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Updated: Jun 27, 2026

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A Study on a Method for Detecting Surface Defects in Optical Modules Based on Information Entropy Feature Extraction.

Longbing Yang1,2, Quan Xu1, Min Liao3

  • 1School of Mechanical Engineering, Xihua University, Chengdu 610039, China.

Entropy (Basel, Switzerland)
|June 26, 2026
PubMed
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This summary is machine-generated.

This study introduces a novel image processing method for optical module surface defect detection. It significantly improves detection rates and clarity, outperforming traditional methods without deep learning.

Area of Science:

  • Optoelectronics
  • Image Processing
  • Artificial Intelligence Hardware

Background:

  • Optical modules are critical for AI computing and digital communications, with rapidly growing demand.
  • Current surface defect detection methods struggle with noise interference and low-contrast defects, leading to high false negative rates.
  • Accurate and robust defect detection is essential for the mass production of optical modules.

Purpose of the Study:

  • To develop a traditional image processing scheme for optical module surface defect detection that enhances accuracy and noise resistance.
  • To address the limitations of existing methods in handling environmental noise and detecting low-contrast, minute defects.
  • To provide a cost-effective and efficient solution suitable for large-scale mass production.

Main Methods:

Keywords:
defect detectioneffective information gainimage processinginformation entropy

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  • A novel detection scheme (WOMC) integrating information entropy constraints into traditional image processing.
  • Noise suppression via entropy-weighted Hough transform during calibration.
  • Contour localization using Canny edge detection and local entropy filtering.
  • Defect recognition through Hu similarity matching and entropy difference verification.

Main Results:

  • Achieved an average improvement of 35.77% in image clarity compared to traditional methods.
  • Demonstrated a 2.25-fold increase in detection rate under Gaussian and salt-and-pepper noise.
  • Reported high performance metrics: 96.67% accuracy, 97.32% recall, 3.31% false positive rate, and a 96.99% comprehensive performance score.
  • Successfully detected low-contrast and minute defects with improved robustness.

Conclusions:

  • The proposed WOMC method offers superior performance in optical module surface defect detection, particularly in noisy environments.
  • This approach provides a viable, low-computing-cost alternative to deep learning models for mass production.
  • The method is highly accurate, reliable, and suitable for the stringent requirements of industrial-scale manufacturing.