Enhancing Classroom Behavior Recognition with Lightweight Multi-Scale Feature Fusion

Classroom behavior recognition is a hot research topic, which plays a vital role in assessing and improving the quality of classroom teaching. However, existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures, and inconsistent objects. To address this challenge, we proposed an effective, lightweight object detector method called the RFNet model (YOLO-FR). The YOLO-FR is a lightweight and effective model. Specifically, for efficient multi-scale feature extraction, effective feature pyramid shared convolutional (FPSC) was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone. Secondly, to address the problem of multi-scale variability in the scene, we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network (RGCSPELAN) to improve the network performance further and reduce the amount of computation and the number of parameters. In addition, by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset. Experimental results indicate that, compared to the baseline model, the RFNet model has increased mean accuracy precision (mAP@50) from 69.6% to 71.0% on the SCB dataset3 and from 91.8% to 93.1% on the STBD-08 dataset. The RFNet approach has effectiveness precision at 68.6%, surpassing the baseline method (YOLOv11) at 3.3% and archieve the minimal size (4.9 M) on the SCB dataset3. Finally, comparing it with other algorithms, it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.