Texture analysis in corrosion management: A scoping review

Corrosion-induced failures result in economic losses exceeding 3-4% of GDP annually across developed nations, necessitating advanced detection and monitoring methodologies. Texture analysis techniques have emerged as powerful tools for automated corrosion assessment, evolving from traditional statistical descriptors to sophisticated deep learning approaches. This scoping review systematically maps the landscape of texture analysis methodologies applied to corrosion detection, monitoring, and management across industrial sectors, identifying current capabilities, limitations, and research gaps. Following PRISMA-ScR guidelines, a comprehensive search across IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and ACM Digital Library for literature published between 2010-2025 was conducted. Search terms encompassed texture analysis methods (GLCM, LBP, HOG, wavelet transforms, CNN-based approaches) combined with corrosion-related keywords. A total of 127 relevant studies were identified, spanning traditional texture descriptors, hybrid approaches, and deep learning methods, which was further filtered down to 25 representative studies. Performance metrics ranged from 78-98% accuracy, with CNN-based methods showing better performance in complex industrial environments. Traditional texture analysis methods such as GLCM and LBP continue to perform adequately in controlled settings but fall short in complex industrial scenarios compared to CNN-based approaches. Hybrid methodologies that blend traditional texture descriptors with deep learning show promise by balancing accuracy and computational efficiency.