智能骨科:使用YOLOv5和CNN架构进行多种长骨检测和分类的AI两级系统,用于兽医解剖教育和法医应用
1Department of Anatomy, Faculty of Veterinary Medicine, Erciyes University, 38039 Kayseri, Türkiye.
Veterinary sciences
|August 28, 2025
概括
这项研究开发了一种使用YOLO和CNN的AI系统,用于精确的骨检测和兽医和法医应用中的物种分类. 该模型达到97.6%的准确性,并提供离线移动功能以安全处理数据.
科学领域:
- 兽医解剖学
- 法医科学
- 人工智能
背景情况:
- 在兽医解剖学,法医和考古学中, 准确识别骨类和物种至关重要.
- 现有的方法可能缺乏各种现场应用所需的速度,准确性或可访问性.
研究的目的:
- 开发和验证人工智能驱动的自动骨检测和分类系统.
- 加强人工智能在兽医教育,法医调查和考古研究中的应用.
主要方法:
- 使用YOLO算法检测牛,马和狗的骨头.
- 使用卷积神经网络 (CNN) 进行骨类和物种分类.
- 在26148张图像的数据集上训练模型.
主要成果:
- 在骨检测和分类方面达到高达97.6%的准确率.
- 开发了一个可在移动设备上部署的系统,并作为一个离线的封闭模型来增强数据安全性.
- 集成了一个虚拟助手用于用户交互 (文本/语音) 和PDF输出.
结论:
- 人工智能系统在兽医解剖学教育,法医,考古学和犯罪现场调查方面显示出巨大的数字化潜力.
- 该系统的准确性,安全功能和用户友好的界面支持其在关键领域的实际应用.
- 这项研究为人工智能驱动的法医和解剖学分析的未来进展奠定了基础.
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