Crack Detection Of Industrial CT Image Based On Improved Ridglet Transform

Fatigue failure is a common failure mode of mechanical and structural parts, accounting for mechanical accident more than 50%. Therefore the fatigue damage is an important research area of mechanical reliability. The fatigue failure is mainly due to the existence of undetected defects (cracks). Computer tomography (CT) is an important non-destructive testing techniques (NDT). When detecting the quality of products, CT technology can obtain the sectional images and three- dimensional images, it can give the detailed information of the internal dimensions. CT is non-destructive, direct and accurate, so it is widely used in many areas. In this paper, we do edge extraction for cracks in industrial CT images, it is the foundation of crack measurement and automatic identification.Wavelet analysis have made widely progress in the field of signal processing since the mid-80s of the last century, wavelet multi-resolution analysis thought and method have many successful and extensive applications in numerical calculation and signal processing and many other fields. On the basis of wavelet theory, between 1998 and 1999, E. J. Candès and D.L.Donoho set up a multi-scale method which was particularly suited to express the singularity——ridgelet transform. ridgelet is obtained by adding a direction parameter on wavelet function,so it not only has partial time-frequency analysis capability like wavelet, but also has strong direction selection and identification capabilities,it can effectively express the signal characteristics with a singular direction.Ridgelet transform translate line singularity to point singularity through Radon transform, but can not effectively deal with the curve singularity in the image; On the basis of ridgelet transform, monoscale ridgelet transform divide the image into some small parts, then do ridgelet transform on each part, it can deal with the curve singularity effectively, but the sizes of the small parts are fixed, it can not adapt the change of the curvature. In this paper, on the basis of monoscale ridgelet transform, an adaptive segmentation ridgelet transform was researched and used into the crack detection in actual industrial CT images. The experiments show that this method can effectively obtain exact and independent crack edge. Compared with obtaining crack area through doing ridgelet transform directly on the original image, this method can obtain more adjacent crack area, and it is more propitious to the measurements of the curving crack length and width. The cracks in workpiece are mainly three-dimensional cracks, which usually form a fracture surface. In allusion to the three-dimensional cracks in the workpiece, in this paper, we research a method to detect the three-dimensional cracks. First do ridgelet transform on the three-dimensional image data to gain the direction and the range of the cracks, then do image skeleton extract and edge extract on the two-dimensional cracks. The experiments show that this method can effectively obtain the crack edge of the three-dimensional cracks. For the small cracks which can not be detected by doing ridgelet transform directly on the two-dimensional cracks, the method in this paper can effectively obtain the edge of the small cracks.