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A Study of Defect Detection Techniques for Metallographic Images.

Wei-Hung Wu1, Jen-Chun Lee2, Yi-Ming Wang1

  • 1Department of Mechatoronics Engineering, National Changhua University of Education, Changhua City 50007, Taiwan.

Sensors (Basel, Switzerland)
|October 2, 2020
PubMed
Summary

This study introduces a new deep learning model, Multi-scale ResNet (M-ResNet), for automatic metallographic defect detection. The M-ResNet achieves high accuracy, improving upon existing methods for analyzing metal and alloy structures.

Keywords:
convolutional neural networkdeep learningmetallographic analysisresidual neural network

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Area of Science:

  • Materials Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Metallography analyzes metal and alloy structures, aiding in identification, quality control, and defect characterization.
  • Current metallographic defect detection relies heavily on human experts, presenting a significant challenge for automation.
  • Deep learning, particularly convolutional neural networks, has shown promise in computer vision tasks.

Purpose of the Study:

  • To develop a novel deep learning architecture for automated metallographic defect detection.
  • To improve the accuracy and efficiency of identifying defects in metallographic images, especially small objects.
  • To propose a practical system for real-world metallographic analysis applications.

Main Methods:

  • A modified Residual Network (ResNet) architecture was developed, termed Multi-scale ResNet (M-ResNet).
  • The M-ResNet incorporates multi-scale operations to enhance the detection of objects across various sizes.
  • The model was trained and evaluated for its performance in recognizing defects in metallographic images.

Main Results:

  • The proposed M-ResNet achieved a mean Average Precision (mAP) of 85.7% in recognition performance.
  • This accuracy surpasses that of existing methods for metallographic defect detection.
  • The multi-scale approach proved effective for detecting small objects within the images.

Conclusions:

  • The developed M-ResNet offers a robust and accurate solution for automatic metallographic defect detection.
  • This deep learning approach addresses the limitations of manual analysis in metallography.
  • The system has significant potential as an application for enhancing metallographic analysis.