Deep-Learning Vision for Asset Serialization in Low Light
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Solution Overview
Problem
Current systems are inadequate for accurately and efficiently capturing identifiers on unique assets, such as those in the defense and healthcare sectors, which lack traditional serial numbers or identifiers and are often stored in challenging environments with poor lighting, requiring manual methods that are time-consuming and prone to errors.
Innovation Solution
A computer-implemented system using deep-learning computer vision models for image detection and recognition, integrated with a mobile application and database interface, capable of detecting and recognizing text on assets without conventional serial numbers, even in low-light conditions, and improving prediction accuracy through user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to read and log serial numbers, then identification can be performed without specialized equipment, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical reading and logging processes with an automated computer vision system that uses image capture, optical character recognition (OCR), and machine learning algorithms to automatically detect, recognize, and record serial numbers on assets, thereby increasing both speed and accuracy of identification
Solution Approach 2:
The system creates digital copies (images) of the asset identifiers and processes these copies through software algorithms to extract serial number information, eliminating the need for direct manual reading while preserving the original asset unchanged
2Ease of operation
If traditional serial number tags or labels are applied to assets, then identification becomes easier with standard scanning equipment, but it increases device complexity and may not be suitable for all asset types
Solution Approach 1:
The patent develops a universal identification system that can handle multiple asset types with different identifier formats (engraved, etched, printed, embossed) using a single platform, eliminating the need for asset-specific tagging methods while maintaining ease of operation across diverse asset classes
Solution Approach 2:
The system introduces an intermediary image capture and processing layer between the physical asset identifier and the database, using computer vision technology to bridge the gap between various identifier formats and the standardized digital inventory system
3Volume of stationary object
If assets are stored in challenging environments with poor lighting, then space utilization is optimized, but identifier detection becomes difficult or impossible
Solution Approach 1:
The patent implements dynamic image processing capabilities that adapt to varying lighting conditions, using adjustable exposure settings, multiple shot integration, and real-time processing to capture and enhance identifier visibility regardless of the storage environment's lighting quality
Data Source
AI summary
An embodiment of the present invention is directed to a combination of two deep-learning computer vision models—customized with post-processing—wrapped in a mobile application that is backed by an Application Programming Interface (API) supporting concurrent mobile users to accomplish asset serialization tasks in a warehouse or other storage environment.