To address the issue of low detection accuracy in smart classroom behavior detection due to dense student populations, mutual occlusion, and large differences in target scales, an improved YOLO11-based classroom behavior detection model, ADU-YOLO11, is proposed. Firstly, the partial convolutional downsampling layers are replaced with the Adown downsampling module to reduce model complexity and minimize the loss of key feature information. Secondly, the dynamic detection head DyHead (Dynamic Head) is adopted, which enhances detection capabilities through scale, spatial, and task-aware attention. Finally, the UIoU (Unified-IoU) loss function is used to optimize the training convergence speed and improve the regression accuracy of the predicted bounding boxes. Experimental results show that ADU-YOLO11 achieves mAP50 and mAP50-95 improvement of 1.1% and 1.6% respectively, along with a precision increase of 2.7% and a recall increase of 0.6% compared to the original YOLO11n on the STBD-08 student-teacher behavior dataset. Moreover, it outperforms other object detection algorithms, demonstrating its effectiveness and superiority in smart classroom behavior detection.
Key words
classroom behavior detection /
YOLO11 /
Adown /
DyHead /
UIoU
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References
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