Construction and Teaching Evaluation of Students'Classroom Behavior Detection Model Based on YOLOv8 Algorithm
Improving the quality of education is the key to cultivate innovative talents.In the traditional classroom,educators master students'behavior through manual methods such as classroom observation and random questioning.Such methods have problems such as lagging classroom information transmission and feedback.Therefore,based on the YOLOv8 deep learning algorithm,a stu-dent classroom behavior detection model is constructed.By collecting data in the real classroom environment,labeling and format conversion,the processed data is used for training to construct the detection model.The model identifies six kinds of students'class-room behaviors,including writing,sitting,playing with mobile phones,sleeping,standing and lowering head.The research shows that the accuracy of the test results reaches 83.3%,which verifies that this model realizes the automatic identification and classifica-tion of students'classroom behaviors,and assists educators to judge students'learning situation and make teaching decisions.
target detectionYOLOv8students'classroom behavior detectionmodel construction
万方数据
维普
