一种基于WAD-YOLOv8的方法,用于在课堂上检测学生行为
Lisu Han1, Xuejian Ma2, Mengna Dai3
1School of Anesthesiology, Shandong Second Medical University, Weifang, 261053, China. hanlisu783@163.com.
Scientific reports
|March 21, 2025
概括
这项研究引入了用于课堂行为检测的增强YOLOv8模型,提高了多尺度和封闭目标的准确性. 先进的模型提供实时性能,有助于有效的学生监控.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的对象检测模型在复杂的环境中面临局限性,如教室,在受限的受体场和不足的多尺度特征学习中扎.
- 现有骨干中固定的卷积内核阻碍了捕获各种功能的能力,这些功能对于准确的行为分析至关重要.
研究的目的:
- 增强YOLOv8模型,以改善复杂的课堂行为检测.
- 克服标准对象检测架构固有的受感场和多尺度特征学习方面的局限性.
主要方法:
- 介绍了一种基于卷积注意力的跨阶段部分网络 (CA-C2f) 模块,用于全面的感受场融合和调整.
- 集成基于注意力的2D位置编码-多头注意 (2DPE-MHA) 模块,以捕获远程依赖.
- 纳入一个动态抽样因子 (Dysample),以关注详细的地区,防止信息丢失.
主要成果:
- 与现有方法相比,增强的YOLOv8模型在基准数据集 (SCB,SCB2,SCB-S,SCB-U) 上表现优越.
- 在平均精度 (mAP@0.5) 中实现了显著的改进,从2.2%到18.7%的平均精度和mAP@0.5:0.95从2.3%到14.8%的平均精度.
- 保持实时推断速度,优于其他对象检测模型.
结论:
- 拟议的模型有效地解决了复杂的课堂行为检测方面的挑战,特别是针对多尺度,封闭和小目标.
- 集成CA-C2f,2DPE-MHA和Dysample模块显著提高了检测准确度和细节保存.
- 该模型提供了一个实用的解决方案,用于实时监控课堂行为,协助教育工作者和管理人员.
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