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DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion

文献类型： 外文期刊

作者： Han, Yuzhe 1 ; Cheng, Qimin 1 ; Wu, Wenjin 2 ; Huang, Ziyang 1 ;

作者机构： 1.Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China

2.Hubei Acad Agr Sci, Inst Agr Prod Proc & Nucl Agr Technol, Wuhan 430064, Peoples R China

关键词： nutrition estimation; deep learning; depth prediction; RGB-D fusion

期刊名称：FOODS （ 影响因子：5.2； 五年影响因子：5.5 ）

ISSN：

年卷期： 2023 年 12 卷 23 期

页码：

收录情况： SCI

摘要： A reasonable and balanced diet is essential for maintaining good health. With advancements in deep learning, an automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition.

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