Industrial Object Detection: Leveraging Synthetic Data for Training Deep Learning Models

The increasing use of synthetic training data has emerged as a promising solution in various domains due to its ability to provide accurately labelled datasets at a lower cost compared to manually annotated real-world data. In this study, we investigate the use of synthetic data for training deep learning models in the field of industrial object recognition. Our goal is to evaluate the performance of different models trained with varying ratios of real and synthetic data, with the aim of identifying the optimal ratio that yields superior results. In addition, we investigate the impact of introducing randomisation into the synthetic data on the overall performance of the trained models. The results of our research contribute to the understanding of the role of synthetic data in industrial object detection.