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An Improved YOLOv5 Model: Application to Mixed Impurities Detection for Walnut Kernels.

Lang Yu1, Mengbo Qian1, Qiang Chen1

  • 1College of Optical Mechanical and Electrical Engineering, Zhejiang A & F University, Hangzhou 311300, China.

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|February 11, 2023
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Summary

An improved YOLOv5 model enhances impurity detection in walnuts, achieving 88.9% accuracy. This AI-powered system offers a significant improvement for food processing quality control.

Keywords:
YOLOv5impurities detectionsmall object detectionwalnut kernels

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Food Science

Background:

  • Accurate impurity detection is crucial in food processing, particularly for items like walnut kernels where small contaminants are challenging to identify before packaging.
  • Existing methods struggle with the precise identification of small impurities mixed within food products.

Purpose of the Study:

  • To develop an improved impurity detection model for walnut kernels based on the YOLOv5 network.
  • To enhance the accuracy and efficiency of identifying small impurities in food products.

Main Methods:

  • An enhanced YOLOv5 model was developed by incorporating a small target detection layer, a Transformer-Encoder (Trans-E) module, a Convolutional Block Attention Module (CBAM), and the GhostNet module.
  • The model was trained and tested on a custom walnut kernel database, evaluating performance using mean average precision (mAP).

Main Results:

  • The improved YOLOv5 model achieved a mean average precision (mAP) of 88.9%, a 6.7% increase compared to the original YOLOv5 network.
  • The enhanced model demonstrated superior performance in detecting small impurities, with only a minor 3.9% reduction in detection rate, meeting real-time detection demands.

Conclusions:

  • The proposed improved YOLOv5 model significantly enhances the accuracy and capability for detecting small impurities in walnut kernels.
  • This AI-driven approach provides a valuable technical reference for real-time impurity detection in the food industry.