Automated Defect Recognition in X-ray Projections Using Neural Networks Trained on Simulated and Real-World Data

In this contribution, we investigate a methodology based on neural networks for efficient learning of light metal castings for defect detection in X-ray imaging. The motivation comes from the high effort in time and costs which is currently required to set up new objects or parts. To overcome this drawback, on the one hand, we want to reduce the complexity for the user by applying neural networks for defect detection. On the other hand, we try to use as much data from the simulation as possible instead of real data which is costly to collect. The performance of the investigated approaches is evaluated using real-world data from wheel inspection. We show that training on simulated data only is inferior to training with costly real world data. Combining both types results in the best performance and the closer the simulated data matches the real-world, the better the performance.