Student Classroom Behavior Detection based on Improved YOLOv7
Fan Yang
Accurately detecting student behavior in classroom videos can aid in
analyzing their classroom performance and improving teaching effectiveness.
However, the current accuracy rate in behavior detection is low. To address
this challenge, we propose the Student Classroom Behavior Detection method,
based on improved YOLOv7. First, we created the Student Classroom Behavior
dataset (SCB-Dataset), which includes 18.4k labels and 4.2k images, covering
three behaviors: hand raising, reading, and writing. To improve detection
accuracy in crowded scenes, we integrated the biformer attention module and
Wise-IoU into the YOLOv7 network. Finally, experiments were conducted on the
SCB-Dataset, and the model achieved an mAP@0.5 of 79%, resulting in a 1.8%
improvement over previous results. The SCB-Dataset and code are available for
download at: https://github.com/Whiffe/SCB-dataset.