Synthetic Image Data Generation for Wiring Harness Component Detection Using Machine Learning

Machine learning is a powerful tool for computer vision tasks in manufacturing, as features are automatically extracted and a high variety of components or failures are reliably detected. A focal prerequisite for high-performing machine learning models is a database that is large in quantity as well as quality, and representative for the computer vision task in the manufacturing environment. In addition, manufacturing applications require a domain-specific dataset. Thus, we generated and integrated synthetic data for object detection using convolutional neural networks, specifically for wiring harness component detection. A synthetic data generation pipeline for images was developed and implemented. Experiments were conducted to assess the domain gap between synthetic and real images and to determine factors that are beneficial to synthetic data generation. The experimental findings demonstrate relevant training approaches to integrate synthetic data, factors that have a positive impact on training, and high-performance results comparable to using real data only.