Deep Learning for Student Behavior Detection in Smart Classroom Environments

The ongoing integration of information technology in education has rendered the monitoring of student behavior in smart classrooms essential for improving teaching quality and student engagement. Classroom environments frequently provide many problems, such as heterogeneous student behaviors, significant obstructions, loss of intricate details, and complications in recognizing diminutive targets. These limitations lead to current approaches remaining inadequate in accuracy and stability. This paper enhances YOLOv11 with the following improvements: developed the CSP-PMSA module to enhance contextual modeling in complex backgrounds, developed a scale-aware head (SAH) to improve the perception and localization of small targets via channel unification and scale adaptation, and introduced a Multi-Head Self-Attention (MHSA) mechanism to model global dependencies and positional bias across various subspaces, thereby enhancing the discrimination of visually analogous behaviors. The experimental findings indicate that in intricate classroom settings, the model attains mAP@50 and mAP@50–95 scores of 91.6% and 75.7%, respectively. This indicates enhancements of 2.7% and 2.6% compared to YOLOv11, and 4.6% and 3.6% relative to DETR, demonstrating remarkable detection precision and dependability. Additionally, the model was implemented on the Jetson Orin Nano platform, confirming its viability for real-time detection on edge devices and offering substantial assistance for practical implementations in smart classrooms.