Food Calorie and Nutrition Analysis System based on Mask R-CNN

Meng Lin Chiang, Chia An Wu, Jian Kai Feng, Chiung Yao Fang, Sei Wang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Over the past few decades, obesity has become a serious problem. Obesity is associated with many of the leading causes of death, such as chronic diseases including diabetes, heart disease, stroke, and cancer. The most effective way to prevent obesity is through food intake control, which involves understanding food ingestion, including the nutrients and calories of each meal. To assist with this issue, this study develops a food calorie and nutrition system that can analyze the composition of a food based on a provided image. Further, we introduce a newly collected dataset, Ville Cafe, for food recognition. This dataset contains 16 categories with 35,842 images, including salad, fruit, toast, egg, sausage, chicken cutlet, bacon, French toast, omelet, hash browns, pancake, ham, patty, sandwich, French fries, and hamburger. The system is based on a Mask Region-based Convolutional Neural Network (R-CNN) with a union postprocessing, which modifies the extracted bounding boxes and masks, without the non-maximum suppression (NMS), to provide a better result in both analytics and visualization. The recognition accuracy for the combination of Ville Cafe and the Food-256 Datasets was 99.86%, and the intersection over union (IoU) was 97.17%. The food weight estimation experiment included eight classes: salad, fruit, toast, sausage, bacon, ham, patty, and French fries. These classes respectively had 40, 40, 44, 40, 41, 49, 26, and 40 data points, adding up to 320 data points for the linear regression model. In the experimental results, the average absolute error was 8.22, and the average relative error was 0.13.

Original languageEnglish
Title of host publication2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1721-1728
Number of pages8
ISBN (Electronic)9781728147437
DOIs
Publication statusPublished - 2019 Dec
Event5th IEEE International Conference on Computer and Communications, ICCC 2019 - Chengdu, China
Duration: 2019 Dec 62019 Dec 9

Publication series

Name2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019

Conference

Conference5th IEEE International Conference on Computer and Communications, ICCC 2019
CountryChina
CityChengdu
Period19/12/619/12/9

    Fingerprint

Keywords

  • food calorie analysis
  • food image recognition
  • food nutrition analysis
  • instance segmentation
  • Mask R-CNN

ASJC Scopus subject areas

  • Information Systems and Management
  • Modelling and Simulation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems

Cite this

Chiang, M. L., Wu, C. A., Feng, J. K., Fang, C. Y., & Chen, S. W. (2019). Food Calorie and Nutrition Analysis System based on Mask R-CNN. In 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019 (pp. 1721-1728). [9064257] (2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCC47050.2019.9064257