面罩识别与增强的库克优化和基于深度学习的更快的区域神经网络
Binay Kumar Pandey1, Digvijay Pandey2, Mesfin Esayas Lelisho3
1Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology Pantnagar, Uttrarakhand, India. binaydece@gmail.com.
Scientific reports
|November 29, 2024
概括
这项研究介绍了一种基于无人机的系统,用于监测户外的口罩使用和社交距离. 该系统使用图像处理和人工智能来检测口罩合规性,并在发生违规时向当局发出警报.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 公共卫生监督 公共卫生监督
背景情况:
- 不受控制的户外环境对从空中图像中准确检测面罩构成挑战.
- 现有的监测方法可能缺乏实时监测公共卫生指南的动态能力.
研究的目的:
- 开发和评估基于无人机 (UAV) 的系统,用于识别面具使用情况和监控社会距离.
- 为了提高无人机的图像质量,在户外环境中提高检测准确度.
主要方法:
- 图像预处理技术,包括灰度转换和对比度增强 (最佳波纹基面罩,增强瓜方法).
- 使用Gabor变换 (GT) 和冲击宽度变换 (SWT) 进行特征提取.
- 使用加权天真湾 (WNBC) 和基于区域的卷积神经网络 (R-CNN) 进行分类,用于面具检测和社交距离监控.
主要成果:
- 该系统成功地识别了正确和不正确的口罩佩戴,并监测了拥挤地区的社交距离.
- 针对没有戴口罩的个人,向医务人员和警察发送警报,自动音频警报促进社会距离.
- 该方法实现了高性能指标,使用大型数据集和10倍交叉验证进行验证.
结论:
- 拟议的无人机系统为公共卫生监测提供了有效的解决方案,特别是在监测面具遵守和社会距离的监测方面.
- 集成先进的图像处理和深度学习算法,在现实世界户外条件下提高了检测准确度和系统效率.
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