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YOLOv11-GLIDE: An Improved YOLOv11n Student Behavior Detection Algorithm Based on Scale-Based Dynamic Loss and
Haiyan Wang1, Guiyuan Gao1, Wei Zhang1
1College of Computer Science and Technology, Changchun University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|November 27, 2025
Summary
This study introduces YOLOv11-GLIDE, an improved algorithm for student classroom behavior recognition. It enhances accuracy and efficiency, offering valuable data for intelligent education systems.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Educational Technology
Background:
- Student classroom behavior recognition is crucial for intelligent education systems.
- Real-time analysis supports teaching evaluation, classroom management, and personalized instruction.
- Existing methods face challenges with low accuracy and occlusion.
Purpose of the Study:
- To develop an improved algorithm for accurate and efficient student behavior detection in classrooms.
- To address limitations of existing methods, specifically low detection accuracy and occlusion.
Main Methods:
- Proposed YOLOv11-GLIDE algorithm, an enhancement of YOLOv11n.
- Incorporated Channel Prior Convolutional Attention (CPCA) for integrated feature extraction.
- Implemented scale-based dynamic loss (SD Loss) and Sparse Depthwise Convolution (SPD-Conv).
Main Results:
- YOLOv11-GLIDE demonstrated improved accuracy (mAP@0.5 +2.5%, mAP@0.5-0.95 +7.6%) compared to YOLOv11n.
- Achieved a lightweight design with reduced parameters (-9.4%) and GFLOPS (-11.1%).
- Reached a detection speed of 127.9 FPS, suitable for real-time monitoring.
Conclusions:
- YOLOv11-GLIDE offers a superior balance of accuracy and efficiency for student behavior recognition.
- The algorithm meets practical requirements for embedded classroom monitoring systems.
- This advancement contributes to data-driven educational decision-making and personalized learning.
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