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相关实验视频

Updated: Jun 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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可食品的自动检测和识别系统使用先进的深度学习模型.

Yogesh Kumar1, Apeksha Koul2, Kamini3

  • 1Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India.

Scientific reports
|March 20, 2024
PubMed
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查看所有相关文章
此摘要是机器生成的。

这项研究引入了一种新的深度学习方法,通过分析食声来识别食物. 该研究使用各种深度学习模型证明了食品识别的高准确性,展示了饮食应用的潜力.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 通过食声识别食物对于过敏管理,饮食限制和文化理解至关重要.
  • 现有的方法在仅基于听觉线索的基础上区分食品时缺乏可靠的准确性.

研究的目的:

  • 开发和评估一种新的深度学习方法,用于使用食声准确识别食物.
  • 探索各种深度学习架构的有效性和基于音频的食品分类特征提取技术.

主要方法:

  • 在20种食品中收集和分析了1200个标记的音频文件.
  • 采用信号处理技术 (光谱,MFCC) 来进行特征提取.
  • 训练并混合的深度学习模型包括GRU,LSTM,InceptionResNetV2,CNN,双向LSTM+GRU,RNN+双向LSTM和RNN+双向GRU.

主要成果:

  • 门式循环单元 (GRU) 模型实现了最高的准确率99.28%.
  • 像双向LSTM+GRU和RNN+双向LSTM这样的混合型号表现出强的性能,分别准确率为97.7%和97.45%.
  • 所有评估的深度学习模型都显示出在将特定的声音模式与食物类联系起来方面具有显著的潜力.

结论:

关键词:
音频信号处理 音频信号处理定制化的卷积神经网络深度学习是一种深度学习.吃东西听起来很有声音.食品的标识 食品的标识梅尔频率的塞普斯特拉尔系数谱图谱图谱图谱图谱图谱图谱图谱图谱图谱

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  • 深度学习模型是非常有效的识别食品基于他们的吃声.
  • 拟议的方法为需要自动识别食品的应用提供了一个有希望的方法.
  • 进一步的研究可以探索更大的数据集和各种食物类别,以提高概括性.