Student behavior detection model based on multilevel residual networks and hybrid attention mechanisms

Abstract

Accurately analyzing student behaviors allows for better evaluation of student engagement, which in turn can improve teaching quality. To address challenges such as multi-scale scenes, occluded targets, and subtle fine features in classroom environments, while also considering model implementability, we propose an efficient student behavior detection model, RSAY. This model leverages multi-scale information extraction and a hybrid attention mechanism to support teaching. Both the backbone and feature fusion networks of the model integrate our designed Rep_SC_Atten module, which incorporates our novel multi-level residual network architecture and a lightweight hybrid attention mechanism. This hybrid architecture enhances the model’s sensitivity and ability to extract multi-scale information, while ensuring effective extraction of fine-grained features via the attention mechanism. Additionally, the DDetect strategy is introduced in the detection head to reduce model size without sacrificing accuracy. We evaluated our model using the SCB-Dataset and a custom student behavior dataset, demonstrating a 6.3% improvement in accuracy over the baseline model.