Location Label Image Processing for Accurate Warehouse Positioning
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Solution Overview
Problem
Existing product storage facilities face inefficiencies in accurately estimating the location of products due to large scale and manual inspection methods, which are time-consuming and less optimal compared to automated processes.
Innovation Solution
A system utilizing image capture devices to capture images of location labels within the facility, applying machine learning techniques for image processing, including deblurring, rectification, optical character recognition, and confidence evaluation to transform image data into location information for precise inventory management and task allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to evaluate products and locate items, then workers can directly observe product conditions, but the process becomes time-consuming and less efficient
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system that captures images of location labels and uses machine learning models to extract and interpret location information. This substitution of mechanical/manual processes with automated optical and computational systems resolves the contradiction by providing both high accuracy through image analysis and high productivity through automation.
Solution Approach 2:
The patent introduces an intermediary image processing system that acts as a mediator between the physical product storage environment and the digital location information system. The system captures images of location labels and uses multiple processing stages (deblurring, rectification, OCR) to transform visual data into actionable location information, enabling accurate and efficient location estimation without direct manual inspection.
2Productivity
If automated image processing is applied to transform images to location information, then productivity and accuracy are improved, but device complexity increases
Solution Approach 1:
The patent divides the image processing task into multiple sequential stages: image capture, deblurring processing, rectification processing, OCR text recognition, and location information extraction. Each stage is handled by a specialized machine learning model or processing module. This segmentation reduces the complexity of any single component while achieving high overall productivity through the coordinated workflow of multiple specialized modules.
Data Source
AI summary
Some embodiments provide systems comprising: a machine learning model database; a deblur system configured to receive at least a portion of an image comprising a presumed location label captured by the image capture device, and apply at least a deblurring machine learning framework to generate a deblurred label image comprising the presumed location label; a rectification system configured to apply an machine learning transform algorithm to the deblurred label image to generate a rectified label image; an optical character recognition (OCR) system configured to apply a recognition machine learning model to the rectified label image to estimate text; and a location estimation system configured to estimate a location of the presumed location label as a function of the estimated text of the presumed location label relative to known text on known location labels position at respective different known locations within the product storage facility.