Retail Shelf Imaging for Empty Space and Product Mismatch Detection
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Solution Overview
Problem
The process of replenishing empty shelves in stores is labor-intensive, inconsistent, and time-consuming due to the need for manual visual inspections by employees.
Innovation Solution
A system using shelf-mounted imaging devices to capture and process images of shelves, employing object detection algorithms and product fingerprinting to identify missing or misplaced products, and sending alerts to employees for restocking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual inspection is used to monitor shelves, then employees can identify missing and misplaced products, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the mechanical manual inspection system with an automated image processing system. Imaging devices capture shelf images, and computer vision algorithms automatically detect and identify products, eliminating the need for manual visual inspection while maintaining or improving identification accuracy.
Solution Approach 2:
The system enables self-service monitoring where the shelf inventory monitoring is performed automatically without human intervention. The imaging devices and processing system continuously monitor shelves, identify missing or misplaced products, and generate alerts autonomously.
2Productivity
If employees periodically inspect shelves to ensure proper stocking, then inventory can be replenished, but the process is inconsistent and labor-intensive
Solution Approach 1:
The system provides continuous monitoring of shelf inventory through automated imaging devices that capture images at regular intervals or in real-time. This continuous action replaces periodic manual inspections, ensuring consistent detection of missing or misplaced products without labor-intensive employee involvement.
Solution Approach 2:
The system implements feedback mechanisms where detected inventory issues (missing or misplaced products) automatically generate alerts sent to employees via computing devices. This feedback loop ensures timely response to inventory problems and maintains consistent monitoring standards.
3Extent of automation
If automated image processing is implemented to detect products, then labor consumption is reduced, but system complexity increases
Solution Approach 1:
The system segments the automated detection task into distinct functional modules: imaging device for image capture, image processing module for detecting product boundaries, fingerprint generation module for product identification, and alert generation module for notification. This segmentation manages complexity by organizing functions into separate, manageable components.
Solution Approach 2:
The patent introduces intermediate processing steps including image preprocessing, feature extraction, and fingerprint generation as mediators between image capture and final product identification. These intermediaries break down the complex recognition task into manageable stages, reducing overall system complexity.
Data Source
AI summary
The disclosed system and method relate to automatically detecting empty spaces on retail store shelves, identifying the missing product(s) and causing the space to be replenished or restocked. For example, stores may use shelf-mounted imaging devices to capture images of shelves across the aisle from the imaging devices. The images captured by the imaging devices may be pre-processed to de-warp, de-skew images and stitch together multiple images in order to retrieve an image that captures a full width of a shelf. The pre-processed images can then be used to detect products on the shelf, identify the detected products. An iterative projection algorithm or product fingerprint matching algorithm can be used to identify the products. When an incorrect product listing or an empty shelf space is encountered, a message may be sent to the store employee to remedy the issue.