一种使用变压器和卷积神经网络模型的新型混合面罩检测方法
Haifa M Al-Sarrar1, Heyam H Al-Baity1
1Information Technology Department, Collage of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种混合深度学习模型,将变压器和卷积神经网络结合起来,用于准确地检测面罩. 该模型达到89.4%的精度,为公共卫生监测提供了实用解决方案.
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.
- 人工智能的人工智能
背景情况:
- 面罩检测对于公共安全至关重要,目前的模型通常基于卷积神经网络 (CNN).
- 美国有线电视新闻网 (CNN) 面临着低质量的图像和捕捉远程依赖的困难,这限制了它们的有效性.
- 变压器神经网络为处理视觉数据中的远程依赖提供了增强的能力.
研究的目的:
- 开发一个自动混合面罩检测模型,集成变压器和卷积神经网络架构.
- 根据现有最先进的方法,评估拟议的混合模型的性能.
- 评估该模型是否适用于实时公共卫生应用.
主要方法:
- 开发了一种混合深度学习模型,结合了变压器和CNN组件.
- 训练和评估面罩检测任务的模型.
- 将混合型号的性能指标 (精度,执行时间) 与其他领先型号进行比较.
主要成果:
- 拟议的混合型号实现了最高的平均精度89.4%.
- 该模型展示了2.8秒的高效执行时间.
- 实验结果证实,与现有方法相比,混合动力模型的性能优越.
结论:
- 混合变压器-CNN模型对于准确的面罩检测是有效的.
- 该模型的高精度和速度使其适合实用,实时应用.
- 这项技术可以为传染病控制和公共卫生保健工作做出重大贡献.
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