Automatic defect identification technology of digital image of pipeline weld

Abstract

Digital image of pipeline weld is an important basis for the reliability management of pipeline welds. However, the error rate of artificial discrimination is high. In order to increase the defect identification accuracy of digital image of pipeline weld, we adopted several methods (e.g. multiple edge detection, detection channel and threshold segmentation) to carry out image processing on the image defects of pipeline welds. Then, a defect characteristic database on the digital images of pipeline welds was constructed, including grayscale difference, equivalent area (S/C), circularity, entropy, correlation and other parameters. Furthermore, a multi-classifier construction (SVM) model was established. Thus, the classification and evaluation on the defects in the digital images of pipeline welds were realized. Finally, an automatic defect identification software for digital image of pipeline weld was developed and verified on site. And the following research results were obtained. First, after image processing, the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise. In the case of noise, however, pseudo-edge emerges in the detection results. In this case, the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold. Second, there are 14 parameters in the defect characteristic database, including shape characteristic, lamination characteristic and image length pixel. Third, by virtue of the SVM classification model, the shape characteristics of each type of defect can be clarified, and the defect characteristics can be identified, such as crack, slag inclusion, air hole, incomplete penetration, non-fusion and strip. Based on field application, the following results were obtained. First, this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds. Second, its identification accuracy is higher than 90%. Third, by virtue of this technology, automatic defect identification and evaluation of digital image of pipeline weld is realized. In conclusion, these research results help to ensure the safe operation of pipelines. Keywords: Pipeline weld, Ray film, Digital image, Defect database, SVM classification model, Defect identification, Automatic identification, Software development