Synthetically Generated Images for Industrial Anomaly Detection

Automation of inspection for quality control is needed to overcome the errors and delays inherent in manual processes. Machine learning methods have the potential to greatly improve automated inspection. However, machine learning techniques require training data that are precisely labeled and reflect the distribution of defects to be detected. Physically collecting suitable training data requires significant time and prolongs the overall time for system development. To address this challenge, a new study is presented that explores synthetic data generation for a state-of-the-art anomaly detection (AD) model in the electric motor housing (EMH) surface inspection. The study successfully demonstrates using synthetic data for anomaly detection and presents a comparison of detection performance by models trained solely on synthetic data and models trained on both synthetic and real data. The study shows that real data combined with synthetic data can increase overall model performance. The study also addresses current challenges in using synthetic data and proposes directions for future work.