Abstract:

In the contemporary era of health and fitness, accurate caloric estimation plays a pivotal role in dietary management and weight control. This study proposes a novel approach to estimate caloric content in food items through a deep learning framework utilizing the You Only Look Once (YOLO) object detection algorithm. The research focuses on developing a real-time system capable of identifying various food items from images and estimating their caloric values by leveraging an extensive database of nutritional information. The YOLO model is trained on a diverse dataset of food images to ensure robust detection and classification performance across different cuisines and presentation styles. By integrating the detected food items with their corresponding caloric values, the system facilitates automated caloric estimation, providing users with a seamless interface for dietary monitoring. The effectiveness of the proposed method is evaluated through extensive experiments, demonstrating its potential to enhance nutritional awareness and support informed dietary choices. This study underscores the importance of innovative technological solutions in promoting healthier lifestyles and improving individual dietary practices.

Keywords:
Dataset,
Yolo algorithm

Objectives:
The primary objectives of this study include:
1. Developing a YOLO-based Model: To design and implement a YOLO model trained on a comprehensive dataset of food images for accurate object detection and classification.
2. Caloric Estimation Integration: To integrate the YOLO model with a nutritional database to provide instant caloric values for identified food items.
3. User-Friendly Interface: To create a user-friendly interface that allows individuals to upload images of their meals and receive instant caloric estimations, enhancing their dietary management experience.
4. Evaluating System Performance: To assess the performance of the proposed system through rigorous experimentation, including accuracy, speed, and user satisfaction.
5. Exploring Future Applications: To explore the potential applications of this technology in promoting healthier lifestyles and dietary practices, both for individual users and within broader public health initiatives.