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相关实验视频

Updated: Jun 4, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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使用计算机视觉和人工智能算法自动化食品重量和内容估计.

Bryan Gonzalez1, Gonzalo Garcia2, Sergio A Velastin3,4

  • 1Escuela de Ingenieria Electrica, Pontificia Universidad Catolica de Valparaıso, Valparaíso 2340025, Chile.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
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这项研究使用人工智能和计算机视觉进行自动化食品服务分析,准确计算菜,并高精度估计餐厅的份量大小.

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 食品服务管理管理.

背景情况:

  • 食品的自动定量对于有效的食品分配至关重要.
  • 传统的盘子计数和部分估计方法是劳动密集型的,容易出错.

研究的目的:

  • 开发和验证一个人工智能驱动的系统来量化食品分发服务.
  • 准确计算菜,识别食物含量,并估计餐厅环境中的份量大小.

主要方法:

  • 使用YOLO (You Only Look Once) 架构进行对象检测和图像分析.
  • 采用RGB和深度摄像头来捕捉托盘交付过程和测量食品体积.
  • 根据体积测量,开发密度模型来估计特定食品的重量.

主要成果:

  • 在对象检测任务中达到0.873的平均平均精度 (mAP).
  • 在重量估计中显示出较低的误差率:大米为5.07%,肉为3.75%.
  • 在现实世界的餐厅环境中验证了系统的可行性和准确性.

结论:

  • 拟议的计算机视觉和人工智能系统为食品服务量化提供了高度准确和高效的解决方案.
关键词:
人工智能的人工智能是人工智能.计算机视觉 计算机视觉深度学习是一种深度学习.食品重量估计 食品重量估计

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  • 这项技术有可能显著改善库存管理,减少浪费和食品分发的运营效率.