In response to the extensive construction of digital campuses and the initiative for energy-saving campuses

especially in colleges and universities

the energy-saving issue of classroom lighting has become particularly crucial. This paper proposes an intelligent energy-saving lighting control system. This system relies on OpenCV combined with the YOLOv5 algorithm for personnel target detection and light brightness detection

aiming to achieve automated management of classroom lighting and thereby achieve the goal of energy conservation and emission reduction. This paper uses Raspberry Pi as the core controller to build the hardware platform of the system to accelerate the speed of image processing. Conduct in-depth research and optimization on personnel target detection and light brightness detection using OpenCV combined with the YOLOv5 algorithm. Design the control logic and user interface of the lamp group to achieve lighting control based on the distribution of personnel in the classroom

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. Through experimental verification

the system can respond quickly and accurately identify personnel. The experimental results show the potential of this system in terms of energy saving and classification accuracy. This intelligent lighting system has obvious advantages in cost-effectiveness and ease of operation

and has good practical value and broad application prospects.