Image Embedding Matching for Carton Origin Identification
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Solution Overview
Problem
Inventory management systems face challenges in identifying the origin of items, particularly in facilities where items from different batches merge, leading to difficulties in tracking individual cartons due to their similar appearance, which requires manual labeling and is time-consuming and labor-intensive.
Innovation Solution
A method using machine learning to generate image embeddings for cartons at unloading stations, linking them to location identifiers, and employing a matching algorithm to determine the origin of cartons based on image embeddings, with backtracking to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to track item origin, then tracking accuracy is improved, but labor intensity and time consumption increase
Solution Approach 1:
The patent replaces manual mechanical labeling with an automated optical-mechanical system consisting of cameras, conveyors, and automated label applicators. The system captures images of items, processes them through image recognition algorithms, and automatically applies labels with origin information, eliminating manual labor while maintaining tracking accuracy.
Solution Approach 2:
The system enables items to be automatically identified and labeled without human intervention. The image recognition system autonomously processes items on the conveyor, extracts origin information from images, and applies appropriate labels, making the tracking process self-service and highly efficient.
2Measurement precision
If manual labeling is used to track item origin, then tracking accuracy is improved, but operational complexity increases
Solution Approach 1:
The patent merges multiple functions into an integrated system: image capture, image processing, origin identification, and label application are combined into a single automated workflow. This consolidation reduces operational complexity by eliminating separate manual steps while maintaining tracking accuracy through the coordinated operation of system components.
Solution Approach 2:
The automated system performs multiple functions simultaneously: it captures images, identifies items, determines origin, and applies labels. This multi-functionality reduces the need for separate manual operations and simplifies the overall tracking process while maintaining high accuracy.
3Extent of automation
If image embeddings are used to identify item origin, then automation is improved, but computational resources increase
Solution Approach 1:
The system extracts only the essential features from item images that are necessary for origin identification, rather than processing entire high-resolution images. By extracting key visual characteristics and comparing them against reference data, the system achieves accurate automated identification while minimizing computational resource consumption.
Solution Approach 2:
The system performs partial image processing by focusing only on the most discriminative features needed for origin identification. Rather than analyzing every pixel or detail of each image, it applies targeted image embedding techniques that process only the necessary visual information, reducing computational overhead while maintaining automation effectiveness.
Data Source
AI summary
Techniques for item origin identification by matching image embeddings are described herein. For example, a computer system can access first image data corresponding to a set of items at a first location of a facility. The first image data can be associated with the first location and can be obtained by a first imaging device. The computer system can generate first set of image embeddings based on the first image data. The first set of image embeddings can represent the set of items. The computer system can determine that a first item of the set of items originated at the first location based at least in part on a matching algorithm comparing the first set of image embeddings with a second image embedding that represents the first item at an identification location of the facility.