Food Calorie Estimation System Based on Semantic Segmentation Network

被引:0
作者
Kong, Xiang-Yong [1 ]
Sun, Xiao-Han [1 ]
Wang, Yu-Ze [1 ]
Peng, Rui-Yang [1 ]
Li, Xin-Yue [1 ]
Yang, Yi-Heng [1 ]
Lv, Ying-Rui [2 ]
Tseng, Shih-Pang [3 ,4 ]
机构
[1] Univ Shanghai Sci & Technol, Sch Hlth Sci & Engn, 516 Jungong Rd, Shanghai 200093, Peoples R China
[2] Univ Shanghai Sci & Technol, Business Sch, 516 Jungong Rd, Shanghai 200093, Peoples R China
[3] Changzhou Coll Informat Technol, Sch Software & Big Data, 22 Mingxin Middle Rd, Changzhou 213164, Peoples R China
[4] Sanda Univ, Sch Informat Sci & Technol, 2727 Jinhai Rd, Shanghai 201209, Peoples R China
关键词
food calorie estimation; semantic segmentation; pattern recognition; deep learning;
D O I
10.18494/SAM4061
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
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%.
引用
收藏
页码:2013 / 2033
页数:21
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