An Automating Interpretation System of Industrial Radiographic Digital Images Used in Nondestructive Testing

Abstract

This thesis presents a method for automating the interpretation of industrial radiographic digital images used in nondestructive testing of subsurface defects. The goal of this study is to develop a system for detecting and identifying defects in welding processes from digital radiographic images. The proposed approach consists of three main stages: digital image processing, feature extraction, and pattern recognition. Twelve features were selected in a process to classify welding defects. Three well-known classifiers were applied in the stage of the classification process: Support Vector Machine (SVM), k-nearest neighbor (KNN) and artificial neural networks classifiers (ANN). A confusion Matrix was used to analyze the performance of the methods. Numerical experimental results confirmed the reliability and feasibility of the proposed model for detecting and locating and separating defect from non-defect indications.