自动化与信息工程2025，Vol.46Issue(5)：47-53,7.DOI:10.12475/aie.20250506
基于计算机视觉的课堂情况分析系统
Classroom Situation Analysis System Based on Computer Vision
摘要
Abstract
Aiming at current problems in universities such as large class sizes,susceptibility to attendance fraud,and teachers'difficulty in promptly understanding each student's learning state,this paper proposes a classroom situation analysis system based on computer vision.The system utilizes the multi-task cascaded convolutional neural network(MTCNN)to detect faces from the real-time video stream of classroom students captured by a camera.It employs the face recognition model to extract facial features for student identity verification.Key point detection algorithms and the YOLOv10 object detection algorithm are used to detect student behaviors like sleeping and using mobile phones in class,respectively.Dynamic thresholds and a state persistence mechanism are introduced to avoid misjudgment of transient behaviors,and the thresholds for detecting sleeping and phone usage behaviors are dynamically adjusted.Test results show that the system can accurately perform functions including automatic classroom attendance,abnormal behavior detection,visualization of attendance results,and automatic generation of classroom situation analysis reports.This helps teachers grasp students'learning states in real-time,allowing them to adjust teaching strategies promptly,thereby improving teaching quality.关键词
计算机视觉/课堂情况分析/人脸识别/异常行为检测/动态阈值/YOLOv10/多任务级联卷积神经网络Key words
computer vision/classroom situation analysis/face recognition/abnormal behavior detection/dynamic threshold/YOLOv10/multi-task cascaded convolutional neural network分类
信息技术与安全科学引用本文复制引用
王永强,罗洪叶,赵贤..基于计算机视觉的课堂情况分析系统[J].自动化与信息工程,2025,46(5):47-53,7.基金项目
贵州电子科技职业学院科研项目基金(2023YJZK002) （2023YJZK002）
