Cached Image Recognition for Automated Product Storage Counts
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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 manually inspecting product storage areas.
Innovation Solution
An image capture device moves about the facility capturing images, a computing device processes these images to detect and identify products, crops individual products, and generates templates for each, storing them in an electronic database for subsequent recognition and updating product counts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of price tag labels and products is performed, then accuracy of inventory verification is improved, but productivity and operational efficiency deteriorate due to time consumption
Solution Approach 1:
The patent replaces the mechanical manual inspection system with an automated optical inspection system. Image capture devices (cameras) mounted on robotic or automated systems capture images of products and price tags, which are then processed by computer vision algorithms and machine learning models to automatically verify inventory accuracy, eliminating the need for manual visual inspection while maintaining high accuracy
Solution Approach 2:
The system enables self-service inventory verification where the automated image capture and processing system performs the inspection task independently without human intervention. The machine learning models automatically analyze captured images, identify products and price tags, and generate inventory verification results, allowing the system to serve itself in performing the inspection function
2Reliability
If manual inspection by workers is performed, then product storage areas can be verified, but operational costs increase due to labor requirements
Solution Approach 1:
The patent substitutes the mechanical labor system with an automated electronic inspection system. Image capture devices, processing units, and database systems work together to automatically verify product storage areas, replacing human workers and eliminating associated labor costs while maintaining verification reliability
Solution Approach 2:
The system creates digital copies (images) of the physical product storage areas and price tags. These image copies are then processed and analyzed by computer vision algorithms to verify inventory information, allowing the system to work with replicated digital representations rather than requiring direct physical inspection
3Measurement precision
If workers are deployed for manual inspection, then inventory accuracy can be maintained, but time availability for other tasks is reduced
Solution Approach 1:
The system performs preliminary automated inspection actions continuously or at scheduled intervals without requiring worker intervention. Image capture devices continuously capture images of product storage areas, and the processing system automatically analyzes these images to maintain inventory accuracy, freeing up worker time for other value-added tasks
Solution Approach 2:
The automated inspection system operates continuously or at frequent intervals, maintaining constant surveillance of product storage areas. This continuous automated action ensures inventory accuracy is maintained at all times without interrupting worker productivity, as the system operates independently and continuously
Data Source
AI summary
Systems and methods of detecting and recognizing products on product storage structures of a product storage facility include an image capture device that moves about and captures images of the product storage structures at the product storage facility. A computing device processes the obtained images to detect and identify the products on the product storage structure, crops each of the identified individual products from the image to generate a plurality of cropped images and generates an image histogram template, feature vector template and location information template for each of the cropped images. The cropped images are stored in an electronic database and represent a reference model for each of the identified individual products and are stored in association with the generated image histogram template, feature vector template and location information template to facilitate recognition of products subsequently captured on the product storage structure by the image capture device.