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Classroom Behavior Detection Method Based on PLA-YOLO11n.

Hongshuo Zhang1, Guohui Zhou1, Wei He1

  • 1School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.

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|September 13, 2025
PubMed
Summary
This summary is machine-generated.

We developed the PLA-YOLO11n model for accurate classroom behavior detection to enhance teaching effectiveness. This improved model achieved a 3.8% increase in mean average precision (mAP@0.5) on the SCB2 dataset.

Keywords:
AIFILSKAPConvYOLOv11classroom behavior detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Educational Technology

Background:

  • Accurate student behavior detection is crucial for analyzing learning states and improving teaching.
  • Existing models may struggle with detecting small targets or integrating features effectively.

Purpose of the Study:

  • To propose an improved classroom behavior detection model, PLA-YOLO11n, based on the YOLO11 architecture.
  • To enhance the detection of student behaviors and improve overall teaching effectiveness through better analysis.

Main Methods:

  • Developed a novel C3K2_PConv module integrating partial convolution into YOLO11 backbone and neck layers.
  • Incorporated a large-kernel self-attention (LSKA) mechanism for enhanced small-target feature representation.
  • Replaced the SPPF with an attention feature integration module (AIFI) and added a high-resolution detection head.

Main Results:

  • The PLA-YOLO11n model demonstrated superior performance compared to the original YOLO11.
  • Achieved a 3.8% increase in mean average precision (mAP@0.5) on the SCB2 dataset.
  • The enhancements effectively improved the detection of classroom behaviors.

Conclusions:

  • The proposed PLA-YOLO11n model offers a significant advancement in classroom behavior detection.
  • The novel modules and mechanisms contribute to improved accuracy and effectiveness in educational settings.
  • This approach has the potential to positively impact teaching strategies and student learning analysis.