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Multi-Spectral Food Classification and Caloric Estimation Using Predicted Images.

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使用卷积神经网络进行多谱食品分类和热量估计.

Ki-Seung Lee1

  • 1Department of Electrical and Electronic Engineering, Konkuk University, 1 Hwayang-dong, Gwangjin-gu, Seoul 05029, Republic of Korea.

Foods (Basel, Switzerland)
|September 9, 2023
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概括
此摘要是机器生成的。

这项研究引入了一种使用卷积神经网络 (CNN) 的多光谱成像技术,用于自动识别食物和估计热量,有助于预防肥胖. 该方法显著提高了识别食品类型及其热量含量的准确性.

关键词:
卷积神经网络是一种卷积神经网络.数据融合数据融合饮食评估 饮食评估食品分析 食品分析多光谱成像技术的使用.进行非侵入性分析.

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科学领域:

  • 计算机视觉 计算机视觉
  • 食品科学 食品科学 食品科学
  • 生物医学工程 生物医学工程

背景情况:

  • 准确的食品记录对于管理代谢疾病和肥胖至关重要.
  • 当前的方法往往需要大量的用户干预,限制了遵守和准确性.
  • 需要用于食品类型和热量含量分析的自动化系统.

研究的目的:

  • 开发一种自动化系统,用于识别食品类型,并使用多光谱成像来估计热量含量.
  • 通过将RGB图像与紫外线 (UV),可见光和近红外线 (NIR) 光谱区域的图像融合,提高食品分析的准确性.
  • 优化波长组合,以提高分类和热量估计性能.

主要方法:

  • 利用了UV,可见和NIR区域 (385-1020nm) 的多光谱图像与RGB图像相结合.
  • 采用卷积神经网络 (CNN) 来对食品进行分类和估计热量含量.
  • 在101种食物类型的10,909张图像上训练CNN,并在3,636张图像上验证.
  • 应用了一种分片选择方法来确定最佳的波长组合.

主要成果:

  • 食品分类的准确性从88.9%增加到97.1%随着RGB.NIR图像的添加.
  • 热量估计的平均绝对百分比误差 (MAPE) 从41.97%降至18.97%.
  • 获得了最高的99.81%的食品类型分类准确率 (使用19张图像) 和最低的10.56%的MAPE (使用14张图像).

结论:

  • 多光谱成像,特别是结合紫外线和红外线波段,显著提高了自动食品分类和热量估计.
  • 开发的基于CNN的系统为饮食监测提供了一种有希望的低干预方法.
  • 这项技术在通过改善饮食跟踪来预防肥胖和代谢疾病方面具有潜在的应用.