Development of an Adaptive Entropy-Based GLCM Feature Extraction Method for Defect Detection in Welding Images

Abstract

Quality inspection of welding joints is fundamental to ensuring structural integrity and reliability, particularly in high-risk industrial sectors. The use of image processing for welding defect detection offers a more objective and consistent approach than visual interpretation; however, the performance of detection systems strongly depends on the ability of feature extraction methods to represent image texture characteristics. Gray Level Co-occurrence Matrix (GLCM) is a widely used texture feature extraction method because it can quantitatively and interpretably represent spatial relationships among gray levels. Nevertheless, static GLCM configurations may limit its representational capability when images exhibit low contrast, non-uniform intensity distributions, or small and complex defect characteristics. This study aims to develop an adaptive entropy-based GLCM feature extraction method through the integration of Adaptive ROI, ROI Entropy, and Feature Fusion. The development adopts a Research and Development (R&D) approach. The GLCM-Entropy method was subsequently tested and compared with GLCM Before Development using 500 radiographic images obtained through X-ray inspection and evaluated using a confusion matrix. The test results show that GLCM Before Development achieved an accuracy of 91.40%, whereas GLCM-Entropy achieved 96.20%, with F1-scores of 87.09% and 97.42%, respectively. These results demonstrate that the integration of global and local texture information in GLCM-Entropy improves classification performance compared with GLCM Before Development.