Food Item Calorie Estimation Using YOLOv4 and Image Processing

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2021 by IJCTT Journal
Volume-69 Issue-5
Year of Publication : 2021
Authors : Samidha Patil, Shivani Patil, Vaishnavi Kale, Mohan Bonde
  10.14445/22312803/IJCTT-V69I5P110

MLA Style: 
Samidha Patil., et al. "Food Item Calorie Estimation Using YOLOv4 and Image Processing."  International Journal of Computer Trends and Technology,  vol. 69, no. 5, May. 2021, pp.69-76. Crossref https://doi.org/ 10.14445/22312803/IJCTT-V69I5P110

APA Style:   
Samidha Patil., Shivani Patil., Vaishnavi Kale., & Mohan Bonde 
(2021) . Food Item Calorie Estimation Using YOLOv4 and Image Processing. International Journal of Computer Trends and Technology , 69(5), 69-76. https://doi.org/ 10.14445/22312803/IJCTT-V69I5P110

Abstract
In last decade or two, an increase in growth of obesity has been seen all around the world. There has been increasing research to tackle obesity using food logging and food item calorie analysis. An increase in healthy living has led to numerous food management applications, which have image recognition to automatically record meals. To achieve healthy living it's important for someone to observe his/her daily calorie intake. The project aims to incorporate modern technique for object detection together with image analysis techniques to determine a more accurate calorie count from images of food items. The strategy employed involves determining the calorie count of the food item through mathematical calculations of the features extracted from food image by image segmentation. In this paper, we propose a mobile software for food calorie estimation from images of food items. By using YOLO- You Only Look Once for Object detection and Image segmentation for calorie estimation we are able to detect the food and thereby calculate the required food calories from the varied datasets of Indian cuisine.

Keywords
Calorie Estimation, Object Detection, YOLO.

Reference

[1] Anil K Jain and Farshid Farrokhnia., Unsupervised texture segmentation using Gabor filters, Pattern recognition. 24(12) (1991) 1167–1186.
[2] Tatsuya Miyazaki, Gamhewage C de Silva, and Kiyoharu Aizawa, Image-based calorie content estimation for dietary assessment, In2011 IEEE Inter-national Symposium on Multimedia. (2011) 363– 368.
[3] Parisa Pouladzadeh, Shervin Shirmohammadi, and Rana AlMaghrabi, Measuring Calorie and Nutrition from Food Image, IEEE Transactions on Instrumentation and Measurement, 63(8) (2014) 1947–1956.
[4] Karen Simonyan & Andrew Zisserman, Very deep convolutional networks for large-scale image recognition , ICLR (2015)
[5] Patrick McAllister, Huiru Zheng, Raymond Bond, and Anne Moorhead, Semi-automated system for predicting calories in photographs of meals. In2015 IEEE International Conference on Engineering, Technology and Innovation/International Technology Management Conference (ICE/ITMC) (2015) 1–6. IEEE.
[6] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 779–788.
[7] Koichi Okamoto and Keiji Yanai, An automatic calorie estimation system of food images on a smartphone, In Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management (2016) 63–70.
[8] Joseph Redmon and Ali Farhadi, Yolo9000: Better, Faster, Stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017) 7263–7271.
[9] Manal Chokr, Shady Elbassuoni, Calories Prediction from Food Images, Proceedings of the Twenty-Ninth AAAI Conference on Innovative Applications (IAAI-17)) (2017).
[10] Shu Liu,Lu Qi,Haifang Qin,Jianping Shi, Jiaya Jia, Path Aggregation Network for Instance Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018) 8759-8768.
[11] Sanghyun Woo, Jongchan Park, Joon-Young Lee and In So Kweon,CBAM: Convolutional Block Attention Module, Proceedings of the European Conference on Computer Vision (ECCV) (2018) 3- 19.
[12] Sujata Chaudhari, Nisha Malkan, Ayesha Momin, Mohan Bonde, Yolo Real Time Object Detection, SSRG International Journal of Computer Trends and Technology 68(6) (2020) 70-76.
[13] Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection, arXiv preprint arXiv:2004.10934 (2020).
[14] Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh, CSPNet: A New Backbone That Can Enhance Learning Capability of CNN (2020) 390-391.
[15] Retrieved from https://towardsdatascience.com/a-comprehensiveguide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
[16] Retrieved from https://www.upgrad.com/blog/basic-cnn-architecture/
[17] Food dataset from https://www.kaggle.com/moltean/fruits
[18] Food dataset from https://www.kaggle.com/rahulbhalley/food-101
[19] Aqua-calc Food Volume to Weight Conversions. [Online]. http://www.aqua-calc.com/page/density-table
[20] Actual calorie database [Online] https://www.uncledavesenterprise.com/file/health/Food%20Calories %20List.pdf