Creating Synthetic Training Data for Machine Vision Quality Gates

Abstract Manufacturing companies face the challenge of combining increasing productivity and quality standards with customer″​=oriented mass production. To achieve the required quality standards, quality controls are carried out after selected production steps. These are often visual inspections by trained personnel based on checklists. To automate visual inspection industrial, cameras and powerful machine vision algorithms are needed. Large amounts of visual training data are usually required in order to train these algorithms. However, collecting training data is time″​=consuming, especially in customer″​=oriented mass production. Synthetic training data generated by CAD tools and rendering software can alleviate the lack of available training data. Within the paper at hand, a novel approach is presented examining the use of synthetic training data in machine vision applications. The results show that synthetically generated training data used to train machine vision quality gates is fundamentally suitable. This offers great potential to relieve process and productions developers in the development of quality gates in the future.