复杂场景中的行为特征的深度视觉计算和嵌入式对象识别应用程序
Libo Zong1,2,3, Jiandong Fang1,2,3
1College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究使用人工智能驱动的面部识别和行为检测来分析学生的课堂参与度. 嵌入式应用程序帮助教师通过识别特定的学生需求和行为来个性化教学,以提高教学质量.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 现有的研究往往忽视了学生在课堂分析中的个人需求.
- 个性化的教学支持对于提高教学质量至关重要.
- 人工智能和大数据为详细的课堂评估提供了潜力.
研究的目的:
- 开发一种嵌入式应用程序,用于分析学生在课堂上的个体行为.
- 为了弥补个性化教学支持的差距.
- 通过数据驱动的洞察力提高教学质量.
主要方法:
- 实现了一个使用Insightface进行面部识别和YOLOv5用于目标检测的嵌入式应用程序.
- 创建了课堂面部数据集,并训练了用于准确识别学生的算法.
- 相关的面部和身体区域检测,用于精确的学生行为分析.
主要成果:
- 对各种行动的行为检测精度超过0.67.
- 在面部识别方面实现了41.5%的平均错误检测率.
- 该系统可靠地检测学生的行为,并识别个人.
结论:
- 嵌入式应用程序提供了有价值的数据,以了解学生的个体行为.
- 教师可以利用这些见解来更好地管理课堂和提供个性化的支持.
- 这项技术是提高整体教学质量和学生参与度的关键.
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