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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 35, Number 6(3) (2023)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 2013-2033
S&M3304 Research Paper of Special Issue https://doi.org/10.18494/SAM4061 Published: June 30, 2023 Food Calorie Estimation System Based on Semantic Segmentation Network [PDF] Xiang-Yong Kong, Xiao-Han Sun, Yu-Ze Wang, Rui-Yang Peng, Xin-Yue Li, Yi-Heng Yang, Ying-Rui Lv, and Shih-Pang Tseng, (Received July 31, 2022; Accepted May 19, 2023) Keywords: food calorie estimation, semantic segmentation, pattern recognition, deep learning
The food calorie estimation system (FCES) is designed to record dietary information for diabetic patients to monitor their dietary intake to estimate the number of calories they are consuming. Deep learning technologies have recently been used for FCESs. In this work, we use the neural network for the pattern recognition of food images to calculate the number of calories. In contrast to the traditional convolutional neural network, we build a semantic segmentation network model based on SegNet + MobileNet to segment the food images and extract the area feature of food images. By determining the corresponding relationship between the area feature of the food image and the food calorie value, the number of calories in the food can be estimated and realized. The experimental results show that the accuracy of food recognition reached 97.82% and that of calorie estimation was above 84.95%.
Corresponding author: Shih-Pang Tseng  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Xiang-Yong Kong, Xiao-Han Sun, Yu-Ze Wang, Rui-Yang Peng, Xin-Yue Li, Yi-Heng Yang, Ying-Rui Lv, and Shih-Pang Tseng,, Food Calorie Estimation System Based on Semantic Segmentation Network, Sens. Mater., Vol. 35, No. 6, 2023, p. 2013-2033. |
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