基于卷积神经网络的智慧教室监控平台设计
Design of Smart Classroom Monitoring Platform Based on Convolutional Neural Networks
庞丁铭1
作者信息
摘要
文章提出一种基于卷积神经网络(Convolutional Neural Networks,CNN)的智慧教室监控平台设计,旨在实现对学生行为的智能化识别.该平台的总体设计架构包括网络摄像头、Wi-Fi、服务器、显示屏及行为识别模型等组件.在实验阶段,通过网络爬虫采集图像数据构建数据集,并采用CNN识别学生行为,包括游戏、说话、睡觉等多个状态.实验结果表明,该方法的识别效果良好,能够准确标识学生的不同状态.
Abstract
This paper proposes a design of a smart classroom monitoring platform based on Convolutional Neural Networks(CNN),aiming to achieve intelligent recognition of student behavior.The overall design architecture of the platform includes the integration of components such as network cameras,Wi-Fi,servers,displays,and behavior recognition models.In the experimental stage,image data is collected through web crawlers to construct a dataset,and CNN is used to identify student behavior,including multiple states such as gaming,speaking,and sleeping.The experimental results show that this method has good recognition performance and can accurately identify different states of students.
关键词
卷积神经网络(CNN)/智慧教室/行为识别Key words
Convolutional Neural Network(CNN)/smart classroom/behavior recognition引用本文复制引用
出版年
2024
NETL
NSTL
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