Abstract: In the fields of industry, nuclear facilities, oil and gas, pipe is commonly used as the means of
material delivery. And it is easy to appear various defects. The traditional manual detection system has
the disadvantages of low accuracy, low efficiency and high cost. The digital image processing
technology can automatically detect and classify the pipe image, thus effectively overcoming the above
shortcomings. First, image enhancement, image segmentation, mathematical morphology and boundary
tracking are used for image preprocessing. Then, after extracting the size, shape and texture features of
the defective area, we choose the circularity, convexity, eccentricity, entropy, correlation and cluster
tendency as the feature vector. Finally, K-means clustering analysis based on particle swarm
optimization and statistical pattern recognition classifier is used for classification. Using the image
preprocessing algorithm in this paper, we can successfully extract the pipe defects and achieve the
purpose of automated pipe defect detection. K-means clustering analysis based on particle swarm
optimization successfully clusters the pipe defect images into three categories which are crack defects,
pipe joint defects and hole corrosion respectively. Compared with the traditional K-means algorithm, K-means clustering analysis based on particle swarm optimization can increase clustering accuracy by 9%,
16.7% and 12.5% respectively. The clustering analysis based on particle swarm optimization and the
statistical pattern recognition classifier is used to classify the pipe defects. The classification accuracy of
the three types of defects is more than 80%. The accuracy of pipe joint defects and hole corrosion is more
than 90%. In summary, an integrated algorithm scheme for automated pipe defect detection and
classification based on digital image processing technology is proposed. The experiments show that the
algorithm scheme has the characteristics of high degree of automation, high versatility and accuracy.

Key words: pipe defect detection, image processing, particle swarm optimization, cluster analysis, statistical pattern recognition