Design of library intelligent lighting system based on deep learning

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.