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...
Published in: | 2023 IEEE International Conference on Computing, ICOCO 2023 |
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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 |
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2023 IEEE International Conference on Computing, ICOCO 2023 |
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container_issue |
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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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English |
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Conference paper |
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scopus |
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Scopus |
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1809678018987163648 |