Smart Diet Diary: Real-Time Mobile Application for Food Recognition
Abstract
:1. Introduction
- We developed a semi-automated diet tracking smartphone application based on a food recognition engine trained using faster R-CNN.
- We developed our food recognition model for mobile application and extended it with a mechanism to calculate the volume and approximate calorie value.
- We generated a customized food image dataset composed of over 16,000 images from fourteen classes to train and test the system.
- We achieved a combined accuracy of approximately 80.1%, with a maximum of 90.2% for some of the classes.
2. Related Work
3. Materials and Methods
3.1. System Architecture
3.2. Application Development
3.3. Dataset
3.4. Object Detection and Classification
3.5. Volume Estimation
Algorithm 1. GrabCut |
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3.6. Calorie Estimation
3.7. Mobile Application
3.7.1. User Profile
3.7.2. Diary Component and Food Database
3.7.3. Food Classifier Component
3.7.4. Food Correction Component
4. Results and Discussion
4.1. Evaluation Metrics
4.2. Evaluation Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Sr. No. | Category | Training | Test | Total |
---|---|---|---|---|
1 | Apple | 671 | 200 | 871 |
2 | Bluebird sour cream | 805 | 200 | 1005 |
3 | Chicken curry | 651 | 200 | 851 |
4 | Coca-Cola can | 1035 | 200 | 1230 |
5 | Coin | 1546 | 400 | 1946 |
6 | ETA chicken | 817 | 200 | 1017 |
7 | ETA sour cream | 809 | 200 | 1009 |
8 | Fanta can | 1059 | 150 | 1209 |
9 | McDonalds Big-Mac | 849 | 200 | 1049 |
10 | McDonalds Fillet-o-Fish | 872 | 200 | 1072 |
11 | McDonalds Fries | 849 | 200 | 1049 |
12 | McDonalds McChicken | 836 | 200 | 1036 |
13 | Orange | 718 | 200 | 918 |
14 | Pizza Hut | 846 | 200 | 1046 |
Item | Accuracy | Average Calories | Actual Calories |
---|---|---|---|
Coca-Cola can | 60% | 153 | 142 |
Apple | 92% | 77 | 78 |
Orange | 90% | 53 | 58 |
Authors | Dataset | Categories | Techniques | Accuracy |
---|---|---|---|---|
Proposed Approach | Self-collected | 10 | Faster R-CNN | 80.06% |
Yanai et al. (2021) [50] | UECFOOD100 | 1000 | DCNN | 78.77% |
UEC-FOOD256 | 1000 | 67.57% | ||
Joutou et al. (2009) [47] | 50 | Multiple kernel learning | 61.34% | |
Zhang et al. (2015) [45] | Online image database, e.g., ImageNet, Flickr, and Google Images | 15 | HOG/ SIFT | 80.30% |
Chen et al. (2016) [19] | VIREO Food-172 | 100 | Multi-task DCNN | 82.12 to 97.29% |
Lu (2016) [69] | Small-scale dataset | 10 | CNN, 3 convolution-pooling layers, 1 fully connected layer | 74.0% |
Ege et al. (2017) [18] | Web image mining | 15 | multi-task CNN | 77.90 to 80.60% |
Horiguchi et al. (2018) [28,52] | FoodLog-FLD | 213 | CNN-based fixed-class | 54.60 to 72.40% |
Subhi and Ali (2018) [70] | Self-collected Malaysian foods | 11 | Modified VGG19-CNN, 21 convolutional layers, 3 fully connected layers | Not reported |
Islam et al. (2018) [71] | Food-11 dataset | 11 | CNN, 5 convolution layers, 3 max-pooling layers, 1 fully connected layer | 74.70% |
Inception V3 pre-trained, 2 fully connected layers | 92.86% | |||
Jeny et al. (2019) [72] | Self-collected, Bangladeshi foods | 6 | FoNet-based deep residual Neural network with 47 layers | 98.16%. |
Razali et al. (2021) [73] | Sabahan foods | 11 | EFFNet + CNN | 94.01% |
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Nadeem, M.; Shen, H.; Choy, L.; Barakat, J.M.H. Smart Diet Diary: Real-Time Mobile Application for Food Recognition. Appl. Syst. Innov. 2023, 6, 53. https://doi.org/10.3390/asi6020053
Nadeem M, Shen H, Choy L, Barakat JMH. Smart Diet Diary: Real-Time Mobile Application for Food Recognition. Applied System Innovation. 2023; 6(2):53. https://doi.org/10.3390/asi6020053
Chicago/Turabian StyleNadeem, Muhammad, Henry Shen, Lincoln Choy, and Julien Moussa H. Barakat. 2023. "Smart Diet Diary: Real-Time Mobile Application for Food Recognition" Applied System Innovation 6, no. 2: 53. https://doi.org/10.3390/asi6020053
APA StyleNadeem, M., Shen, H., Choy, L., & Barakat, J. M. H. (2023). Smart Diet Diary: Real-Time Mobile Application for Food Recognition. Applied System Innovation, 6(2), 53. https://doi.org/10.3390/asi6020053