AI Model for Retail Shelf Monitoring
Published in
2 min readApr 29, 2020
Challenges
Grocery Store or Big marts lose revenue due to 2 reasons:
- 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.
- Product Misplaced: Often misplaced product or disarranged product can cost money for business especially in high end fashion outlets where everything needs to be perfect. This links directly to customer’s sho[ping experience.
Methodology
Created a deep learning based Model to detect out of stock or misplaced product in real time. It allowed the real time monitoring and helped in pin-pointing these issue which results in better customer experience and more business for Store or Mart. The procedure implemented is as follows:
- Using a CCTV, continuous video stream is getting captured.
- The live stream is being passed to the model.
- The model using that live video predicting Out of stock and misplaced items and showing them as output.
- System alerts like SMS/ email can be triggered alerting the right person to fix the situation.
Technology Used
We used state-of-the-art learning based model and custom data-set from our nearest mart.
- Architecture: Retina-net with Resnet-101 as Backbone.
- Loss Function: Focal Loss
- Data-Set: Custom Data-set collected from 20 minutes video of our nearest local mart .
- Image Size Used: Trained on 128x128, 256x256 ,512x512 and 600x600 size images using gradual resizing to achieve higher accuracy and better generalization of the model.
*Also shared on ViHaze (My Startup).