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Defects on the Pipeline surface such as cracks cause main problems for governments, specifically when the pipeline is covered under the ground. Manual examination for surface defects in the pipeline has several disadvantages, including varying standards, and high cost. In this paper, a combination of two algorithms based on mathematical morphology and curvature evaluation for segmentation of defects is proposed. Then, we use fuzzy k-means clustering to classify pipe defects. The proposed method can be completely automated and has been tested on more than 250 scanned images of petroleum pipelines of Iran.