StudentNet: an edge-deployable approach for student behavior detection in smart classrooms

Accurately identifying student behavior in classroom environments is essential for the development of intelligent education systems. However, existing methods often exhibit high false-positive and false-negative rates in complex backgrounds or densely populated scenes, which degrades detection accuracy.

To overcome these limitations, we propose StudentNet, a novel framework incorporating the Behavior-Enhanced Fusion Super-Resolution Network (BEF-SRNet), the Direction-Sensitive Adaptive Convolution module (DSAC), and the Adaptive Spatial Feature Fusion (ASFF) strategy. BEF-SRNet separates regions of interest from background clutter to improve the discriminability of behavioral features. DSAC uses multi-directional branches and channel attention to extract structural behavioral cues while suppressing noise. ASFF enhances scale-invariant feature representation to improve semantic consistency and detection performance.

Experiments on SCB-Dataset3 show that StudentNet improves mAP@50 and F1-score by 6.3 and 6.4 percentage points, respectively, compared with the YOLOv8n baseline. Deployment on the Core-3399Pro-JD4 edge platform further shows that the INT8-quantized model achieves 38.4 FPS with acceptable accuracy degradation.

These results indicate that StudentNet improves student behavior detection in complex classroom scenes and is suitable for real-time smart classroom applications on edge devices.