ProGAN-Based Synthetic Image Generation for Industrial Visual Inspection of Metallic Parts
To address this gap, we study Generative AI-driven synthetic image generation using Progressive Growing GANs (ProGAN).
ProofPaper ↗
Key points
- Automated visual inspection is essential for efficient repair and recycling of metallic components.
- AI-based defect detection is a strong fit.
- We implement a ProGAN pipeline with Wasserstein loss and gradient penalty and evaluate key hyperparameters on two public datasets, MVTec AD (metal nut) and MPDD (tube).
- These images can augment training data, improve robustness, and reduce the cost of collecting rare defects.
Sources (1)
- [1]ProGAN-Based Synthetic Image Generation for Industrial Visual Inspection of Metallic PartsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 07:14 AM
To address this gap, we study Generative AI-driven synthetic image generation using Progressive Growing GANs (ProGAN).
Automated visual inspection is essential for efficient repair and recycling of metallic components.
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