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Metal Corrosion and the Efficiency of Corrosion Inhibitors in Less Conductive Media
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Computer Vision-Based Corrosion Detection and Feature Extraction for Rock Bolts.

Shucan Lu1, Saisai Wu1, Xinxin Ma1

  • 1Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering, School of Resources Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Materials (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for detecting rock bolt corrosion using image processing and deep learning. The technique accurately quantifies corrosion, enhancing structural safety and maintenance strategies.

Keywords:
computer visioncorrosion detectiondeep learningfractal dimensionimage processing

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Area of Science:

  • Engineering
  • Materials Science
  • Computer Science

Background:

  • Rock bolt corrosion poses significant risks to engineering safety and infrastructure longevity.
  • Effective monitoring and maintenance are crucial for mitigating these risks.

Purpose of the Study:

  • To develop an integrated approach for automatic rock bolt corrosion detection.
  • To enhance the accuracy and reliability of corrosion assessment using advanced computational methods.

Main Methods:

  • Utilized computer-vision object-detection algorithms with a Feature Pyramid Network for enhanced multi-scale detection.
  • Employed image binarization and grayscale matrix analysis for feature extraction.
  • Applied Fractional Brownian Motion modeling for corrosion morphology simulation and fractal feature calculation.

Main Results:

  • The automated model accurately identified and quantified corrosion features, including pitting density.
  • Fractal dimension significantly increased with corrosion time, serving as a reliable indicator of surface roughness and corrosion rate.
  • The study validated the accuracy of fractal feature calculations through morphology simulation.

Conclusions:

  • The integrated framework combining image processing, deep learning, and fractal theory provides a robust solution for rock bolt corrosion monitoring.
  • This approach offers a reliable quantitative method for assessing metal corrosion, improving maintenance strategies and ensuring engineering safety.