Product-Storage Image Processing for Automated Inventory Accuracy
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Solution Overview
Problem
Manual inspection of product storage facilities is time-consuming and increases operational costs due to the large number of shelves and products, necessitating a more efficient inventory management system.
Innovation Solution
Implementing a system with a trained machine learning model and image capture device to automatically process images of product storage areas, categorizing images into groups based on product recognition, and using a control circuit to retrain the model for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to inventory products, then workers can identify and categorize products, but the process is time-consuming and increases operational costs
Solution Approach 1:
The patent replaces the mechanical manual inspection system with an automated image processing system. Image capture devices capture images of product storage areas, and machine learning models automatically analyze these images to identify products, count quantities, and update inventory records, eliminating the need for manual visual inspection while maintaining or improving inventory accuracy
Solution Approach 2:
The system enables self-service inventory management where the automated image processing system continuously monitors and updates inventory without human intervention. The machine learning models automatically detect products, recognize them from training data, and update database records, allowing the inventory system to service itself rather than requiring manual worker involvement
2Measurement precision
If manual inspection is used to inventory products, then workers can identify products, but operational costs increase significantly
Solution Approach 1:
The patent replaces the mechanical manual inspection system with an automated image processing system. Image capture devices capture images of product storage areas, and machine learning models automatically analyze these images to identify products, count quantities, and update inventory records, eliminating the need for manual visual inspection while maintaining or improving inventory accuracy
Solution Approach 2:
The system uses feedback loops where the machine learning models continuously learn from new images and update their recognition capabilities. The control circuit receives feedback about product identification accuracy and adjusts the models accordingly, improving operational efficiency over time while reducing the need for manual verification and associated labor costs
3Productivity
If automated image processing is implemented, then manual labor is reduced, but system complexity increases
Solution Approach 1:
The patent employs a universal machine learning model that can identify multiple types of products across different storage areas using a single trained system. The model is trained on diverse product images and can generalize to recognize various products, eliminating the need for separate specialized systems for different product categories and reducing overall system complexity
Solution Approach 2:
The system creates a digital copy of the physical inventory through image capture and stores it in a database. The machine learning models work with these digital representations rather than physically manipulating products, simplifying the system architecture. The control circuit manages these digital copies and uses them to update inventory records, reducing the complexity compared to direct physical inventory management
Data Source
AI summary
In some embodiments, apparatuses and methods are provided herein useful to processing captured images of objects at a product storage facility. In some embodiments, there is provided a system for processing captured images of objects including a trained machine learning model and a control circuit. In some embodiments, the trained machine learning model is configured to process unprocessed captured images. In some embodiments, the control circuit is configured to associate each of the processed images into one of a first group, a second group, or a third group; remove at least one processed image associated with the first group from the processed images in accordance with a first processing rule; and output remaining processed images associated with the first group and processed images associated with the second group to be used to retrain the trained machine learning model.