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An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5.

Guijuan Lin1, Keyu Liu1, Xuke Xia2

  • 1School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China.

Sensors (Basel, Switzerland)
|January 8, 2023
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Summary

This study introduces an improved YOLOv5 algorithm for fabric defect detection on embedded devices, enhancing accuracy and speed for small, challenging defects.

Keywords:
Swin TransformerYOLOv5computer visiondeep learningfabric detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Textile Manufacturing

Background:

  • Embedded devices face limitations in computing resources, hindering effective fabric defect detection.
  • Challenges include small defect sizes, extreme aspect ratio variations, and slow detection speeds.

Purpose of the Study:

  • To enhance fabric defect detection on embedded devices by addressing limitations in speed and accuracy for small defects.
  • To improve the perception and detection rate of small fabric faults.

Main Methods:

  • A sliding window multihead self-attention mechanism was proposed for small target detection.
  • The Swin Transformer module replaced the main module in the YOLOv5 algorithm.
  • A weighted bidirectional feature network and a four-scale detection layer were incorporated.
  • Generalized focal loss was implemented to improve positive sample learning.

Main Results:

  • The improved algorithm achieved 85.6% accuracy on the fabric dataset.
  • Mean Average Precision (mAP) increased by 4.2% to 76.5%.
  • The algorithm meets real-time detection requirements for embedded devices.

Conclusions:

  • The proposed enhancements significantly improve fabric defect detection performance on resource-constrained embedded systems.
  • The method effectively addresses challenges posed by small defect sizes and aspect ratios.
  • The optimized algorithm enables efficient and accurate real-time fabric quality control.