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Updated: May 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

使用混合变压器模型和图像预处理技术提高食品识别准确度.

B N Jagadesh¹, Srihari Varma Mantena², Asha P Sathe³

¹School of Computer Science and Engineering, VIT-AP University, Vijayawada, India.

Scientific reports

|February 15, 2025

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使用条款与条件

此摘要是机器生成的。

这项研究引入了一种先进的计算机视觉系统,用于准确识别食物,这对营养研究至关重要. 混合变压器模型达到99.83%的准确性,增强饮食监测和个性化的营养.

科学领域:

计算机视觉和机器学习
营养科学和生物信息学

背景情况:

准确的食物识别对于营养研究和饮食监测至关重要.
现有的方法经常与数据集噪声和复杂的视觉特征作斗争.

研究的目的:

开发一个强大的,高度准确的连续食品识别系统.
为了提高性能,利用先进的计算机视觉和混合变压器模型.

主要方法:

利用相互引导图像过 (MuGIF) 进行数据集增强.
雇员视觉几何组 (VGG) 用于特征提取.
开发了一种混合变压器模型 (视觉变压器和旋转变压器),使用改进的离散蝙蝠算法 (IDBA) 进行了优化.

主要成果:

达到 99.83% 的卓越分类准确率.
与现有的食品识别方法相比,显著提高了性能.

结论:

混合变压器架构与先进的预处理相结合,为食品识别提供了更高的准确性和效率.
拟议的系统在饮食监测和个性化营养建议方面具有强大的实际应用潜力.

关键词:

改进了离散蝙蝠算法.相互引导的图像过.斯温变压器是什么意思视觉变压器视觉变压器视觉几何组视觉几何组

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