Defect Datasets

Defect datasets are crucial for training robust machine learning models for automated defect detection in various manufacturing processes, addressing the challenges of limited and expensive data acquisition. Current research focuses on generating synthetic defect data using generative models like diffusion probabilistic models and StyleGAN, alongside developing efficient and scalable model architectures such as lightweight convolutional neural networks and transformer-based approaches for improved accuracy and speed. These advancements are significantly impacting industrial quality control by enabling faster, more accurate, and cost-effective defect detection across diverse applications, from semiconductor manufacturing to metal surface inspection.