Computer Vision Label Registration for Automated Inventory Tracking
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Solution Overview
Problem
Existing systems require manual intervention for label detection and registration on objects, leading to inefficiencies and potential errors, especially when labels are not detectable or readable.
Innovation Solution
A computer-vision-based system and method that automatically detects and reads labels on objects using machine learning, associates them with object locations, and employs change detection algorithms to manage inventory and assets, with human intervention as a fallback for undetectable or unreadable labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for label detection and registration, then human judgment can handle complex cases, but efficiency decreases and errors increase
Solution Approach 1:
The system performs self-service by automatically detecting objects, reading labels, and registering information without requiring human intervention. The computer vision system independently completes the entire label detection and registration process, eliminating manual labor while maintaining accuracy through automated verification mechanisms.
Solution Approach 2:
The patent replaces manual mechanical operations with automated computer vision systems. Instead of human operators physically scanning and recording labels, the system uses cameras, machine learning algorithms, and automated processing to detect, read, and register label information electronically.
2Productivity
If automated computer vision systems are used, then productivity increases, but complexity of the system increases
Solution Approach 1:
The system achieves multi-functionality by integrating multiple capabilities into a single unified platform. The same computer vision system performs object detection, label recognition, barcode scanning, inventory tracking, and data registration simultaneously, eliminating the need for separate specialized systems for each function.
Solution Approach 2:
The patent introduces an intermediary processing layer that simplifies the connection between hardware sensors and business logic. This intermediate software layer handles image processing, machine learning inference, and data validation, shielding the complex underlying technology from the user interface and simplifying system integration.
3Measurement precision
If machine learning algorithms are applied, then label detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing images to enhance key features before they reach the machine learning model. Techniques such as image normalization, feature extraction, and data augmentation are applied in advance to prepare optimal input data, enabling faster and more accurate real-time processing.
Solution Approach 2:
The patent applies partial action by selectively applying machine learning algorithms only to critical regions of interest rather than processing entire images. The system identifies and focuses computational resources on specific areas where labels are likely to appear, reducing overall processing time while maintaining high accuracy for the most important detection tasks.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient, automated label detection and registration without human intervention, improving accuracy and reducing errors by leveraging machine learning and computer vision techniques.
Implementation Method 1
a camera configured to capture images of objects on a support surface
Implementation Method 2
the processor is configured to: detect one or more objects in the captured images
Implementation Method 3
detect one or more labels in the captured images; automatically read label information from each detected label
Data Source
AI summary
A computer vision system for automatic identification, tracking and management of inventory and/or assets, wherein: the computer vision system is programmed with predetermined and configurable image data conditions that identify and capture certain types of movement; the computer vision system is programmed to constantly capture and process image data from one or more sets of sensors to detect the image data conditions; the computer vision system is programmed to trigger the process of identifying all objects and extracting label information from objects present in a field of view of the system if the image data conditions are fulfilled; the computer vision system processes the image data with one or more object detection algorithms and label detection algorithms and generate correspondences between objects and labels identified; and the computer vision system running change detection algorithms and semantic analysis to detect if any object was moved, removed, occluded and if any new objects were added in to the system.