Procedural synthetic training data generation for AI-based defect detection in industrial surface inspection

Abstract

Supervised machine learning methods are increasingly used for detecting defects in automated visual inspection systems. However, these methods require large quantities of annotated image data of the surface being inspected, including images of defective surfaces. In industrial contexts, it is difficult to collect the latter since acquiring sufficient image data of defective surfaces is costly and time-consuming. Additionally, gathered datasets tend to contain selection-bias, e.g. under representation of certain defect classes, and therefore result in insufficient training data quality. Synthetic training data is a promising alternative as it can be easily generated unbiasedly and in large quantities. In this work, we present a procedural pipeline for generating training data based on physically based renderings of the object under inspection. Defects are being introduced as 3D-models on the surface of the object. The generator provides the ability to randomize object and camera parameters within given intervals, allowing the user to use the domain randomization technique to bridge the domain gap between the synthetic data and the real world. Experiments suggest that the data generated in this way can be beneficial to training defect detection models.