Shelf Imaging System for Automated Inventory Detection
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Solution Overview
Problem
The current process of replenishing empty shelves in stores is labor-intensive, inconsistent, and time-consuming, as store employees manually identify missing products and misplaced items, requiring a more efficient method to restock and reorganize shelves effectively.
Innovation Solution
A system utilizing shelf-mounted imaging devices to capture and process images of shelves, employing object detection algorithms and iterative projection methods to identify missing products and anomalies, sending notifications to employees for remedial action, thereby automating the detection and restocking process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual visual inspection by employees is used to identify missing products, then the system is simple to implement, but the process is labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual visual inspection by employees with an automated imaging system that uses cameras, processors, and computer vision algorithms to detect missing products. The imaging device captures shelf images, the processor analyzes them using machine learning models, and the system automatically identifies missing products and generates restocking tasks, eliminating the need for manual inspection while improving productivity.
Solution Approach 2:
The system enables self-service monitoring where the imaging and processing system automatically detects missing products without human intervention. The processor continuously monitors shelf images, identifies missing products autonomously, and generates restocking tasks automatically, allowing the system to serve itself rather than requiring employee inspection.
2Reliability
If manual visual inspection is performed, then implementation is straightforward, but the process is inconsistent and time-consuming
Solution Approach 1:
The patent replaces inconsistent manual inspection with automated computer vision technology. The imaging device captures standardized images of shelves, and the processor uses machine learning algorithms to consistently identify missing products across different times and locations, eliminating the variability inherent in manual inspection while reducing time requirements.
Solution Approach 2:
The system enables continuous monitoring of shelves through automated imaging and processing. The processor can analyze shelf images at any time without interrupting store operations, providing continuous inspection rather than periodic manual checks, thereby improving consistency while reducing total inspection time through efficient automated processing.
3Extent of automation
If employees manually monitor shelves, then the system requires minimal technology, but it is labor-intensive
Solution Approach 1:
The patent replaces labor-intensive manual monitoring with an automated imaging system. The imaging device captures shelf images, the processor analyzes them using machine learning to identify missing products, and the system automatically generates restocking tasks. This substitution eliminates the need for employees to manually monitor shelves, achieving high automation while reducing labor requirements.
Solution Approach 2:
The system performs self-service monitoring where the automated imaging and processing system continuously detects missing products without requiring employee involvement. The processor autonomously analyzes images, identifies missing products, and generates restocking tasks, replacing the labor-intensive manual monitoring process with self-service automation.
4Productivity
If automated imaging systems are deployed, then productivity and consistency improve, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional integrated system where a single imaging device performs multiple functions: capturing shelf images, detecting products, identifying missing items, and providing restocking information. The processor handles multiple tasks including image analysis, product recognition, and task generation. This consolidation of functions into a single system improves productivity while managing complexity through integration rather than multiple separate devices.
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. For example, the captured image may be compared against a reference background image using a background modeling algorithm to identify empty spaces and mis-shelved items within the shelf.