@Article{cmes.2026.085102,
AUTHOR = {Asim Niaz, Muhammad Umraiz, Syed Farhan Alam Zaidi, Kwang Nam Choi},
TITLE = {ViLoc-Net: Leveraging Synthetic Defect Generation and Vision Transformers for Industrial Surface and Texture Anomaly Detection},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/27770},
ISSN = {1526-1506},
ABSTRACT = {Automated visual inspection in industrial settings often struggles with limited defect data and poor generalization to unseen anomalies. To overcome this challenge, we propose a hybrid anomaly detection pipeline, which integrates embedding-based, reconstruction-based, and self-supervised learning approaches. The framework also proposes a new Realistic Industrial Defect Synthesis (RIDS) module that synthesizes structured and textured synthetic anomalies based on the target masks, composite maps, and blending techniques. This helps to learn from pseudo-labeled data without the need for large annotated datasets. The pipeline further includes ViLoc-Net, a Vision Transformer-based localization network that obtains global features and then reconstructs detailed segmentation maps by a multi-scale decoder. The method is tested on various industrial datasets, demonstrating high accuracy, robustness, and generalization, making it suitable for real-world inspection tasks.},
DOI = {10.32604/cmes.2026.085102}
}