Reference Image Templates for Faster Product Shelf Identification
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Solution Overview
Problem
Manual inspection of price tag labels and products in large product storage facilities is time-consuming and increases operational costs, as workers could be performing other tasks if not involved in manual inspections.
Innovation Solution
An image capture device and computing system are used to create reference template images for detecting and recognizing products, involving image capture, product detection, cropping, clustering, and selecting reference template images to automate the inspection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of products and price tag labels is performed, then product identification accuracy is maintained, but inspection time and operational costs increase significantly
Solution Approach 1:
The patent creates reference template images that are copies of actual product appearances. These templates are stored and used for comparison during inspection, allowing the system to identify products by matching captured images against the reference templates rather than requiring manual verification of each product's unique characteristics
Solution Approach 2:
The patent replaces the mechanical/manual inspection process with an automated image processing system. The system captures images of products and price tags, processes them through image analysis algorithms, and automatically identifies products by comparing captured images against stored reference templates, eliminating the need for human workers to manually inspect each item
2Measurement precision
If manual inspection of products and price tag labels is performed, then product identification accuracy is maintained, but operational costs increase significantly
Solution Approach 1:
The system creates and stores reference template images that replicate product appearances. During operational inspections, the system retrieves appropriate templates and compares them with captured images, enabling accurate product identification without requiring human inspectors to manually verify each product, thereby reducing labor costs and improving operational efficiency
Solution Approach 2:
The patent automates the inspection process by replacing manual human inspection with an automated image capture and processing system. The system uses image analysis algorithms to automatically identify products and verify price tags, eliminating the need for human workers to perform these repetitive inspection tasks, thus reducing operational costs and increasing productivity
3Productivity
If automated image processing is used to create reference templates, then inspection speed increases, but system complexity increases
Solution Approach 1:
The patent divides the inspection process into distinct sequential steps: capturing images of products and price tags, processing images to extract key features, creating reference templates from the processed images, storing templates for later retrieval, and during inspection, retrieving templates and comparing them with new captured images. This segmentation allows each step to be optimized independently and simplifies the overall system architecture
Solution Approach 2:
The system performs preliminary actions by creating and storing reference template images before actual inspection occurs. These templates are generated in advance from sample products and price tags, allowing the inspection process to simply retrieve and compare templates rather than analyzing products from scratch during inspection, thereby increasing inspection speed while keeping the system relatively simple
Data Source
AI summary
Systems and methods of creating reference template images for detecting and recognizing products at a product storage facility include an image capture device having a field of view that includes a product storage structure of the product storage facility, and a computing device including a control circuit communicatively coupled to the image capture device. The computing device obtains images of the product storage structure captured by the image capture device, analyzes the obtained images to detect individual products located on the product storage structure. Then, the computing device identifies the individual products detected in the images and crops each of the identified products from the images to generate cropped images. The computing device then creates a cluster of the cropped images and selects one of the cropped images as a reference template image of an identified individual product.


