The collection and annotation of large-scale image datasets remains a significant challenge in training vision-based AI models, especially in domains such as industrial automation. In industrial settings, this limitation is especially critical for quality inspection tasks within Flexible Manufacturing Systems and Batch-Size-of-One production, where high variability in components restricts the availability of relevant datasets. This study presents a pipeline for generating photorealistic synthetic images to support automated visual inspection. Rendered images derived from geometric models of manufactured parts are enhanced using a Cycle-Consistent Adversarial Network (CycleGAN), which transfers pixel-level features from real camera images. The pipeline is applied in two scenarios: (1) domain transfer between similar objects for data augmentation, and (2) domain transfer between dissimilar objects to synthesize images before physical production. The generated images are evaluated using mean Average Precision (mAP) and the Turing test, respectively. The pipeline is further validated in two industrial setups: object detection for a pick-and-place task using a Niryo robot, and anomaly detection in products manufactured by a FESTO machine. The successful implementation of the pipeline demonstrates its potential to generate effective training data for vision-based AI in industrial applications and highlights the importance of enhancing domain quality in industrial synthetic data workflows.
Nandakumar et al. (2025) studied this question.