A recognition algorithm to detect pipe weld defects

Taking magnetic flux leakage (MFL) imaging of pipe weld defects as the research object, a weld defect image recognition algorithm based on greygradient co-occurrence matrix (GGCM) and cluster analysis and mathematical morphology is proposed. Recognition of different types of welding defects was achieved. Firstly, a continuous non-contact scanning MFL system for the pipe weld was used to collect the three-dimensional MFL. Secondly, the three-dimensional MFL signal was converted to a two-dimensional greyscale image. Then the MFL image characteristics of the two-dimensional grayscale image were extracted using GGCM. Based on extracted image features, the characteristic quantity was analysed by using k-means clustering and then through the combination of histogram equalization, Otsu's method of binaryzation, morphologically removing small objects, edge detection, and then structuring a morphologically optimized edge extraction method for edge detection on the grayscale. Through combination of several methods, a new algorithm to improve the detection effect was structured. The results indicated that this algorithm is adaptable and practical. This algorithm solved difficulties associated with the MFL method being used in the weld testing to realize the recognition of pipe weld defects and break through the applicable limitations of traditional signal processing technology.