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Research on fabric surface defect detection algorithm based on improved Yolo_v4.

Yuanyuan Li1, Liyuan Song2, Yin Cai2

  • 1Shanghai University of Engineering Science, Songjiang, Shanghai, 201620, China. liyuanyuanedu@163.com.

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|March 6, 2024
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Summary
This summary is machine-generated.

This study introduces an enhanced YOLOv4 model for industrial defect detection, improving accuracy for small defects. The system achieves high mean average precision (mAP) and real-time processing speeds for defect classification and localization.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Industrial defect detection systems require both classification and localization accuracy.
  • Existing methods often struggle to achieve high accuracy in both tasks simultaneously, particularly for minor defects.
  • Challenges include detecting small targets and ensuring robust feature extraction and propagation.

Purpose of the Study:

  • To develop an improved defect detection system with enhanced accuracy for minor defects.
  • To address limitations in existing defect detection algorithms, specifically in feature extraction and anchor point determination.
  • To achieve real-time defect detection capabilities for industrial applications.

Main Methods:

  • An improved YOLOv4 (You Only Look Once version 4) model is proposed for defect detection.
  • Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used for anchor point determination, replacing subjective K-Means clustering.
  • An ECA-DenseNet-BC-121 feature extraction network and a Dual Channel Feature Enhancement (DCFE) module are integrated to improve feature representation and gradient flow.

Main Results:

  • The improved YOLOv4 system achieved a mean average precision (mAP) of 98.97% on fabric surface defect detection datasets.
  • Performance improvements were significant compared to other models: +7.67% over SSD, +3.75% over Faster R-CNN, +10.82% over YOLOv4-tiny, and +5.35% over YOLOv4.
  • The system achieved a detection speed of 39.4 frames per second (fps), meeting real-time monitoring requirements.

Conclusions:

  • The proposed improved YOLOv4 system effectively enhances defect classification and localization accuracy, especially for minor defects.
  • The integration of DBSCAN, ECA-DenseNet-BC-121, and the DCFE module significantly boosts model performance and robustness.
  • The system demonstrates suitability for real-time industrial defect detection, offering a practical solution for quality control.