HARM-YOLO11n: A Lightweight Real-Time Object Detection Framework for Robust Classroom Behavior Monitoring

Real-time classroom behavior monitoring is an important component of intelligent education systems, yet it remains challenging due to dense student layouts, frequent occlusions, and small-scale behavior targets. To address these challenges, this paper proposes Hierarchical Adaptive Re-parameterized Multi-scale YOLO11n (HARM-YOLO11n), a lightweight object detection framework based on YOLO11n for classroom behavior analysis. The proposed framework integrates structural re-parameterized feature enhancement (SRFE), a spatial-channel adaptive module (SCAM), position-adaptive multi-scale fusion (PAMSF), and IoU-adaptive soft weight (IASW) to improve feature representation, multi-scale interaction, and optimization behavior. SRFE enhances feature extraction through structural re-parameterization, the SCAM reduces redundant feature responses via selective spatial-channel computation, PAMSF performs adaptive multi-scale feature aggregation, and IASW introduces localization-aware sample weighting during training. Experiments on POCO-Dataset, SCB-Dataset3, and STBD-08 show that HARM-YOLO11n achieves improved detection performance compared with representative lightweight detectors while maintaining real-time inference efficiency. Multi-dataset evaluations further indicate stable performance under different data distributions. The results suggest that the proposed framework provides a practical approach for classroom behavior detection in resource-constrained educational environments.