Abstract
Welding defect constitute a great danger to the safe usage of petroleum pipelines. In addition, manual welding defect detection has numerous deficiencies, such as subjectivity and inaccurate estimation of geometric parameters. Thus, we proposed an automatic welding defect detection system for X-ray images. In the system, five typical defects (cracks, lack of penetration, lack of fusion, round defects, and stripy defects) and non-defects were chosen for recognition. There are three stages in the system: defect extraction, defect detection, and defect recognition. In the first stage, background subtraction with an adaptive thresholding method was adopted to identify the potential defects. In the second stage, to extract real defects from the massive number of potential defects, the adaptive cascade boosting (AdaBoost) algorithm was employed for binary classification. Grayscale features and geometric properties of the defects were extracted for the classification. In the third stage, the AdaBoost algorithm was extended for multi-classification. In the process of distinguishing defects from non-defects, a high detection rate is necessary. To ensure the high true positive rate (TPR) and the low false positive rate (FPR), we proposed the cascade AdaBoost algorithm with penalty term. The accuracy of the defect detection was 85.5%, and the TPR was 91.66%. Moreover, three comparison tests of support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were employed to validate the superiority of AdaBoost. The experimental results indicate that the proposed detection system can be effective for defect detection.
Highlights
Nondestructive testing is a widely used and important technique for defect detection in industry
We focus on five typical defects, which are cracks, lack of fusion (LOF), lack of penetration (LOP), stripy defects, and round defects
The variable true positive rate (TPR) is critical for industrial applications of automatic welding defect detection
Summary
Nondestructive testing is a widely used and important technique for defect detection in industry. F. Duan et al.: Automatic Welding Defect Detection of X-Ray Images by Using Cascade AdaBoost With Penalty Term images offers major advantages over manual work in terms of labor consumption, time consumption, digitalized archiving, and objectivity. Zapata et al [20] built a data-driven model that deals with four defects by applying multivariate statistics and machine learning methods They adopted wavelet packet decomposition principal component analysis (WPD-PCA) to extract features. Zapata et al [18] presented a model based on deep neural network used for the automatic detection of welding defects. Defects are mixed with a large number of non-defects when using only image processing methods This may result in a high rate of missing report, which is prohibited in industrial inspection.
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