DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion
Abstract
:1. Introduction
- We proposed a novel monocular image-based nutrition estimation method based on Depth Prediction and Fusion, referred as DPF-Nutrition. It was the first attempt to incorporate depth prediction and RGB-D fusion techniques in vision-based nutrition estimation.
- We designed an RGB-D fusion module that incorporated the proposed cross-modal attention block (CAB) and multi-scale fusion network to fully exploit the informative image features for nutrition estimation.
- Our proposed DPF-Nutrition demonstrated effectiveness in accurately estimating multiple nutrients, which has been evaluated on the public dataset Nutrition5k.
2. Materials and Methods
2.1. Dataset
2.2. Methods
- Module1: Depth prediction module aims to reconstruct the 3D depth information based on 2D monocular images. The depth prediction module employs a vision transformer as the encoder which can reduce the loss of granularity and feature resolution for more accurate 3D information recovery.
- Module2: RGB-D fusion module is specifically designed to fully leverage the features of RGB and predicted depth images for nutrition estimation. This module integrates the proposed multi-scale fusion network and cross-modal attention block (CAB). The multi-scale fusion network effectively enriches the intricate semantic features of fine-grained food images, while the CAB further enhances the complementarity of RGB and depth features.
2.2.1. The Depth Prediction Module
2.2.2. RGB-D Fusion Module
2.2.3. Loss Function
2.3. Evaluation Metrics
3. Results
3.1. Experimental Detail
3.2. Backbone Comparison
3.3. Depth Prediction Analysis
3.4. Cross-Modal Fusion Analysis
3.5. Method Comparison
3.6. Ablation Study
3.7. Visualization Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dataset | Diversity | Size | Annotation | Depth |
---|---|---|---|---|
MenuMatch [19] | 41 | 646 | Calories | N |
ECUSTFD [20] | 160 | 2978 | Volume and Mass | N |
Fang et al. [21] | 3 | 45 | Calories | N |
Ando et al. [22] | 3 | 270 | Calories | Y |
Nutrition5k [10] | 5066 | 3490 | Calories, Mass and Macronutrients | Y |
Methods | Calories MAE (kCal)/PMAE (%) | Mass MAE (g)/PMAE (%) | Fat MAE (g)/PMAE (%) | Carb MAE (g)/PMAE (%) | Protein MAE (g)/PMAE (%) | Mean PMAE (%) |
---|---|---|---|---|---|---|
ViT [29] | 52.4/20.4 | 32.5/16.3 | 3.78/29.4 | 5.72/28.9 | 5.72/28.9 | 24.6 |
VGG16 [28] | 47.9/18.6 | 29.2/14.6 | 3.61/28.0 | 5.18/26.2 | 4.71/26.8 | 22.8 |
InceptionV3 [36] | 46.2/18.0 | 28.4/14.2 | 3.24/25.1 | 5.1/25.8 | 4.37/24.8 | 21.6 |
ResNet50 [23] | 46.9/18.2 | 27.7/13.8 | 3.61/28.0 | 4.51/22.8 | 4.56/25.9 | 21.7 |
ResNet101 [23] | 46.0/17.9 | 27.2/13.6 | 3.42/26.5 | 4.56/23.0 | 4.31/24.5 | 21.1 |
Method | AbsRel | RMSE | |||
---|---|---|---|---|---|
UNet [26] | 0.573 | 0.771 | 0.860 | 0.661 | 0.211 |
FCRN [37] | 0.634 | 0.799 | 0.877 | 0.529 | 0.189 |
HRNet [38] | 0.692 | 0.864 | 0.906 | 0.387 | 0.162 |
DPT [24] | 0.743 | 0.893 | 0.942 | 0.322 | 0.128 |
