YOLOv8n_BT: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv8n

Abstract

Classroom learning behavior recognition can provide effective technical support for teaching and learning. However, in natural classroom teaching scenarios, classroom learning behaviors are often missed or falsely detected due to character occlusion and the small object. To tackle the above issues, this study proposed an improved classroom learning behavior recognition algorithm (YOLOv8n_BT) based on YOLOv8n. On the one hand, for the occlusion problem of classroom learning behaviors, this study incorporated the BRA into the Backbone to better capture feature information; on the other hand, for the small object problem of classroom learning behaviors for back-row-students, this study expanded a Tiny Object Detection Layer (TODL) to detect small targets better. Experiments show that the BRA and the TODL can significantly improve the model performance. The YOLOv8n_BT model, which incorporated both the BRA and the TODL into the YOLOv8n(baseline) model simultaneously, has the most significant performance improvement. Compared with the YOLOv8n(baseline), the YOLOv8n_BT model improved by 3.0%, 6.7%, 5.0%, 3.6%, and 9.0% on P, R, F1, mAP50, and mAP50-90, respectively. The detection performance of YOLOv8n_BT also outperforms other state-of-the-arts.