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Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images.

Yilin Wang1, Yulong Zhang1, Li Zheng1,2

  • 1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)
|October 26, 2021
PubMed
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This summary is machine-generated.

This study introduces an unsupervised deep learning method for tire defect detection using X-ray images. The approach effectively identifies defects without needing labeled defective samples, improving industrial inspection efficiency.

Area of Science:

  • Industrial Imaging
  • Machine Learning
  • Non-Destructive Testing

Background:

  • Manual inspection of tire X-ray images is inefficient and labor-intensive.
  • Supervised deep learning models struggle with imbalanced datasets common in industrial defect detection.
  • Defective tire samples are often scarce, hindering the training of traditional supervised models.

Purpose of the Study:

  • To develop an unsupervised approach for automatic tire defect detection using X-ray imaging.
  • To overcome the challenge of insufficient defective samples in industrial training datasets.
  • To enhance the accuracy and efficiency of tire quality control processes.

Main Methods:

  • An unsupervised reconstruction-based approach is proposed, trained exclusively on defect-free tire X-ray images.
Keywords:
anomaly detectiongenerative adversarial networkimage reconstructionmemory-augmented moduletire defect detection

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  • An augmented reconstruction method and a self-supervised training strategy are introduced to improve feature extraction.
  • The model learns to reconstruct defect-free images; reconstruction residuals are used to identify anomalies.

    • Main Results:

    • The unsupervised method demonstrates effectiveness in detecting defects without requiring labeled defective samples.
    • The self-supervised training strategy enhances the reconstruction residual for improved detection performance.
    • The proposed method achieved an Area Under Curve (AUC) of 0.873 on a tire X-ray dataset.

    Conclusions:

    • The unsupervised, reconstruction-based method is a promising solution for automatic tire defect detection in industrial settings.
    • The approach effectively addresses the challenge of imbalanced datasets by utilizing only defect-free samples for training.
    • This technique offers a viable alternative to manual inspection and supervised learning for tire quality assurance.