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Deep Neural Networks for Image-Based Dietary Assessment
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Attention-Based Convolutional Neural Network for Ingredients Identification.

Shi Chen1, Ruixue Li1, Chao Wang1

  • 1School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310005, China.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI model for accurate fresh ingredient identification in smart catering systems. The multi-attention CNN model achieves 95.90% accuracy, significantly reducing labor costs and improving efficiency.

Keywords:
artificial intelligenceconvolutional neural networkingredients identificationmulti-attention moduleopen set recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Smart catering relies on accurate ingredient identification for efficiency and cost reduction.
  • Existing ingredient classification methods suffer from low accuracy and flexibility.
  • Automating ingredient identification is crucial for reducing labor costs in food service.

Purpose of the Study:

  • To develop a highly accurate and flexible ingredient identification system for smart catering.
  • To address the limitations of current methods in recognizing a wide variety of fresh ingredients.
  • To implement an open-set recognition module for handling previously unseen ingredients.

Main Methods:

  • Construction of a large-scale fresh ingredients database.
  • Design of an end-to-end multi-attention-based convolutional neural network (CNN) model.
  • Integration of an open-set recognition module for unknown category prediction.

Main Results:

  • The proposed CNN model achieved 95.90% accuracy in classifying 170 types of ingredients.
  • The open-set recognition module attained 74.6% accuracy in identifying unknown samples.
  • Successful deployment in smart catering systems resulted in 92% average accuracy and 60% time savings.

Conclusions:

  • The developed AI model represents a state-of-the-art solution for automatic ingredient identification.
  • The system demonstrates practical effectiveness and significant operational benefits in real-world smart catering applications.
  • The inclusion of open-set recognition enhances the model's adaptability to dynamic inventory changes.