改进YOLOv8s模型，精准识别教室中学生行为。
Student Classroom Behavior Recognition Based on Improved YOLOv8s
- 引入SPPF-LSKA增强上下文特征提取能力。
- 在复杂教室场景下mAP50提升1.8%，mAP50-95提升2.1%。
- 适合需要高精度识别小目标和遮挡场景的应用。
在课堂教学中，学生行为可反映其学习状态与参与度，对教学质量分析具有重要意义。针对真实教室场景中存在的学生目标密集、小物体多、频繁遮挡及类别分布不均等问题，本文提出基于YOLOv8s的改进模型ALC-YOLOv8s。该模型引入SPPF-LSKA以增强上下文特征提取，采用CFC-CRB与SFC-G2优化多尺度特征融合，并结合ATFLoss提升对少数类与难样本的学习能力。实验结果表明，相比基线模型，改进模型在mAP50上提升1.8%，在mAP50-95上提升2.1%。相较于多种主流检测方法，所提模型能有效满足复杂教室场景下的自动学生行为识别需求。
原文摘要 · Abstract (English)
In classroom teaching, student behavior can reflect their learning state and classroom participation, which is of great significance for teaching quality analysis. To address the problems of dense student targets, numerous small objects, frequent occlusions, and imbalanced class distribution in real classroom scenes, this paper proposes an improved student classroom behavior recognition model named ALC-YOLOv8s based on YOLOv8s. The model introduces SPPF-LSKA to enhance contextual feature extraction, employs CFC-CRB and SFC-G2 to optimize multi-scale feature fusion, and incorporates ATFLoss to improve the learning ability for minority classes and hard samples. Experimental results show that compared with the baseline model, the improved model achieves increases of 1.8% in mAP50 and 2.1% in mAP50-95. Compared with several mainstream detection methods, the proposed model can well meet the requirements of automatic student behavior recognition in complex classroom scenarios.
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