Research on student classroom behavior recognition based on YOLOv5 algorithm
With the popularization of smart classrooms and educational data mining,our project aims to create an automatic detection and analysis method for student learning data in a smart classroom.Based on the YOLOv5 model,two types of learning data are implemented:classmate counting and learning behavior recognition.This model recognizes seven types of student class-room behaviors,which assists teachers in judging student learning situations and making pedagogical decisions,including writing with head down,reading with head down,listening with head up,turning,raising hand,standing,and group discussion.Research shows that the detection accuracy of the model reaches 97.921%.
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