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Unsupervised Tablet Defect Detection Method Based on Diffusion Model.

Mengfan Zhang1, Weifeng Liu1, Linqing He1

  • 1School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710016, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel Diffusion-based Tablet Defect Detection (DTDD) method. DTDD enhances tablet anomaly detection by improving reconstruction and precisely locating defects, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Quality Control

Background:

  • Reconstruction-based unsupervised methods are used for tablet anomaly detection.
  • Existing methods suffer from poor reconstruction and inaccurate localization of defects.

Purpose of the Study:

  • To propose an unsupervised Diffusion-based Tablet Defect Detection (DTDD) method.
  • To improve the reconstruction effect and localization accuracy of tablet defects.

Main Methods:

  • Utilizes an Assisted Reconstruction (AR) network to enhance reconstruction quality.
  • Employs a Scale Fusion (SF) network and improved anomaly measurement for precise defect positioning.

Main Results:

  • The DTDD method shows superior performance compared to existing algorithms.
Keywords:
anomaly locationdiffusion modelfeature extractiontablet anomaly detectionunsupervised detection method

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  • Demonstrates significant improvements in detection accuracy and abnormal area positioning.

    • Conclusions:

    • The proposed DTDD method is effective for tablet defect detection.
    • Offers a robust solution for unsupervised anomaly detection in industrial settings.