Making Autonomous Stores Smarter (MASS): A Practical Solution to Improve Product Detection Performance Using Synthetic Dataset at Scale

Product detection in large retail stores requires extensive annotated real data, which is expensive and lacks adaptability when new products are introduced. This paper presents an end-to-end product detection approach using domain randomization to generate large synthetic datasets for training. We propose a set of randomizations at the product and scene level and a method for generating large amounts of domain-randomized synthetic data. To evaluate performance on this dataset, we propose a pipeline where a model is pre-trained on simulation data and fine-tuned on small amounts of real data. To further close the domain gap, we use CycleGAN-based domain adaptation to generate photorealistic synthetic data for product classification. Our multi-class product detection solution achieves a 64% increase in mAP@0.5 when training on 20 times fewer real images, and our product classification solution achieves a 10.3% increase in Top-1 accuracy and a 4.92% increase in Top-5 accuracy when training on ten real samples per product. The results show that generating domain-randomized synthetic datasets is an accurate and scalable approach to product detection in stores and can aid autonomous store deployments by decreasing the labeling effort while increasing the accuracy of initial product classification.