Adaptive OCR Tag Verification for Similar-Looking Equipment
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Solution Overview
Problem
Misidentification of equipment during maintenance can lead to safety risks and unintended outages due to similar-looking equipment with identical identifiers, particularly in critical environments like nuclear power plants.
Innovation Solution
An equipment verification system using computer vision and machine learning algorithms to analyze images of equipment identifier tags, applying adaptive image processing and optical character recognition to accurately identify and verify equipment tags by comparing them with a database of valid identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual equipment identification methods are used, then the process is simple and quick, but the accuracy is low and misidentification risks occur
Solution Approach 1:
The patent replaces manual visual inspection and mechanical identification processes with an automated computer vision system that uses image processing algorithms to detect, segment, and recognize equipment identifier tags. The system automatically captures images, processes them through multiple algorithms (edge detection, contour analysis, OCR), and compares results against a database to verify equipment identity, eliminating human error while maintaining operational efficiency.
Solution Approach 2:
The patent introduces an intermediary image processing system that acts as a mediator between the equipment tag and the final identification decision. The system uses multiple processing stages including edge detection, contour finding, character segmentation, and OCR recognition as intermediate steps to transform the raw image into verified equipment identification, ensuring accuracy while managing complexity through structured intermediate processing layers.
2Reliability
If multiple image processing algorithms are applied to improve identification accuracy, then the reliability increases, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing image preprocessing steps such as grayscale conversion, binary thresholding, and noise filtering before the main identification process. These preliminary operations prepare the image data in advance, making subsequent processing more efficient and reliable. The system also pre-processes images to identify and extract only the relevant tag regions, reducing the computational burden of later recognition stages.
Solution Approach 2:
The patent segments the image processing task into distinct modular stages: initial image acquisition, preprocessing (grayscale, binary), edge detection, contour analysis, character segmentation, OCR recognition, and database comparison. Each segment handles a specific aspect of the identification process, allowing parallel processing where possible and enabling the system to achieve high reliability through multiple verification steps without excessive time loss.
3Adaptability or versatility
If adaptive image processing algorithms are used to handle various tag formats, then the adaptability increases, but the device complexity increases
Solution Approach 1:
The patent implements dynamics by using adaptive image processing algorithms that automatically adjust their parameters based on the input image characteristics. The system dynamically selects and configures processing algorithms (edge detection methods, thresholding levels, contour analysis parameters) according to the specific tag format, lighting conditions, and image quality detected, enabling versatile handling of different tag types without requiring manual configuration or complex hardcoding for each format.
Data Source
AI summary
Employing computer vision to verify equipment is discussed. One example method includes receiving an image comprising an equipment identifier tag and identifying text regions in the image. The method additionally includes generating a clustered text region from the text regions. The clustered text region includes a subset of the text regions. The method also includes adaptively applying an image processing algorithm to a sub-image of the image. The sub-image comprises the clustered text region and the image processing algorithm is selected from a set of image processing algorithms based on the sub-image. The method further includes generating a predicted tag identifier for the processed clustered text region. Additionally, the method includes determining a tag identifier for the equipment identifier tag based on a comparison between the predicted tag identifier for the processed clustered text region and a set of tag identifiers comprising the tag identifier.