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

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.