An image expansion method based on Wasserstein generative adversarial network for surface defects of fair-faced concrete (preprint)
Abstract
The research presents a valuable contribution to the detection of surface defects of fair-faced concrete through proposing an image augmentation method with a Wasserstein generative adversarial network. Integrating a self-attention mechanism and gradient penalty, the algorithm substantially mitigates data scarcity by generating high-quality and diverse defect images. Trained by data collected from five structures, the model improves recognition accuracy, which can serve as an effective approach for small sample learning in the analysis of concrete defects.
Fields of Study & Subjects
Infrastructure Maintenance and Monitoring
Generative Adversarial Networks and Image Synthesis
Industrial Vision Systems and Defect Detection
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 1 March 2026
- Language
- EN
- Document type
- Preprint