Efficient Classroom Behavior Detection through End-to End Multiscale Feature Fusion

Classroom behavior detection is essential for smart education. Enhance teaching effectiveness, promote personalized learning, and support holistic student development. Existing methods that rely on single-feature input are susceptible to noise interference, which can lead to misjudgments in classroom behavior detection. This paper proposes an efficient classroom behavior detection through end-to-end multiscale feature fusion. Initially, the original YOLOv7 input module was replaced with a multi-feature information input module to enhance spatial feature utilization efficiency. Then, a feature weight attention mechanism was incorporated to allow the main network to weigh feature information on various scales comprehensively. This approach has improved the accuracy of the algorithm’s identification and reduced the number of network parameters. Finally, including a focal loss function suppressed background interference, improving recognition accuracy. Experimental results demonstrate that the improved algorithm attains 94.1% accuracy in the RizeHand dataset and effectively recognizes student behaviors in real-world scenarios.