Retail Image Analysis for Automated Product Stock Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual inspection of product storage facilities is time-consuming and costly, as workers need to visually check hundreds of shelves and thousands of products for stock levels, diverting resources from other tasks.
Innovation Solution
A system utilizing a movable image capture device equipped with machine learning models to automatically capture, process, and analyze images of product storage areas, clustering and retraining models to improve product recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to check product stock levels, then workers can identify products that need restocking, but the process becomes time-consuming and increases operational costs
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated image processing system using machine learning models. Image capture devices take photographs of shelves, and trained models automatically identify products and their stock levels, eliminating the need for workers to manually inspect each product while maintaining high identification accuracy.
Solution Approach 2:
The system enables self-service inventory monitoring where the machine learning model automatically processes images, identifies products, determines stock levels, and generates alerts without human intervention. The model continuously learns and improves through retraining on new data, making the system increasingly autonomous and efficient.
2Measurement precision
If manual inspection is deployed across large product storage facilities, then all product areas can be monitored, but operational costs significantly increase
Solution Approach 1:
The patent replaces expensive manual labor with an automated computer vision system. Image capture devices and machine learning models process inventory data automatically, eliminating the need to pay workers for inspection tasks while maintaining or improving monitoring accuracy across the entire facility.
Solution Approach 2:
The machine learning model serves multiple functions: identifying products, determining stock levels, detecting product placement errors, and generating alerts. This multi-functionality consolidates what would otherwise require multiple specialized roles into a single automated system, reducing overall operational costs.
3Measurement precision
If machine learning models are retrained with all available images, then product recognition accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most valuable training data by identifying images where the model is uncertain about product identification or stock level determination. Instead of retraining on all available images, the system selectively uses only those images that will most improve model performance, significantly reducing retraining time and computational resources while maintaining accuracy improvements.
Solution Approach 2:
The system applies partial retraining by using a subset of training data rather than the complete dataset. By focusing on specific edge cases and uncertain predictions, the model achieves sufficient accuracy improvement without the excessive computational burden of processing every available image, balancing quality and efficiency.
Data Source
AI summary
In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects including a memory and a control circuit executing a trained machine learning model. The memory may be configured to store a plurality of images comprising first images and second images. The control circuit may be configured to: allocate each of the first images into one of a plurality of datasets; cluster each image in the dataset into one of a plurality of groups; select a sample from at least one of the plurality of groups; cluster each of the second images into one of dominant product identifier group and a non-dominant product identifier group; select a sample from the dominant product identifier group and a sample from the non-dominant product identifier group; and output the selected sample.