Enhanced Metal Surface Defect Localization with SH-SAM and Grey Level Co-Occurrence Matrix (GLCM) in Robotics Automation
Year: 2025
Type: article
Abstract: The identification of metal surface defects is essential for many sectors, including aerospace and automotive, in the production of quality products. Traditional approaches are time-consuming and error-prone and, hence, an automated, accurate fault localisation solution in robotic automation is necessary. This research aims to enhance the defect detection process by integrating SH-SAM with GLCM for enhanced precision and robustness in robotic automation. We therefore aim at developing a whole system that integrates texture with spectral analysis for accurate identification of defects. The proposed method integrates Supervised Hyperspectral Anomaly Detection (SH-SAM) with a grey-level co-occurrence Matrix (GLCM) to analyze spectral anomalies and texture patterns on metal surfaces. This integration enhances the discovery of flaws by robotic automation. The approach that combines SH-SAM and GLCM performed better compared to any of the methods individually on F1 score (88.5%), accuracy (92%), and precision (89%). It also outperformed all the others in defect localization as RME was decreased to 9.3%. The integration of SH-SAM and GLCM offers a highly effective solution for the localisation of metal surface defects with improved accuracy and reduced errors. This method shows great potential for real-time robotic automation in metal surface inspection applications.
DOI: 10.5875/mx8d9844
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411334870