Storage Facility Image Processing for Automated Stock Detection
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Solution Overview
Problem
Manual inspection of products in large product storage facilities is time-consuming and increases operational costs, as workers spend significant time determining stock levels and replenishing items.
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 manage and retrain the model for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to determine stock levels, then workers can identify products, but the process is time-consuming and increases operational costs
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated computer vision system. Image capture devices capture photos of product storage areas, and machine learning models process these images to automatically identify products and their stock levels, eliminating the need for workers to manually inspect each product while maintaining or improving identification accuracy.
Solution Approach 2:
The system enables self-service inventory monitoring where the automated image processing system continuously tracks stock levels without human intervention. The machine learning model processes images autonomously to determine which products are adequately stocked and which need replenishment, allowing the system to monitor itself rather than requiring constant manual inspection.
2Reliability
If manual inspection is performed, then workers can assess product status, but operational costs increase significantly
Solution Approach 1:
The patent replaces manual labor with an automated automated vision system that uses image capture devices and machine learning models to assess product stock status. This substitution eliminates the need for workers to manually evaluate products while maintaining reliable assessment accuracy and significantly improving operational efficiency by removing the time constraint on worker availability.
Solution Approach 2:
The system implements continuous feedback loops where image capture devices repeatedly photograph storage areas, machine learning models process these images to assess stock levels, and the results feed back into inventory management decisions. This automated feedback mechanism provides reliable and continuous product status assessment without the productivity limitations of manual inspection.
3Measurement precision
If workers inspect product storage areas, then they can identify out-of-stock items, but workers are unavailable for other tasks
Solution Approach 1:
The patent replaces worker-based inspection with an automated image processing system that continuously monitors storage areas. The machine learning model processes images to accurately identify out-of-stock items while workers remain free to perform other value-added tasks, thereby maintaining detection accuracy while improving worker task flexibility and adaptability.
Solution Approach 2:
The system performs self-service inventory monitoring through automated image capture and processing, eliminating the need for workers to be tied up with inspection tasks. This allows workers to be allocated to other productive activities while the system autonomously maintains accurate tracking of product stock status and out-of-stock conditions.
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.