A lightweight YOLOv11n-based framework for student behavior detection in smart classroom environments

Real-time recognition of student behaviors in intelligent classrooms presents significant challenges due to high scene complexity, frequent occlusion, and the need for deployment on resource-constrained edge devices. This paper introduces YOLOv11n-SFS, a lightweight and high-efficiency object detection framework designed for embedded educational monitoring systems. The proposed approach incorporates three key architectural enhancements: (1) StarNet, a parameter-efficient backbone utilizing element-wise multiplication and depthwise separable convolutions; (2) C3k2-Faster, an optimized neck module based on Partial Convolution for improved multi-scale feature fusion; and (3) SCLD (Shared Conv Light Detector), a compact detection head using Group Normalization and shared convolution to ensure stable performance under limited batch sizes. Experimental results on a curated student behavior dataset demonstrate that YOLOv11n-SFS achieves 95.68% [email protected] with only 1.63 M parameters and a model size of 3.4 MB, while delivering 35.66 FPS on CPU inference. The model demonstrates strong deployment potential in practical classroom systems, offering a practical balance between accuracy, computational cost, and hardware efficiency for embedded AI applications in educational environments.