Defect features recognition in 3D industrial CT images

Due to the limitations of production conditions, there is a certain probability that workpiece product has internal defects, which will have a certain impact on the performance of workpiece. Therefore, the internal defects detection of workpiece is essential. This study proposed a defect recognition method based on industrial computed tomography (CT) image to identify the internal defects of workpiece. The block fractal algorithm was used to locate the defect parts of the image, then the improved k-means clustering algorithm was used to segment the defect parts, and feature vector was extracted by Hu invariant moments. Finally, the firefly algorithm and radial basis function (RBF) neural network were combined to identify the defect. It was found from the experiments that the algorithm in this study had the accuracy of 97.89%, which proved the reliability of the algorithm and provided some suggestions for the defects recognition.