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

Computational Intelligence and Neuroscience
Nhat-Duc Hoang, Van-Duc Tran

Abstract

To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems. Therefore, timely and accurate detection of corrosion on pipe surface is a crucial task. The conventional manual surveying process performed by human inspectors is notoriously time consuming and labor intensive. Hence, this study proposes an image processing-based method for automating the task of pipe corrosion detection. Image texture including statistical measurement of image colors, gray-level co-occurrence matrix, and gray-level run length is employed to extract features of pipe surface. Support vector machine optimized by differential flower pollination is then used to construct a decision boundary that can recognize corroded and intact pipe surfaces. A dataset consisting of 2000 image samples has been collected and utilized to train and test the proposed hybrid model. Experimental results supported by the Wilcoxon signed-rank test confirm that the proposed method is highly suitable for the task of interest with an accuracy rate of 92.81%. Thus, the model proposed in this study can be a promising tool to assist building maintenance agents during the phase of pipe system su...Continue Reading

References

Jul 9, 1994·BMJ : British Medical Journal·D G Altman, J M Bland
Feb 16, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·X Tang
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Apr 9, 2017·Journal of Bone and Mineral Metabolism·Muthu Rama Krishnan MookiahKarupppasamy Subburaj
Sep 12, 2018·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Bejoy Abraham, Madhu S Nair

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Citations

Jul 3, 2021·Materials·Thomas De KerfSteve Vanlanduit
Nov 6, 2020··Vsevolod VlaskineMarc Majors

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MATLAB
MATLAB Statistics and Machine Learning Toolbox
SVM
GLRL
GLCM
Visual NET
MO

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