Reference Image Templates for Automated Shelf Product Recognition
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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 price tag labels and products is performed, then product identification accuracy is maintained, but inspection time and operational costs increase significantly
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated image capture and processing system. Image capture devices capture images of products and price tags, and computing systems process these images to automatically identify products and verify price tags, eliminating the need for manual visual inspection while maintaining identification accuracy.
Solution Approach 2:
The patent creates digital copies (images) of products and price tags instead of requiring physical manual inspection. The image capture device takes photographs of products on shelves, and the computing system analyzes these digital copies to identify products and verify pricing information, replacing the manual copying process with automated digital imaging.
2Measurement precision
If manual inspection of price tag labels and products is performed, then product identification accuracy is maintained, but operational costs increase significantly
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated image capture and processing system. Image capture devices capture images of products and price tags, and computing systems process these images to automatically identify products and verify price tags, eliminating the need for manual visual inspection while maintaining identification accuracy.
Solution Approach 2:
The system performs self-service inspection where the automated system independently captures images, processes them to identify products, and verifies price tags without requiring human intervention. The computing system autonomously completes the entire inspection process, freeing workers from manual inspection tasks.
3Productivity
If automated image capture and processing systems are implemented, then inspection efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the inspection system into distinct functional components: image capture devices for data collection, computing systems for processing and analysis, and database systems for storing product and pricing information. This modular segmentation allows each component to be optimized independently while working together to achieve automated inspection.
Solution Approach 2:
The patent introduces an intermediary processing layer where computing systems receive images from capture devices, analyze them to identify products and verify price tags, and then store the results in databases. This intermediary processing step coordinates the interaction between different system components, managing complexity through a structured information flow.
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 and being 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 ones of the products located on the product storage structure. Then, the computing device identifies the individual ones of the products detected in the images and crops each of the individual ones 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.