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A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

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This summary is machine-generated.

This study introduces a deep learning method for high-precision candy defect detection, enhancing food quality management. The approach improves accuracy and recall rates for real-time identification of defects on production lines.

Keywords:
YOLOv7computer visiondeep learningdefects detectiongenerative adversarial networks

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Food Science

Background:

  • Candy defect detection is crucial for quality management but faces challenges due to small defect sizes and automated production limitations.
  • Existing methods struggle with accurate identification and batch sampling of defects on production lines.
  • Data imbalance between complete and defective candies negatively impacts detection performance.

Purpose of the Study:

  • To propose a high-precision candy defect detection method using deep learning.
  • To address challenges of small defect size, random shapes, and data imbalance in automated candy production.
  • To enhance the efficiency of food quality management through advanced computer vision techniques.

Main Methods:

  • Generated pseudo-defective candy images using Style Generative Adversarial Network-v2 (StyleGAN2) for enhanced authenticity.
  • Employed Generative Adversarial Network (GAN) for negative sample data enhancement to mitigate data imbalance.
  • Improved the YOLOv7 target detection model by integrating Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC), C3C2 module, and a global attention mechanism.

Main Results:

  • The improved YOLOv7 model achieved a 3.0% increase in recognition accuracy.
  • A 3.7% increase in recall rate was observed with the enhanced detection model.
  • The method supports real-time recognition, demonstrating practical applicability in industrial settings.

Conclusions:

  • The proposed deep learning method significantly enhances candy defect detection accuracy and efficiency.
  • The integration of StyleGAN2 and improved YOLOv7 effectively addresses data imbalance and small defect detection challenges.
  • This research promotes the application of computer vision and deep learning in industrial food quality management.