A Synthetic Data Generation Framework for the Development of Computer Vision Applications in Manufacturing

Machine learning techniques are increasingly used for computer vision applications in manufacturing. Synthetic data, generated through realistic simulations, are utilized to accelerate the data collection process while optimizing accuracy and precision of ML models. However, in manufacturing there is usually the need for the development of several CV applications that support different production steps. This obstacle requires a systematic approach for generating synthetic datasets that can be used for developing effective CV systems. Hence, this work presents a pipeline for generating photorealistic synthetic datasets, using a set of digital tools such as 3D modeling, photorealistic rendering, automated labeling, and ML training tools. The proposed framework is tested and validated in a robot-assisted packaging case in the dairy industry. The industrial use case provides a pilot-level demonstration that the synthetic dataset generation framework can support the development of CV modules across several production steps and thus it can aid in accelerating commissioning and reconfiguration of industrial automation setups. Moreover, the pilot validation indicates that object detection and recognition models trained on synthetic data can provide sufficient performance for the specific requirements of the examined packaging scenario.