Recognition of car parts in automotive supply chains by combining synthetically generated training data with classical and deep learning based image processing
Axel Börold, Michael Teucke, Johannes Rust, Michael Freitag · Procedia CIRP · 2020
In the automotive industry, the ”completely knocked down” (CKD) business requires the identification and verification of a large number of unmarked parts. This is often a manual activity with a high risk of failure. Deep learning methods offer flexibility and a useful distinction between object classes for the automatic classification. However, these methods require large amounts of labeled training data and are sometimes inaccurate. In comparison, classical algorithms can achieve higher accuracy, but are inflexible and therefore unsuitable. This paper presents an approach to increase process reliability while alleviating the data labeling effort by using synthetic training data from spatially scanned and virtualized parts and combining deep learning and classical algorithms. The result is a flexible, sensor-independent, and autonomously trained classification system.