Abstract:
Accurate food calorie estimation is essential for managing dietary intake, particularly for
individuals with diabetes. This project presents an end-to-end system for food classification,
weight estimation, and calorie calculation from a single food image without requiring
a reference object. The system integrates deep learning techniques including convolutional
neural networks (CNNs) for food classification, YOLO and Segment Anything
Model (SAM) for food segmentation and MiDaS for depth estimation. The DeshiFoodBD
dataset consisting of 19 food classes such as bakorkhani, beguni, and kala bhuna is
used for food classification. To evaluate classification performance multiple deep learning
models including a basic CNN, EfficientNetB0, ResNet50, MobileNetV2 and InceptionV3
have been compared. The segmented food region is processed using depth maps
to estimate its volume which is then converted to weight based on standard nutritional
values. Finally, the estimated weight is used to compute calorie content. By leveraging
advanced computer vision techniques, this method aims to provide an automated and
practical approach for dietary monitoring, enhancing convenience and accuracy in food
intake assessment.
