A research team from Skoltech and other institutions pioneered a new fast way to distinguish weighted goods at a supermarket. Unlike existing systems, the algorithm will make neural network training faster when new types of produce arrive. The paper is published in the IEEE Access journal.
Shops continue introducing technologies that seek to improve staff performance and accelerate the process of weighing goods and paying for them. While at some supermarkets customers have to remember a code and weigh goods in the section, in other shops it is typically provided by cashiers at the checkout — they either have to identify the type of fruits or vegetables themselves or ask the customer. At self-checkouts with scales, consumers also have to remember codes. Also, it is difficult to ensure that customers weigh the right type of produce. Skoltech researchers suggest simplifying the process through the computer vision system.
According to the research team, existing instruments have a number of disadvantages: “The difficulty is that there are many visually similar fruits or vegetables at the supermarket, and new types often appear. Classical computer vision systems need to be retrained every time the new variety is delivered. It is time-consuming because we have to collect a lot of data and then label it manually,” explains the leading author of the study, Software Engineer and PhD student from the Wireless Center at Skoltech Sergey Nesteruk.
The PseudoAugment approach allows tuning the neural network for new classes without the extensive process of collecting and labeling data. The system can be configured even before new goods appear on a store shelf.