TY  - EJOU
AU  - Niaz, Asim 
AU  - Umraiz, Muhammad 
AU  - Zaidi, Syed Farhan Alam 
AU  - Choi, Kwang Nam 

TI  - ViLoc-Net: Leveraging Synthetic Defect Generation and Vision Transformers for Industrial Surface and Texture Anomaly Detection
T2  - Computer Modeling in Engineering \& Sciences

PY  - 
VL  - 
IS  - 
SN  - 1526-1506

AB  - 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.
KW  - Image anomaly; industrial anomaly; vision transformers; surface anomaly detection

DO  - 10.32604/cmes.2026.085102