一个轻量级的混合型号,具有位置保存ViT,用于高效的食物识别
Guorui Sheng1, Weiqing Min2,3, Xiangyi Zhu1
1School of Information and Electrical Engineering, Ludong University, Yantai 264025, China.
Nutrients
|January 23, 2024
概括
一个新的高效混合食品识别网络 (EHFR-Net) 通过结合卷积神经网络 (CNN) 和视觉转换器 (ViT) 来改进移动食品图像识别. 这种方法通过智能手机上的准确,轻量级的人工智能增强了饮食管理.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 智能营养管理依赖于准确的食物图像识别.
- 轻量级的深度学习模型对于饮食跟踪的移动部署至关重要.
- 现有的视觉转换器 (ViT) 在全球信息方面表现出色,但忽视了空间细节.
研究的目的:
- 开发一种新的,轻量级的神经网络,以在移动设备上高效地识别食物图像.
- 解决ViT在保存用于食品识别的空间信息方面的局限性.
- 整合本地和全球特征提取以提高准确性.
主要方法:
- 提出了一个高效的混合食品识别网络 (EHFR-Net),集成CNN和ViT.
- 引入了位置保存视觉变压器 (LP-ViT) 来保留位置信息.
- 使用倒置残余块进行轻量级的本地特征提取,并使用统一的混合块 (HBlock) 进行特征集成.
主要成果:
- 在ETHZ Food-101数据集上,EHFR-Net实现了90.7%的准确性,超过了MobileViTv2的3.5%.
- 该模型与基于ViT的最先进的轻量级网络相比,表现出卓越的性能.
- 实验证实了拟议的LP-ViT和HBlock集成的有效性.
结论:
- EHFR-Net为移动食品图像识别提供了一个高度准确和高效的解决方案.
- 开发的LP-ViT有效地保存空间信息,提高了ViT在这个领域的性能.
- 这项研究通过改进的人工智能驱动的饮食工具来推进智能营养管理.
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