Student Classroom Behavior Detection based on Improved YOLOv7
This work addresses the problem of monitoring student engagement for educators, but it is incremental as it builds on existing YOLOv7 with minor modifications.
The paper tackled low accuracy in student behavior detection from classroom videos by proposing an improved YOLOv7 method, achieving a 1.8% increase in mAP@0.5 to 79% on a new dataset.
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.