AI Model For Retail Shelf Monitoring

Source: Deep Learning on Medium


Identifying Out-Of-Stock & misplaced/ disarranged products on Grocery Shelves in Real Time

Challenges:

Grocery Store or Big marts lose a good part of business due to 2 reasons:

  1. Product Out of Stock: Products that are out of stock on shelves, but available in the stores is a missed opportunity. Manual process of checking stock is labour intensive and time consuming.
  2. Product Misplaced: Often misplaced product or disarranged product can cost money for business specially in high end fashion outlets where everything needs to be perfect.

Our Solution:

A deep learning based Model to detect out of stock or misplaced product in real time. It allows the real time monitoring and helps in pin-pointing these issue which results in better customer experience and more business for Store or Mart.

Solution Implementation:

  1. Using a CCTV, continuous video stream is getting captured.
  2. The live stream is being passed to the model.
  3. The model using that live video predicting Out of stock and misplaced items and showing them as output.
  4. System alerts like SMS can be triggered alerting the right person to fix the situation.

Example images from our solution:

Original image (L) — Our model identifying misplaced products (R)
Original image (L) — Our model identifying misplaced products (R)
Original image (L) — Our model identifying misplaced products (R)
Original image (L) — Our model identifying misplaced products (R)

Technical Approach :

We are using state of Art Deep learning based model and using custom data-set from our nearest mart.

  1. Architecture: Retina-net with Res-net-101 as Backbone.
  2. Loss Function: Focal Loss
  3. Data-Set: Custom Data-set collected from 20 minutes video of our nearest local mart .
  4. Image Size Used: Trained on 128×128, 256×256 ,512×512 and 600×600 size images using gradual resizing to achieve higher accuracy and better generalization of the model.