Machine Learning-Based Welding Defect Recognition Using GLCM Features and k-fold Cross-Validation: KNN and SVM Techniques

Although radiographic inspection is one of the oldest techniques for non-destructive testing, it is still considered vital in many industrial fields to ensure the quality of welds and meet the demands of work conditions and design, as well as safety and reliability requirements. This paper presents an algorithm that identifies and categorizes welding defects in radiographic images using machine learning techniques. For this aim, two supervised classifiers are proposed and performed, which are 1) K-nearest Neighbour (KNN), which is a nonparametric classifier, and 2) a multiclass classifier based on Support Vector Machine (SVM) as an intensive learning-based classifier (enthusiastically learns). SVM is commonly used in binary classification, but it can be adapted for multi-classification using various common methods, such as one-versus-one and one-versus-all. The texture features are adopted in this paper as inputs to the classifiers, where two groups of them are used: the feature of the Local Binary Pattern (LBP) and the grey-level co-occurrence extraction matrix (GLCM), to obtain the feature vector. To avoid the risk of overfitting, four k-fold cross-validations are applied. The experimental results are reported for two different classifiers, achieving an accuracy of 91.66% when combining GLCM and SVM.