| 张立军,曹江涛,姬晓飞,王天昊.基于深度学习的学生课堂学习状态监测系统设计[J].电子测量与仪器学报,2024,38(4):37-45 |
| 基于深度学习的学生课堂学习状态监测系统设计 |
| Design of classroom learning state monitoring system forstudents based on deep learning |
| |
| DOI： |
| 中文关键词: 人脸识别 计算机视觉 疲劳检测 出勤检测 课堂监测 |
| 英文关键词:face recognition computer vision fatigue detection attendance detection classroom monitoring |
| 基金项目:国家自然科学基金(61673199)、辽宁省科技公益研究基金(2016002006)项目资助 |
|
|
| Author | Institution |
| Zhang Lijun | School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China |
| Cao Jiangtao | School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China |
| Ji Xiaofei | School of Automation, Shenyang Aerospace University, Shenyang, Liaoning 110136, China |
| Wang Tianhao | School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China |
|
| 摘要点击次数: 99 |
| 全文下载次数: 2974 |
| 中文摘要: |
| 现阶段，学生课堂学习状态的研究大多集中在单人的在线监测，对于多人且环境复杂的线下课堂的监测还处于探索阶段。该研究针对线下教育设计了学生课堂学习状态监测系统，对学生课堂出勤情况及学生面部出现的疲劳状态进行实时监测。首先，使用DSFD人脸检测算法结合ResNet深度残差网络对学生进行人脸识别，记录学生出勤情况；然后，使用ERT回归树集合算法结合头部姿态估计对打哈欠和低头瞌睡的疲劳行为进行检测；再使用加入CBAM模块改进的YOLOv5目标检测算法对学生闭眼行为进行检测；最后，形成一套完整的集合出勤、疲劳检测的学生课堂学习状态监测系统。该系统在实际课堂的测试环境下，可以准确的对学生的出勤进行统计，并且可以实时的监测学生面部出现的打哈欠、低头瞌睡、闭眼的疲劳状态，检测的准确率均超过90%，检测速度约为14.1 fps，证明该系统具有重要的使用价值。 |
| 英文摘要: |
| At present, most of the research on students’ classroom learning status focus on single-person online monitoring, and the monitoring of offline classroom with multiple students and complex environment is still in the exploratory stage. A monitoring system for students’ classroom learning status was designed for offline education to monitor students’ classroom attendance and fatigue state of students’ faces in real time. First, DSFD face detection algorithm combined with ResNet deep residual network was used to recognize students’ faces and record students’ attendance. Then, ERT regression tree set algorithm combined with head pose estimation was used to detect the fatigue behavior of yawning and drowsiness. Then, the improved YOLOv5 object detection algorithm added CBAM module was used to detect students’ closed eyes behavior. Finally, a complete set of integrated attendance, fatigue detection of student classroom learning state monitoring system is formed. In the actual classroom test environment, the system can accurately calculate the students’ attendance, and can real-time monitor the fatigue state of yawning, lower head and closed eyes on the face of the students. The detection accuracy rate is more than 90%, and the detection speed is about 14.1 fps, which proves that the system has important use value. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
