DSS-YOLOv7: An Enhanced YOLOv7-Based Algorithm for Classroom Behavior Detection

This paper addresses the challenges of detecting students' hand-raising behavior in classrooms, which include small target sizes, complex student features, and distracting classroom background information. We propose an improved YOLOv7-based algorithm, Deformable Simulated-SE Enhanced YOLOv7 (DSS-YOLOv7), specifically designed for classroom behavior detection. The proposed algorithm introduces several enhancements: it optimizes the Feature Pyramid Networks (FPN) structure by incorporating Deformable Convolutions (DCNv2), which effectively capture variations in students' behaviors, thereby improving detection accuracy and robustness. Additionally, the Squeeze-and-Excitation (SE) attention mechanism is introduced to enhance the model's focus on important features and improve recognition accuracy. The integration of the Simple Attention Module (SimAM) helps to reduce model complexity and minimize confusion caused by background information. Furthermore, data augmentation techniques are applied to the dataset, significantly enhancing the model's feature learning capabilities. Experimental results demonstrate that the DSS-YOLOv7 model achieves a 2.6% improvement in mean Average Precision ([email protected]), reaching 85%, compared to YOLOv7 on the SCB-Dataset. The DSS-YOLOv7 model exhibits superior performance in detecting classroom behaviors under complex conditions.