基于深度学习的图书馆智能照明系统的设计
Hong Wang1,2, Yingjie Sheng1,2, Yang Guo3,4
1College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.
Scientific reports
|August 31, 2025
概括
这项研究引入了大学图书馆的智能照明系统,使用了改进的YOLOv5算法来准确地检测和定位人类. 这种智能系统提高了能源效率和图书馆的智能化.
科学领域:
- 计算机视觉
- 人工智能
- 智能系统工程
背景情况:
- 目前的大学图书馆照明系统的智能性较低, 能耗较高.
- 需要自动化和适应性照明解决方案来改善用户体验并减少能源浪费.
研究的目的:
- 设计和实施基于分区自主控制的大学图书馆智能照明解决方案.
- 增强人类目标检测和空间定位准确性以适应照明.
主要方法:
- 一个改进的YOLOv5算法,包含一个坐标注意力机制和深度可分离的卷积,用于人类目标检测.
- 一种多层感知子 (MLP) 模型,用于将二维人类坐标映射到三维空间定位.
- 在YOLOv5中优化边界框回归损失函数.
主要成果:
- 在人体检测中达到91.4%的平均精度 (mAP).
- 展示了图书馆空间内多个人的快速准确定位.
- 实验结果显示,与基线方法相比,平均精度增加了6. 9%.
结论:
- 拟议的智能照明系统有效地进行人体检测和定位.
- 该系统为学术图书馆开发智能照明提供了实用且可参考的解决方案.
- 这种方法显著提高了图书馆照明系统的智能和能源效率.
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