Application of Pseudo-color Image Feature-Level Fusion in Nondestructive Testing of Wire Ropes

Abstract

Among the many nondestructive testing methods of ferromagnetic materials, magnetic flux leakage (MFL) testing is a very mature technology. Due to the single source of target information in traditional MFL detection technology, the recognition accuracy is low. Infrared image is a color artificially given, so it is also a pseudo-color image. We use the feature-level image fusion technology to fuse color infrared images and pseudo-color MFL images and quantitatively identify the defect feature vectors after fusion. A wavelet noise reduction algorithm is used to perform noise reduction processing on the MFL signal and used the pseudo-color transform method to convert the noise-reduced MFL signal into a pseudo-color image. For the collected infrared images of the wire rope, we use an image segmentation algorithm to extract the defective parts. The color moments and texture features of the pseudo-color MFL defect image and the color infrared defect image are extracted for feature-level fusion and trained as the input of the nearest neighbor algorithm. The final experimental results prove that the fusion of the two images information has achieved good results and the defect recognition rate has been improved.