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Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine

Nhat-Duc Hoang1, Van-Duc Tran2

  • 1Lecturer, Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, R.809-No.03 Quang Trung, Da Nang 550000, Vietnam.

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Summary
This summary is machine-generated.

This study introduces an automated method using image processing to detect pipe corrosion, improving building maintenance efficiency. The developed model achieved 92.81% accuracy in identifying corroded pipes, offering a promising tool for surveys.

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Area of Science:

  • Engineering
  • Computer Science
  • Materials Science

Background:

  • Building serviceability relies on functional water supply and waste disposal systems.
  • Manual pipe corrosion surveys are time-consuming and labor-intensive.
  • Accurate and timely detection of pipe surface corrosion is critical for maintenance.

Purpose of the Study:

  • To develop an automated image processing method for pipe corrosion detection.
  • To reduce the labor and time associated with manual pipe inspections.
  • To create a reliable tool for building maintenance agents.

Main Methods:

  • Utilized image texture analysis, including color statistics, gray-level co-occurrence matrix, and gray-level run length for feature extraction.
  • Employed a Support Vector Machine (SVM) optimized by differential flower pollination for classification.
  • Trained and tested the hybrid model on a dataset of 2000 image samples.

Main Results:

  • The proposed hybrid model achieved a high accuracy rate of 92.81% in detecting pipe corrosion.
  • Statistical validation using the Wilcoxon signed-rank test confirmed the method's suitability.
  • The automated system demonstrated effectiveness in distinguishing between corroded and intact pipe surfaces.

Conclusions:

  • The developed image processing-based method offers a promising automated solution for pipe corrosion detection.
  • This tool can significantly assist building maintenance agents in conducting efficient pipe system surveys.
  • The study highlights the potential of hybrid machine learning models in infrastructure maintenance.