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.

翻译：大学图书馆的座位占用现象是一个普遍存在的问题。然而，现有解决方案如基于软件的座位预约和基于传感器的占用检测已被证明无法有效解决这一问题。在本研究中，我们提出了一种新方法：基于Faster RCNN的串行双通道目标检测模型。该模型旨在识别图书馆内所有被占用的座位，并持续更新关于座位占用状态的实时信息。为训练该神经网络，我们使用了结合虚幻引擎5（UE5）生成的虚拟图像与真实图像的独特数据集。值得注意的是，我们的测试结果突出了在训练卷积神经网络（CNN）时，尤其是在特定场景下，应用自生成虚拟数据集所带来的显著性能提升。此外，本研究引入了一种开创性的检测模型，该模型将基于Faster R-CNN的目标检测框架与基于迁移学习的目标分类算法无缝融合。这种融合不仅大幅减少了神经网络训练所需的计算资源和时间投入，还显著提高了单帧检测效率。同时，我们还精心开发了用户友好的网页界面和移动应用程序，构建了一个用于检测图书馆内座位占用的计算机视觉驱动平台。值得关注的是，座位占用识别准确率的大幅提升，以及神经网络训练所需计算资源的减少，共同显著提高了图书馆座位管理的整体效率。