Student classroom behavior recognition based on bidirectional feature fusion

In recent years, deep learning has shown remarkable success in the field of object detection, with the YOLO series models known for their speed and effective recognition. A classroom behavior recognition model based on bidirectional feature fusion is proposed, enhancing the YOLOv5 model by incorporating bidirectional feature fusion, attention mechanisms, and modifying the loss function. Experimental results indicate that the improved model achieves a maximum average recognition accuracy of 97%, demonstrating a notable increase of 4.8% compared to the original model, highlighting a significant improvement in accuracy.