Computer Vision for Warehouse Optimization

1.INTRODUCTION

Most of the Artificial Intelligence (AI) & Machine Learning (ML) powered solutions are based on the Sense – Think – Act paradigm. The feeds collected from the cameras installed in the warehouse often called Computer Vision can be processed using AI and Deep Learning techniques to derive intelligent insights that can be used for immediate action.

2. COMPUTER VISION

There are two terms ‘Machine Vision’ and ‘Computer Vision’ that are widely referred to while talking about innovations being introduced in business applications. If we think of machine vision as the body of a system, computer vision is the retina, optic nerve, brain and central nervous system.

Machine Vision is the ability of machines to see and act. It uses a camera to view an image whereas computer vision is the algorithms that process and interpret the image. A machine vision system requires computer vision and does not work without a computer and specific software.

Computer vision can process 3D and moving images as well and perform complex operations to detect features within an image, analyze them and provide rich insights.

The rapid increase in business solutions leveraging computer vision can be attributed to the following drivers:

  • Advances in Artificial Intelligence
  • Increase in Computational Speed
  • Decrease in the Cost of Robotics & Infrastructure
  • Increase in the Labour Cost

3. TYPICAL CHALLENGES IN A WAREHOUSE

  • Planning storage of goods in high inflow environments
  • Lack of visibility into warehouse operations leading to interleaving task delays
  • Delay in unloading/shipment due to the unavailability of dock and unloading/loading areas
  • Increase in labour cost due to manual scanning, verification, and reconciliation of goods

4. HOW COMPUTER VISION AIDED BY AI & DEEP LEARNING CAN HELP?

Warehouse operations typically have tasks that are interleaved. The tasks are assigned to different workers with different roles. Unless a task is completed by one worker, the next task can not be initiated which could lead to delays. The following section describes some scenarios where Computer Vision can be used.

4.1 Unloading

  • Tracking dock doors and unloading areas for availability
  • Inform transporters the real-time status of dock doors for reducing wait times
  • Sending alerts and updating status on WMS on truck arrival and departure

4.2 Receiving

  • Auto verification of package labels and extraction of label content to reconcile with the receipts
  • Inspection of received goods for damages
  • Provide visibility into available spaces for storage planning
  • Perform preliminary checks to ensure that put away locations are not occupied to avoid rework

4.3 Put Away

  • Guide workers to ensure the pallet is stored in the specified location
  • Capturing exact put away location
  • Real-time pallet tracking
  • Alerts on unplanned pallet movement

4.3 Pick & Pack

  • Checking availability of order items at pickup location before pick task is assigned
  • Pallet tracking to ensure right item and quantity is picked
  • Barcode scanning and item verification for reconciliation against customer orders

4.4 Shipping

  • Monitor loading area and dock door clearance for initiating shipment tasks
  • Using carton dimensions for truckload planning

5. CONCLUSION

Computer Vision can help enhance efficiency, optimize processes, and minimize delays in warehouse operations.

Leave a Reply

Your email address will not be published.