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01-01-2021 | RADIO WAVE METHODS | Issue 1/2021

Russian Journal of Nondestructive Testing 1/2021

Patch-Based Weld Defect Segmentation and Classification Using Anisotropic Diffusion Image Enhancement Combined with Support-Vector Machine

Journal:
Russian Journal of Nondestructive Testing > Issue 1/2021
Authors:
Reza Faghihi, Mohammadjavad Faridafshin, Amir Movafeghi

Abstract

Several factors such as low image quality, limited human eye resolution, and biased interpretation make the manual radiography testing defect detection erroneous. The aim of this paper is to design a semi-automatic but accurate feature extraction and classification framework, using radiography images of welded joints represented in the GDXray image database. We have trained our support-vector machine classifier with crack, porosity and lack of penetration as the three more frequent classes of radiography defects. The images are segmented after binarization followed by a two-stage image enhancement technique. The body of the two-stage method is made up of an anisotropic diffusion Gaussian filtering, morphological edge detection, and low-pass Gaussian filtering. A three-class support-vector machine based on One-vs.-All implementation of the binary support-vector machine is created, trained, and tested. The method also involves manual adjustments which makes it semi-automatic, and compared to other studies, it is proved to work best with GDXray, a freely available comprehensive radiography image database. The effect of incorporating different optimization algorithms for solving the inherent optimization problem in support-vector machine theory, and utilizing various image processing techniques on defect detection is studied. We show that the combination of image processing and support-vector machine would result in a better performance than the previous studies using the same database.

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