ViLoc-Net: Leveraging Synthetic Defect Generation and Vision Transformers for Industrial Surface and Texture Anomaly Detection

Asim Niaz^1,#, Muhammad Umraiz^2,#, Syed Farhan Alam Zaidi³, Kwang Nam Choi^2,*
1 Ludwig-Maximilians-Universität München, Munich, Germany
2 Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
3 Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), The University of Osaka, Suita City, Osaka, Japan
* Corresponding Author: Kwang Nam Choi. Email: email
# Asim Niaz and Muhammad Umraiz contributed equally to this work

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.085102

Received 05 May 2026; Accepted 15 June 2026; Published online 31 July 2026

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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.