Generative AI for Industrial Applications: Synthetic Dataset

Abstract

The research and development of artificial intelligence (AI) techniques to enhance quality control in industrial equipment might face challenges due to the scarcity and limited privacy of actual industrial datasets. One approach to address this involves utilizing generative AI models that create synthetic data, simulating the characteristics and diversity found in crucial datasets. We present a methodology for generating synthetic datasets for industrial products such as bolts and screws by employing a segment-anything model and a stable diffusion technique for creating accurate representations. Furthermore, we propose the developed model by using a scaled-down version of DinoV2 algorithm's vision transformer (ViT-small). The self-supervised learning approach was studied to fine-tune the model to classify between normal- and defective industrial products, as well as those contaminated with dirt. By additional training dataset created through synthesis, we achieve an improvement in performance. The synthetic data leads to nearly perfect true positive results while completely eliminating false negatives. This indicates a significant advantage in terms of accuracy, recall, precision, specificity, and F1 score, all of which exceed 98%. Similarly, the model's predictions align perfectly with the area under the curve (AUC) metric. Although there is a slight performance reduction when dealing with up to six different class labels, the model retains strong capability in identifying normal products. Notably, the ViT-Small-based self-supervised learning model demonstrates superior accuracy compared to using ViT-Base, with considerations for dataset compatibility and model suitability. In conclusion, this study's contribution lies in enabling the deployment of the Dino V2 model for implementing quality control measures in industrial domains. It emphasizes the challenges that limited real-industrial data by leveraging synthetic data and innovative fine-tuning approaches, ultimately enhancing AI-powered for quality control processes.