Version 1
: Received: 27 June 2023 / Approved: 28 June 2023 / Online: 28 June 2023 (15:29:49 CEST)
How to cite:
Yang, G.; Chang, X.; Wang, Z.; Yang, M.; Chen, X. A serial dual-channel library occupancy detection system based on Faster RCNN. Preprints2023, 2023062050. https://doi.org/10.20944/preprints202306.2050.v1
Yang, G.; Chang, X.; Wang, Z.; Yang, M.; Chen, X. A serial dual-channel library occupancy detection system based on Faster RCNN. Preprints 2023, 2023062050. https://doi.org/10.20944/preprints202306.2050.v1
Yang, G.; Chang, X.; Wang, Z.; Yang, M.; Chen, X. A serial dual-channel library occupancy detection system based on Faster RCNN. Preprints2023, 2023062050. https://doi.org/10.20944/preprints202306.2050.v1
APA Style
Yang, G., Chang, X., Wang, Z., Yang, M., & Chen, X. (2023). A serial dual-channel library occupancy detection system based on Faster RCNN. Preprints. https://doi.org/10.20944/preprints202306.2050.v1
Chicago/Turabian Style
Yang, G., Min Yang and Xin Chen. 2023 "A serial dual-channel library occupancy detection system based on Faster RCNN" Preprints. https://doi.org/10.20944/preprints202306.2050.v1
Abstract
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 collection with UE5 virtual reality. The results of our tests also demonstrate that the utilization of personalized virtual dataset significantly enhances the performance of the convolutional neural network (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three essential 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 efficiency of library seat management.
Keywords
Serial dual-channel detection; Faster RCNN; Transfer learning; Object classification; End to end system
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
