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Artificial intelligence to estimate wine volume from single-view images.

Miriam Cobo¹, Ignacio Heredia¹, Fernando Aguilar¹

¹Institute of Physics of Cantabria (IFCA), CSIC - UC, 39005 Santander (Cantabria), Spain.

|September 19, 2022

Summary

This study introduces an image-based method to accurately measure red wine volume in various glasses. The system achieves high accuracy, aiding in diet and consumption studies.

Keywords:

Deep learning model Quantitative red wine volume estimation Single-view image

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Area of Science:

Computer Vision
Food Science
Analytical Chemistry

Background:

Accurate measurement of liquid volumes is crucial for dietary monitoring and consumption studies.
Manual measurement of wine volume can be time-consuming and prone to errors.
Existing automated methods may lack versatility across different container types and conditions.

Purpose of the Study:

To develop and validate an automated system for determining red wine volume from single-view images.
To create a comprehensive dataset for training and evaluating image-based volume measurement models.
To provide a practical tool for applications in diet monitoring and wine consumption research.

Main Methods:

A novel image-based model was developed to predict red wine volume from photographs.
The WineGut_BrainUp dataset, comprising 24,305 laboratory images, was introduced for system training and evaluation.
The dataset features diverse conditions, including various glass types, wine volumes, backgrounds, and lighting.

Main Results:

The proposed image-based wine measurement system demonstrated satisfactory performance.
The system achieved a Mean Absolute Error (MAE) below a specified threshold, indicating high accuracy.
Experimental results confirm the system's effectiveness in estimating red wine volumes.

Conclusions:

The developed methodology offers a suitable analytical tool for the automated measurement of red wine volume.
The system has significant potential for real-world applications in diet monitoring and wine consumption studies.
This image-based approach provides a non-invasive and efficient method for volume quantification.