Abstract
This study presents a novel classroom surveillance system that integrates multiple modalities, including drowsiness, tracking of mobile phone usage, and face recognition,to assess student attentiveness with enhanced this http URL system leverages the YOLOv8 model to detect both mobile phone and sleep usage,(Ghatge et al., 2024) while facial recognition is achieved through LResNet Occ FC body tracking using YOLO and MTCNN.(Durai et al., 2024) These models work in synergy to provide comprehensive, real-time monitoring, offering insights into student engagement and behavior.(S et al., 2023) The framework is trained on specialized datasets, such as the RMFD dataset for face recognition and a Roboflow dataset for mobile phone detection. The extensive evaluation of the system shows promising results. Sleep detection achieves 97. 42% mAP@50, face recognition achieves 86. 45% validation accuracy and mobile phone detection reach 85. 89% mAP@50. The system is implemented within a core PHP web application and utilizes ESP32-CAM hardware for seamless data capture.(Neto et al., 2024) This integrated approach not only enhances classroom monitoring, but also ensures automatic attendance recording via face recognition as students remain seated in the classroom, offering scalability for diverse educational environments.(Banada,2025)
Abstract (translated)
这项研究提出了一种新型的课堂监控系统，该系统集成了多种模态，包括检测瞌睡、手机使用跟踪和面部识别等，以增强对学生注意力的评估。此系统的开发基于YOLOv8模型来检测手机使用和睡眠状态（Ghatge等人，2024年），同时通过LResNet Occ FC身体追踪结合YOLO和MTCNN实现了面部识别功能（Durai等人，2024年）。这些模型协同工作，提供了全面的实时监控，为了解学生参与度和行为模式提供深入了解。（S等，2023年） 该框架是在专门的数据集上进行训练的，例如用于面部识别的RMFD数据集和用于手机检测的Roboflow数据集。系统经过广泛的评估显示了令人鼓舞的结果：睡眠检测的平均精度（mAP@50）达到了97.42%，面部识别验证准确率为86.45%，而手机检测则达到85.89% mAP@50。 该系统的实现采用了一种核心PHP网络应用程序，并使用ESP32-CAM硬件来无缝采集数据。（Neto等人，2024年）这种综合方法不仅提高了课堂监控的效果，而且还通过面部识别自动记录学生的出勤情况，确保学生在座位上时即被系统捕捉到，从而为各种教育环境提供了可扩展性（Banada, 2025）。
URL
https://arxiv.org/abs/2507.01590
