  The phenomenon of seat occupancy in university libraries is a prevalent
issue. However, existing solutions, such as software-based seat reservations
and sensors-based occupancy detection, have proven to be inadequate in
effectively addressing this problem. In this study, we propose a novel
approach: a serial dual-channel object detection model based on Faster RCNN.
This model is designed to discern all instances of occupied seats within the
library and continuously update real-time information regarding seat occupancy
status. To train the neural network, a distinctive dataset is utilized, which
blends virtual images generated using Unreal Engine 5 (UE5) with real-world
images. Notably, our test results underscore the remarkable performance uplift
attained through the application of self-generated virtual datasets in training
Convolutional Neural Networks (CNNs), particularly within specialized
scenarios. Furthermore, this study introduces a pioneering detection model that
seamlessly amalgamates the Faster R-CNN-based object detection framework with a
transfer learning-based object classification algorithm. This amalgamation not
only significantly curtails the computational resources and time investments
needed for neural network training but also considerably heightens the
efficiency of single-frame detection rates. Additionally, a user-friendly web
interface and a mobile application have been meticulously developed,
constituting a computer vision-driven platform for detecting seat occupancy
within library premises. Noteworthy is the substantial enhancement in seat
occupancy recognition accuracy, coupled with a reduction in computational
resources required for neural network training, collectively contributing to a
considerable amplification in the overall efficiency of library seat
management.
