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相关实验视频

Updated: May 17, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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基于智能手机的深度学习系统用于检测使用视觉分类技术的拉科巴胺养猪肉.

Hong-Dar Lin1, Mao-Quan He1, Chou-Hsien Lin2

  • 1Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
概括
此摘要是机器生成的。

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一个新的智能手机系统可视识别猪肉的来源并检测ractopamine,确保肉的真实性. 这项技术使消费者能够验证猪肉产品,提高市场透明度和安全性.

科学领域:

  • 食品科学与技术 食品科学与技术
  • 农业经济学 农业经济学
  • 消费者保护 消费者保护

背景情况:

  • 拉克托巴胺是一种β-激动剂,用于增加瘦肉产量,但具有健康风险.
  • 台湾允许进口含有ractopamine的猪肉,导致对标签和错误识别的担忧.
  • 由于高需求和贸易政策,消费者需要可靠的方法来验证猪肉的真实性.

研究的目的:

  • 开发基于智能手机的视觉检测系统,用于分类肉类切片,猪肉来源和ractopamine存在.
  • 为消费者提供在零售环境中实时验证肉类真实性的工具.
  • 提高市场透明度,解决有关进口猪肉的担忧.

主要方法:

  • 使用掩盖技术 (圆和方形) 的三阶段图像处理方法来提取感兴趣的区域 (ROI).
  • 使用MobileNet架构进行分类任务,包括肉类切片,猪肉原产地和拉克托巴胺检测.
  • 进行实验以评估系统的准确性和效率.

主要成果:

  • 在肉类切片识别方面实现了96%的分类率 (CR).
  • 在猪肉原产地分类中获得了79.11%的平均CR和90.25%的F1得分.
  • 达到了平均CR的80.67%和F1得分的80.56%的ractopamine检测.
关键词:
移动网络 (MobileNet) 是一个移动网络.计算机视觉 计算机视觉深度学习是一种深度学习.拉克托巴胺养猪肉的猪肉视觉检查 视觉检查 视觉检查 视觉检查

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Last Updated: May 17, 2025

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结论:

  • 基于智能手机的视觉检测系统有效地验证了肉的真实性和猪肉的来源.
  • 该系统表现出高精度和高效率,特别是在MobileNet模型中.
  • 调查结果支持在猪肉行业加强消费者保护和市场透明度.