一个强大的检测和描述框架用于幼儿教育中的对象识别
1Preschool Education Institute, Zhengzhou Preschool Education College, Zhengzhou, China.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了学前教育的深度学习框架,通过对象识别来增强认知发展. 该系统在识别和描述对象方面实现了高精度,支持增强现实应用.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 教育技术的教育技术
背景情况:
- 学前教育对于认知和情感发展至关重要.
- 早期识别形状,颜色和字母构建了一个坚实的学术基础.
- 空间推理和解决问题的技能是从幼儿时期的物体识别中产生的.
研究的目的:
- 介绍一种使用深度学习在学前教育中用于嵌套对象识别的认知增强框架.
- 通过先进的人工智能模型改善幼儿学习.
- 开发一个精确的对象识别和语义描述系统.
主要方法:
- 利用深度学习模型:你只看一次 (YOLOv8) 和视觉几何组 (VGG16) 进行对象检测.
- 使用长期短期记忆 (LSTM) 神经网络进行语义描述.
- 在Google Colab中使用Ultralytics,PyTorch和OpenCV实现了该框架.
- 在定制数据集 (PreEduDS) 上训练并测试模型.
主要成果:
- 实现了对象识别的94.4%准确度.
- 实现了96.5%的准确度,用于预测精确的语义文本描述.
- 认知增强框架的广泛适用性得到证明.
结论:
- 拟议的框架有效地增强了学前教育和培训.
- 该系统非常适合与增强现实 (AR) 应用程序集成.
- 深度学习显著改善了年轻学习者的认知发展工具.
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