Depth Source | Calories MAE (kCal)/PMAE (%) | Mass MAE (g)/PMAE (%) | Fat MAE (g)/PMAE (%) | Carb MAE (g)/PMAE (%) | Protein MAE (g)/PMAE (%) | Mean PMAE (%) |
---|---|---|---|---|---|---|
UNet [26] | 41.6/16.2 | 22.6/11.3 | 3.29/25.5 | 4.43/22.4 | 4.01/22.8 | 19.6 |
FRCN [37] | 40.7/15.8 | 22.7/11.3 | 3.17/24.6 | 4.28/21.6 | 3.87/22.0 | 19.1 |
HRNet [38] | 39.3/15.3 | 21.6/10.8 | 3.01/23.3 | 4.13/20.9 | 3.72/21.2 | 18.3 |
DPT [24] | 37.9/14.7 | 21.2/10.6 | 2.92/22.6 | 4.09/20.7 | 3.56/20.2 | 17.8 |
Depth sensor | 36.5/14.2 | 20.4/10.2 | 2.76/21.4 | 4.08/20.6 | 3.48/19.8 | 17.2 |
Method | Calories MAE (kCal)/PMAE (%) | Mass MAE (g)/PMAE (%) | Fat MAE (g)/PMAE (%) | Carb MAE (g)/PMAE (%) | Protein MAE (g)/PMAE (%) | Mean PMAE (%) |
---|---|---|---|---|---|---|
Enhancement–Fusion | 39.1/15.2 | 22.6/11.3 | 3.03/23.5 | 4.14/20.9 | 3.80/21.6 | 18.5 |
Fusion–Enhancement | 39.3/15.3 | 21.6/10.8 | 3.01/23.3 | 4.13/20.9 | 3.72/21.2 | 18.3 |
CAB | 37.9/14.7 | 21.2/10.6 | 2.92/22.6 | 4.09/20.7 | 3.56/20.2 | 17.8 |
Input | Methods | Calories MAE (kCal)/ PMAE (%) | Mass MAE (g)/ PMAE (%) | Fat MAE (g)/ PMAE (%) | Carb MAE (g)/ PMAE (%) | Protein MAE (g)/ PMAE (%) | Mean PMAE (%) |
---|---|---|---|---|---|---|---|
RGB-D images | Google-Nutrition-depth [10] | 47.6/18.8 | 40.7/18.9 | 2.27/18.1 | 4.6/23.8 | 3.7/20.9 | 20.1 |
RGB-D Nutrition [15] | 38.5/15.0 | 21.6/10.8 | 3.0/23.5 | 4.43/22.4 | 3.69/21.0 | 18.5 | |
Monocular images | Google-Nutrition-monocular [10] | 70.6/26.1 | 40.4/18.8 | 5.0/34.2 | 6.1/31.9 | 5.5/29.5 | 29.1 |
Swin-Nutrition [11] | 41.5/16.2 | 27.5/13.7 | 3.21/24.9 | 4.32/21.8 | 4.47/25.4 | 20.4 | |
DPF-Nutrition (ours) | 37.9/14.7 | 21.2/10.6 | 2.92/22.6 | 4.09/20.7 | 3.56/20.2 | 17.8 |
Index | Model | Calories MAE (kCal)/ PMAE (%) | Mass MAE (g)/ PMAE (%) | Fat MAE (g)/ PMAE (%) | Carb MAE (g)/ PMAE (%) | Protein MAE (g)/ PMAE (%) | Mean PMAE (%) |
---|---|---|---|---|---|---|---|
(a) | RGB Stream | 46.0/17.9 | 27.2/13.6 | 3.42/26.5 | 4.56/23.0 | 4.31/24.5 | 21.1 |
(b) | Depth Stream | 83.5/32.5 | 44.7/22.3 | 6.29/48.8 | 8.08/40.8 | 8.53/ 8.5 | 38.6 |
(c) | (a) + (b) + direct fusion | 42.7/16.7 | 25.0/12.5 | 3.17/24.6 | 4.31/21.8 | 3.97/22.6 | 19.6 |
(d) | (a) + (b) + multi-scale fusion | 40.7/15.8 | 24.8/12.4 | 3.01/23.3 | 4.13/20.9 | 3.88/22.1 | 18.9 |
(e) | (d) + CAB | 37.9/14.7 | 21.2/10.6 | 2.92/22.6 | 4.09/20.7 | 3.56/20.2 | 17.8 |
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Han, Y.; Cheng, Q.; Wu, W.; Huang, Z. DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion. Foods 2023, 12, 4293. https://doi.org/10.3390/foods12234293
Han Y, Cheng Q, Wu W, Huang Z. DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion. Foods. 2023; 12(23):4293. https://doi.org/10.3390/foods12234293
Chicago/Turabian StyleHan, Yuzhe, Qimin Cheng, Wenjin Wu, and Ziyang Huang. 2023. "DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion" Foods 12, no. 23: 4293. https://doi.org/10.3390/foods12234293
APA StyleHan, Y., Cheng, Q., Wu, W., & Huang, Z. (2023). DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion. Foods, 12(23), 4293. https://doi.org/10.3390/foods12234293