Automatic-Signal-Image-Research, Laboratory/Doctoral School in Science and Technology of Engineering and Innovation, University of Antananarivo - Ecole Supérieure Polytechnique d'Antananarivo, Ankatso, 101 Antananarivo, Madagascar.
概括
本研究介绍了一种使用脉冲对神经网络和完全连接的神经网络的新型面罩检测方法,以防止COVID-19的传播. 该系统准确地识别了不戴口罩的个人,引发了公众健康的警报.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 由于COVID-19的传播,需要采取诸如口罩等公共卫生措施.
- 由于不遵守规定,强制执行口罩任务具有挑战性.
- 深度学习方法主导着目前的面罩检测研究.
研究的目的:
- 开发和评估一种用于自动检测面罩的新方法.
- 通过识别不遵守口罩政策的个人来防止病毒的传播.
- 创建一个有效的实时监控系统.
主要方法:
- 这是一种混合方法,结合了脉冲对神经网络 (PCNN) 和完全连接的神经网络 (FCNN).
- 一个三步过程:选择几何区域,基于PCNN的特征提取和决策.
- 该系统处理来自视频监控的图像,触发违规报警.
主要成果:
- 提出的方法实现了高精度,在各种数据集中从83.2%到100%不等.
- 证明了有效处理的最小计算时间.
- 在各种数据集中成功识别了不戴口罩的个人.
结论:
- PCNN-FCNN混合型号为面罩检测提供了有效和高效的解决方案.
- 该系统有可能提高公共卫生合规性,减少病毒传播.
- 该方法的高精度和速度使其适用于现实世界的监视应用.
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