Wang Hong, Sheng Yingjie, Guo Yang, Tian Zengrui, Chu Pan, Chen Mengyao, Li Hao
College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.
Henan Engineering Research Center of Intelligent Buildings and Human Settlements, Zhengzhou, 450000, China.
Sci Rep. 2025 Aug 31;15(1):32025. doi: 10.1038/s41598-025-96690-4.
Aiming at the problems of low intelligence and energy waste in the current university library lighting system, this paper designs an intelligent lighting solution based on sub-area autonomy control. The improved YOLOv5 human target detection algorithm and the human spatial localization algorithm based on multilayer perceptron are used. The introduction of a coordinate attention mechanism and depth-separable convolution into the YOLOv5 backbone network, along with the optimization of the bounding box regression loss function, resulted in a mean average precision (mAP) of 91.4% for human body detection. The MLP model is used to map the 2D coordinates of the human body output from the human target detection algorithm to the 3D coordinates in the actual space, which realizes the fast and accurate localization of the distribution of multiple people in the museum. The related hardware circuits are designed to build a simple lighting system and simulated experiments. The experimental results show that the mean average accuracy of the proposed algorithm increased by 6.9% points, and the program can effectively complete the task of human body detection and localization, which provides certain reference significance for the research of intelligent lighting system in college libraries.
针对当前高校图书馆照明系统存在的智能化程度低和能源浪费等问题，本文设计了一种基于分区自主控制的智能照明解决方案。采用了改进的YOLOv5人体目标检测算法和基于多层感知器的人体空间定位算法。在YOLOv5主干网络中引入坐标注意力机制和深度可分离卷积，并优化边界框回归损失函数，人体检测的平均精度均值（mAP）达到了91.4%。利用MLP模型将人体目标检测算法输出的人体二维坐标映射到实际空间中的三维坐标，实现了博物馆内多人分布的快速准确定位。设计了相关硬件电路，构建了一个简单的照明系统并进行了仿真实验。实验结果表明，所提算法的平均精度均值提高了6.9个百分点，该方案能够有效完成人体检测与定位任务，为高校图书馆智能照明系统的研究提供了一定的参考意义。
