Exploring the use of synthetic training data for the classification of electronic components in Artificial Intelligence systems

Artificial Intelligence (AI) is only as good as its training data. Large training sets with variants on the same classifier improve AI performance and accuracy, especially in image processing systems. Obtaining these large amounts of training data required for training AI and deep neural networks, is labor-intensive, expensive and in some cases not possible. This article explores creating a synthetic image dataset of basic electronic components by using the Blender 3D software package to automatically generate large amounts of synthetic images and image augmentation to expand the synthetic dataset. A YOLOv5 classifier model was trained on the resulting synthetic data, and the performance of the model was evaluated using a set of real-world and synthetic testing images. The results show that good-quality synthetic data that accurately represent real-world electronic components can be used to successfully train a deep learning classifier, leading to cost and time savings in the data acquisition process. However, it also shows that synthetic data that does not accurately represent real-world electronic components is of no use and will reduce the overall performance of the classifier.