Related Experiment Video
Updated: Jan 29, 2026
10:05
Metal Corrosion and the Efficiency of Corrosion Inhibitors in Less Conductive Media
Published on: November 3, 2018
18.5K
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
Summary
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.
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.
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