Deep Learning Food Detection and Calories Counter

Monitoring and managing dietary intake plays a vital role in maintaining a healthy lifestyle. However, accurately tracking food consumption and estimating calorie intake can be challenging. This paper presents a deep learning-based approach of You Only Look Once version 4 (YOLOv4) model for food det...

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Published in:2023 IEEE International Conference on Computing, ICOCO 2023
Main Author: Ibrahim S.; Arif Aiman Wan Hasnan W.M.; Md Ghani N.A.; Fariza Abu Samah K.A.; Abu Mangshor N.N.; Ahmad Fadzil A.F.; Janor R.M.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184850789&doi=10.1109%2fICOCO59262.2023.10397620&partnerID=40&md5=e7d006ee4d70d17cbca97f8a18c25b07
id 2-s2.0-85184850789
spelling 2-s2.0-85184850789
Ibrahim S.; Arif Aiman Wan Hasnan W.M.; Md Ghani N.A.; Fariza Abu Samah K.A.; Abu Mangshor N.N.; Ahmad Fadzil A.F.; Janor R.M.
Deep Learning Food Detection and Calories Counter
2023
2023 IEEE International Conference on Computing, ICOCO 2023


10.1109/ICOCO59262.2023.10397620
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184850789&doi=10.1109%2fICOCO59262.2023.10397620&partnerID=40&md5=e7d006ee4d70d17cbca97f8a18c25b07
Monitoring and managing dietary intake plays a vital role in maintaining a healthy lifestyle. However, accurately tracking food consumption and estimating calorie intake can be challenging. This paper presents a deep learning-based approach of You Only Look Once version 4 (YOLOv4) model for food detection and calorie counting. Leveraging the power of deep neural networks, the proposed study automatically detects and classifies food items from images and provides real-time estimation of their calorie content. Seven classes of food which are fried noodles, fried rice, kaya toast, nasi lemak, roti canai, fried chicken, and fried egg were addressed, by training the model on a diverse and well-annotated food dataset. We also tackle the issue of calorie estimation. Experimental evaluations of the proposed YOLOv4 for food detection demonstrates 96.07% of accuracy. Thus, it could be deduced that the proposed deep learning-based food detection and calorie counter have the potential to significantly improve dietary monitoring and contribute to the promotion of healthier eating habits. © 2023 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Ibrahim S.; Arif Aiman Wan Hasnan W.M.; Md Ghani N.A.; Fariza Abu Samah K.A.; Abu Mangshor N.N.; Ahmad Fadzil A.F.; Janor R.M.
spellingShingle Ibrahim S.; Arif Aiman Wan Hasnan W.M.; Md Ghani N.A.; Fariza Abu Samah K.A.; Abu Mangshor N.N.; Ahmad Fadzil A.F.; Janor R.M.
Deep Learning Food Detection and Calories Counter
author_facet Ibrahim S.; Arif Aiman Wan Hasnan W.M.; Md Ghani N.A.; Fariza Abu Samah K.A.; Abu Mangshor N.N.; Ahmad Fadzil A.F.; Janor R.M.
author_sort Ibrahim S.; Arif Aiman Wan Hasnan W.M.; Md Ghani N.A.; Fariza Abu Samah K.A.; Abu Mangshor N.N.; Ahmad Fadzil A.F.; Janor R.M.
title Deep Learning Food Detection and Calories Counter
title_short Deep Learning Food Detection and Calories Counter
title_full Deep Learning Food Detection and Calories Counter
title_fullStr Deep Learning Food Detection and Calories Counter
title_full_unstemmed Deep Learning Food Detection and Calories Counter
title_sort Deep Learning Food Detection and Calories Counter
publishDate 2023
container_title 2023 IEEE International Conference on Computing, ICOCO 2023
container_volume
container_issue
doi_str_mv 10.1109/ICOCO59262.2023.10397620
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184850789&doi=10.1109%2fICOCO59262.2023.10397620&partnerID=40&md5=e7d006ee4d70d17cbca97f8a18c25b07
description Monitoring and managing dietary intake plays a vital role in maintaining a healthy lifestyle. However, accurately tracking food consumption and estimating calorie intake can be challenging. This paper presents a deep learning-based approach of You Only Look Once version 4 (YOLOv4) model for food detection and calorie counting. Leveraging the power of deep neural networks, the proposed study automatically detects and classifies food items from images and provides real-time estimation of their calorie content. Seven classes of food which are fried noodles, fried rice, kaya toast, nasi lemak, roti canai, fried chicken, and fried egg were addressed, by training the model on a diverse and well-annotated food dataset. We also tackle the issue of calorie estimation. Experimental evaluations of the proposed YOLOv4 for food detection demonstrates 96.07% of accuracy. Thus, it could be deduced that the proposed deep learning-based food detection and calorie counter have the potential to significantly improve dietary monitoring and contribute to the promotion of healthier eating habits. © 2023 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
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language English
format Conference paper
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