Abstract:

The integration of YOLOv8 (You Only Look Once) and advanced image processing techniques has emerged as a groundbreaking innovation, revolutionizing the detection and estimation of food calorie content. This research endeavor is dedicated to introducing a cutting-edge methodology for accurately estimating the calorie content of diverse food items, primarily based on their respective volumes. Leveraging the power of deep learning and computer vision, this study showcases a comprehensive framework that combines the analysis of image data with precise volumetric measurements. By integrating these key components, the research not only offers a robust foundation for quantifying food calorie content but also provides valuable insights for the development of a more effective and accessible dietary monitoring system. Furthermore, the paper meticulously reviews and synthesizes the findings of 21 significant literature sources, shedding light on the intricate landscape of existing research in the domain. Through this critical analysis, the paper emphasizes the unique contributions and advancements that this study brings to the field of food calorie estimation. By amalgamating theoretical insights with practical implementation, this research sets a precedent for the fusion of advanced image processing techniques and deep learning algorithms, paving the way for a more nuanced and accurate approach to food calorie detection and dietary assessment

Description:

YOLO, deep learning, food calorie estimation, computer vision, dietary monitoring, image processing, volumetric analysis