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Food Image Recognition and Food Safety Detection Method Based on Deep Learning.

Ying Wang1, Jianbo Wu2,3, Hui Deng1

  • 1College of Food and Chemistry Engineering, Shaoyang University, Shao Yang, Hunan 422000, China.

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This study introduces a novel neural network method for food image recognition, improving accuracy and speed without manual labeling. It also presents a technique for detecting foreign bodies in food using threshold segmentation.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Food Science

Background:

  • Deep learning, a subset of machine learning, is increasingly applied to image recognition tasks.
  • Existing deep learning methods for food recognition face challenges with complexity, accuracy, and speed.
  • Automated food recognition is crucial for various applications, including quality control and dietary analysis.

Purpose of the Study:

  • To develop an accurate and efficient food image recognition method.
  • To address the limitations of current deep learning approaches in food recognition.
  • To propose a method for detecting and recognizing foreign bodies in food products.

Main Methods:

  • A novel food image recognition method combining Tiny-YOLO and a twin network was developed.
  • A two-stage learning mode, YOLO-SIMM, was proposed with two versions: YOLO-SiamV1 and YOLO-SiamV2.
  • Foreign body detection utilized threshold segmentation technology to separate foreign matter from food.

Main Results:

  • The proposed method achieved general recognition accuracy for food images.
  • The approach eliminates the need for manual data labeling, enhancing practical applicability.
  • Threshold segmentation effectively distinguished desiccants from other foreign matter in food.

Conclusions:

  • The developed neural network-based method shows promise for practical food image recognition and application.
  • The foreign body detection technique is effective in identifying specific contaminants like desiccants.
  • Further development could enhance accuracy and speed, broadening the scope of deep learning in food analysis.