气体传感器和实时视频用于准确的教室占用检测
Koundinya Challa1, Issa W AlHmoud1, Chandra Jaiswal1
1North Carolina A&T State University, 1601 E Market St, Greensboro, North Carolina, 27411, 3363347, USA.
MethodsX
|June 19, 2025
概括
这项研究通过实时教室占用检测提高了供暖,通风和空调 (HVAC) 的效率. 结合视频分析和气体传感器数据,实现了超过98%的精度,以优化能源使用.
科学领域:
- 建筑科学 建筑科学
- 计算机视觉 计算机视觉
- 环境传感器环境传感器
背景情况:
- 暖通空调系统消耗大量的能量,效率经常受到不准确的占用数据的影响.
- 基于实时占用率优化HVAC运营对于智能建筑节能至关重要.
研究的目的:
- 开发和验证用于准确,实时的教室占用检测的双模系统.
- 通过精确的占用监控,提高HVAC效率和建筑的可持续性.
主要方法:
- 视频源 (Logitech C20网络摄像机) 和气体传感器数据 (MS1100) 的集成.
- 利用了YOLOv4物体检测模型,在超过20,000个标记的人脸图像上进行训练.
- 使用OpenCV进行实时视频流处理和分析.
主要成果:
- 在实时的乘客识别和计数中实现了超过98%的准确性.
- 通过结合传感器模式,在各种环境条件下证明了更好的检测可靠性.
- 验证了用于动态HVAC控制的强大且可扩展的框架.
结论:
- 拟议的双模系统为HVAC优化实时占用检测提供了重大进展.
- 这种方法提供了一个实用且可扩展的解决方案,用于降低智能建筑的能源消耗.
- 提高占用检测的准确性和可靠性有助于提高建筑的可持续性和能源效率.
相关概念视频
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