AI models have tremendous cost saving potential. Many industries are waking up to the capacity of smart systems to prevent loss, reduce staffing costs and improve the customer experience. The retail sector is particularly suited to realizing the cost savings of AI through the development of smart checkouts and cashierless stores. AI developers are creating models that can recognise objects reliably and streamline the shopping experience.
The AI teams behind these promising applications need precisely annotated images and video for AI training. However, it can be difficult for AI companies to access high quality labeled data without distracting from their core research goals. The right annotation tool can make the process of creating datasets for retail AI more manageable of AI development teams.
In today’s blog we will look at some of the exciting use cases that are saving retailers money now and in the future. Then we will identify the key features to look out for when selecting an annotation tool for image and video labeling.
Self-checkouts are convenient for customers and allow retailers to reduce staffing costs. However, self-checkouts are also vulnerable to theft and accidental miss-scanning. By inputting incorrect barcodes and scanning cheaper items shoppers can (intentionally or accidentally) pay less than items are worth. Over time this represents a significant loss for companies.
Smart checkouts can improve this situation by monitoring purchases in real time and alerting staff and security to incorrectly scanned items. Smart checkouts are capable of identifying each item as it passes in front of the checkout screen. This leads to more accurate bills and reduces cost for retailers. Precisely annotated training data improves the performance of object recognition.
Missing products and empty shelves can lead to dissatisfaction for shoppers and represent an inefficient allocation of resources. However, it requires a significant investment in staff to guarantee that shelves are kept reliably stocked. AI can be used to automate inventory management in supermarkets. Cameras can monitor storage spaces and alert staff when certain products are running low.
Object recognition allows these AI systems to identify when individual products are missing. Integrated inventory management systems can help retailers to reduce costs whilst securing customer satisfaction. Instance segmentation annotation helps AI models to count and distinguish between individual items.
Cashierless stores are the ultimate in retail automation. They allow customers to select items and leave without passing through a checkout. This provides consumers with a frictionless shopping experience and saves retailers significantly when it comes to staffing costs. For cashierless stores to function facial recognition AI is required.
AI models can recognise individual customers and automatically charge them for items selected when they leave. Point annotation is vital for well functioning facial recognition models. By locating key features in training images point annotation allows AI models to recognise the unique arrangement of each person's face.
Annotation tools help control costs in retail
The use cases mentioned above can reduce costs for retailers. The right annotation tool can help Ai developers to access datasets that produce high performing models. Keylabs is an annotation tool that gives AI teams a range of options for labeling projects.
Keylabs boasts unique analytics capabilities that allow managers to assign annotation tasks to high performing workers. And Keylabs speeds up annotation with efficient hotkeys and high performing interpolation features. Annotation tools can transform AI projects and give today’s retailers a vital edge.