Inventory Stock Determination via 3D SKU Marker Analysis
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Solution Overview
Problem
Traditional stocktaking methods in inventory management are labor-intensive, time-consuming, and prone to errors due to damaged stocks, undetected stocks, and obstacles in the line of sight, especially with automated techniques.
Innovation Solution
A computing system that captures images of objects on pallets, estimates the 3D location of SKU markers, determines the stacking pattern using a learning model, and detects the presence or absence of undetected objects to accurately determine inventory stock levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated stocktaking techniques use images to determine stock, then labor intensity and time consumption are reduced, but measurement precision deteriorates due to damaged stocks, undetected stocks, and obstacles in line of sight
Solution Approach 1:
The patent transitions from 2D image analysis to 3D spatial reasoning by estimating three-dimensional locations of SKU markers and inferring the presence of undetected objects based on stacking patterns and spatial relationships. This dimensional enhancement allows the system to account for occluded objects that cannot be directly seen in 2D images.
Solution Approach 2:
The system uses detected stacking patterns and 3D locations as feedback to identify missing objects. By comparing expected object positions based on stacking patterns with actually detected objects, the system can infer the presence of undetected objects and correct counting errors.
2Measurement precision
If manual stocktaking is performed, then measurement precision is maintained through direct observation, but productivity decreases due to labor-intensive processes and time consumption
Solution Approach 1:
The patent replaces manual mechanical counting with an automated computational system that uses image processing, 3D location estimation, and pattern recognition algorithms. This substitution maintains accuracy by using sophisticated detection methods while dramatically improving productivity through automation.
Solution Approach 2:
The system introduces SKU markers as intermediary objects that facilitate automated detection. These markers serve as reliable reference points that bridge the gap between physical stocks and digital identification, enabling accurate automated counting without direct human observation.
3Measurement precision
If multiple images are captured to improve detection accuracy, then measurement precision improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent combines multiple images into a unified 3D spatial model by integrating detection results from different viewpoints. This merging process consolidates redundant information and resolves ambiguities, improving detection accuracy while managing system complexity through integrated processing.
Data Source
AI summary
The present invention relates to a method of determining stock in an inventory. The method comprises obtaining one or more images comprising one or more objects. Further, estimating a three dimensional (3D) location of a Stock Keeping Unit (SKU) marker associated with each of one or more visible objects. Furthermore, determining a stacking pattern of the one or more objects for each level on the pallet using one of the 3D location of SKU marker and a learning model. Thereafter, detecting at least one of presence or absence of one or more undetected objects at each level based on the stacking pattern and the 3D location of the SKU marker. Finally, determining the stock in the inventory based on the presence or the absence of the one or more undetected objects and the one or more visible objects.