Retail Shelf Management Software Based on Computer Vision

 

Unicsoft built product recognition app to streamline merchandising process in retail stores

About the Client

Client is a multinational company operating shelf revision in supermarkets. They requested our help to automate product detection and recognition on their shelves.

The main features of the tool developed are:
  • TensorBox framework with inception v4 model for regression and classification problems.
  • OpenCV for visualization of the results, training set labelling tasks.
  • Deployment to the Google ML Engine for distributed training of the model.
  • Cascade recognition model to be able to recognize dozens of brands and subbrands among products.

The framework for object recognition is able to work on Google Cloud. Labelling framework for multiple purposes was built using OpenCV. It allows for a full cycle of creation, verification and testing of the training set of labelled images, each including about 200 objects.

This tool helps to recognize goods on supermarket shelves. By “digitizing the shelf,” the client is able to get role-based insights on a huge array of retail metrics that tell them exactly what’s happening on-shelf and what to do to ensure the best shopping experience and drive better sales.

Technology & Tools:

Amazon
OpenCV
Python
SciPy
Ubuntu
Google ML Engine
Google Storage
Tensorfow
Cython
TensorBox
Artificial Intelligence
Image Recognition
Computer Vision
Let’s embed computer vision into your shelf monitoring software together!
Contact the expert

More projects

Business Intelligence App for the Automotive Dealerships
Reporting& Data Analyses tool that enhances the efficiency of an analytical system
AI-INTEGRATED CLOUD-BASED PLATFORM
Predictive remote patient monitoring software