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Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms.

Bryan Gonzalez1, Gonzalo Garcia2, Sergio A Velastin3,4

  • 1Escuela de Ingenieria Electrica, Pontificia Universidad Catolica de Valparaıso, Valparaíso 2340025, Chile.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
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This summary is machine-generated.

This study uses AI and computer vision for automated food service analysis, accurately counting dishes and estimating portion sizes in dining halls with high precision.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Food Service Management

Background:

  • Automated quantification of food items is crucial for efficient food distribution.
  • Traditional methods for dish counting and portion estimation are labor-intensive and prone to error.

Purpose of the Study:

  • To develop and validate an AI-powered system for quantifying food distribution services.
  • To accurately count dishes, identify food content, and estimate portion sizes in a dining hall setting.

Main Methods:

  • Utilized the YOLO (You Only Look Once) architecture for object detection and image analysis.
  • Employed RGB and depth cameras for capturing tray delivery processes and measuring food volume.
  • Developed density models for food-specific weight estimation based on volume measurements.
Keywords:
artificial intelligencecomputer visiondeep learningfood weight estimation

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Main Results:

  • Achieved a mean average precision (mAP) of 0.873 for object detection tasks.
  • Demonstrated low error margins in weight estimation: 5.07% for rice and 3.75% for chicken.
  • Validated the system's feasibility and accuracy in a real-world dining hall environment.

Conclusions:

  • The proposed computer vision and AI system offers a highly accurate and efficient solution for food service quantification.
  • This technology has the potential to significantly improve inventory management, waste reduction, and operational efficiency in food distribution.