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. Furthermore, we develop a user-friendly Web interface and mobile APP to create a computer vision-based platform for library seat occupancy detection. To construct our dataset, we combine real-world data collec-tion with UE5 virtual reality. The results of our tests also demonstrate that the utilization of per-sonalized virtual dataset significantly enhances the performance of the convolutional neural net-work (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three es-sential steps. Firstly, we employ Faster RCNN algorithm to determine whether a seat is occupied by an individual. Subsequently, we utilize an object classification algorithm based on transfer learning, to classify and identify images of unoccupied seats. This eliminates the need for manual judgment regarding whether a person is suspected of occupying a seat. Lastly, the Web interface and APP provide seat information to librarians and students respectively, enabling comprehensive services. By leveraging deep learning methodologies, this research effectively addresses the issue of seat occupancy in library systems. It significantly enhances the accuracy of seat occupancy recognition, reduces the computational resources required for training CNNs, and greatly improves the effi-ciency of library seat management.

翻译：大学图书馆座位占用现象普遍存在。然而，现有解决方案（如基于软件的座位预约系统和基于传感器的占用检测）在有效应对此问题方面仍显不足。本研究提出了一种新方法：基于Faster RCNN的串行双通道目标检测模型。此外，我们开发了用户友好的Web界面和移动APP，构建了基于计算机视觉的图书馆座位占用检测平台。为构建数据集，我们将真实数据采集与UE5虚拟现实技术相结合。测试结果还表明，在特定场景中，个性化虚拟数据集的使用显著提升了卷积神经网络（CNN）的性能。串行双通道检测模型包含三个关键步骤：首先，采用Faster RCNN算法判断座位是否被个体占用；其次，利用基于迁移学习的目标分类算法对空座位图像进行分类识别，从而无需人工判断是否有人疑似占用座位；最后，Web界面和APP分别向图书馆员和学生提供座位信息，实现综合服务。通过深度学习方法，本研究有效解决了图书馆系统中的座位占用问题，显著提升了座位占用识别准确率，减少了训练CNN所需的计算资源，并大幅提高了图书馆座位管理效率。