Research on an Intelligent Study Room Reservation System Based on Machine Vision and Deep Learning Algorithms

This paper proposes an intelligent study room reservation system based on machine vision and deep learning algorithms, aiming to solve the problems of poor real-time performance and easy errors in traditional reservation systems. By combining the YOLOv8 model and real-time camera monitoring, the system can efficiently and accurately detect the occupancy of study room seats and synchronize the information to the reservation management system in real time. The experimental results show that under sufficient lighting and partial occlusion conditions, the detection accuracy of the system is 99.2 % and 96.8 % respectively. Under extreme conditions such as complete occlusion, the accuracy drops to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 1. 3 \%}$</tex>. The average reservation response time of the system is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1. 8}$</tex> seconds, and the seat utilization rate during peak hours is increased by 15 %. Although the system performs well, it still faces challenges in extremely poor lighting and complete occlusion scenarios. Future work will improve performance through multi-camera fusion technology. The innovation of the system lies in the combination of deep learning and machine vision to achieve fully automated detection and management of study room seats.