Real-time supermarket product recognition on mobile devices using scalable pipelines

The recognition of supermarket products on mobile devices is gaining importance as more and more consumers seek to make informed decisions about their purchases in real time. However, the realization is often difficult due to the vast product assortments of modern supermarkets and the limited computational resources available on mobile devices. In this work, we propose a real-time on-device product recognition pipeline, based on the Global Trade Item Number (GTIN) system, that is both robust to dynamic changes in the product assortment and scalable to tens of thousands of products. We evaluate detection performance on SKU110k and R6k datasets and demonstrate the scalability of our pipeline with 5974 different products, using synthetic data. Furthermore, the proposed product recognition pipeline is deployed on a Google Pixel 6 mobile phone, where it achieves an inference time of 121ms (8.3fps), demonstrating its real-time capabilities in practice.