Food Discernment And Calories Estimation Using Segmentation

Author(s)	Shreya Anil Kubade, Suraj Shivaji Redekar
Country	India
Abstract	This research paper presents a new method for identifying and estimating food calorie content using Image Segmentation techniques. The method combines deep learning and Image Segmentation to improve accuracy in identifying and estimating food content. The two-step process involves food segmentation using a convolution neural network and data augmentation strategy. The calorie calculation module, combining deep neural networks and data modeling, is used to categorize foods and estimate calorie content using established formulas. Extensive testing on real-world food images demonstrates that the method outperforms conventional methods, achieving over 90% average segmentation accuracy and less than 10% average calorie estimation error.
Keywords	Computer vision, Machine Learning, Deep Learning, object detection, Mask R-CNN, Instance segmentation, Optimizer and loss function
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-12-11
Cite This	Food Discernment And Calories Estimation Using Segmentation - Shreya Anil Kubade, Suraj Shivaji Redekar - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.10177
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.10177
Short DOI	https://doi.org/gs84ch

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International Journal For Multidisciplinary Research