Strip Surface Abnormality Detection Using RGB Luminance Analysis
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Solution Overview
Problem
Existing technologies struggle to accurately detect rolling abnormalities, such as rolled strip pinching, in hot rolling mills, leading to surface defects and reduced product yield.
Innovation Solution
A computerized image processing system that uses cameras and an image processing computer to detect rolling abnormalities by analyzing luminance data from images of the strip surface, specifically by dividing the luminance data into R, G, and B components and using threshold boundaries to identify abnormal areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual observation by operator is used to detect rolling abnormalities, then the system is simple to operate, but the detection accuracy depends on operator skill and is insufficient for early intervention
Solution Approach 1:
The patent replaces the manual visual observation system with an automated image processing system using cameras and computer algorithms. The image processing section captures images of the strip surface, extracts luminance data, and automatically detects rolling abnormalities through histogram analysis and threshold comparison, eliminating dependence on operator skill while maintaining operational simplicity
Solution Approach 2:
The system performs self-detection by automatically analyzing its own captured images without requiring external expert intervention. The image processing section autonomously calculates histograms, determines thresholds, identifies abnormal regions, and generates detection results, making the detection process self-sufficient and consistently accurate
2Measurement precision
If simple binarization with white/black assessment is used to detect abnormalities, then the processing is fast and simple, but the detection accuracy is insufficient for actual luminance distribution
Solution Approach 1:
The patent transforms the detection approach from simple binary classification to multi-parameter analysis by extracting luminance data and constructing histograms with multiple bins. This allows the system to capture the actual distribution of luminance values rather than forcing all pixels into white/black categories, significantly improving detection accuracy for subtle abnormalities
Solution Approach 2:
The patent segments the luminance data into multiple histogram bins to analyze the distribution pattern. By dividing the luminance range into discrete intervals and counting pixel frequencies in each bin, the system can identify abnormal regions through histogram shape analysis and threshold comparison, providing more nuanced detection than simple binarization
3Reliability
If computerized image processing is implemented to enable early intervention, then detection accuracy improves, but the device complexity increases
Solution Approach 1:
The system performs preliminary detection by continuously monitoring the strip surface and identifying rolling abnormalities before they cause severe damage or require emergency intervention. The image processing section analyzes each captured image in real-time, calculating histograms and comparing against thresholds to detect early signs of abnormalities, enabling preventive actions to be taken
Solution Approach 2:
The system establishes a feedback loop where detection results are immediately available to operators or control systems. The image processing section generates detection results based on histogram analysis, which can trigger alerts, adjust processing parameters, or initiate corrective actions, creating a closed-loop system that continuously improves reliability through real-time monitoring and response
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
The system significantly increases the accuracy of detecting rolling abnormalities, allowing for early intervention and prevention of subsequent rolling abnormalities and facility failures.
Implementation Method 1
a camera 81 for capturing an image of the workpiece 1
Data Source
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AI summary
An abnormality detecting apparatus, which is an apparatus for detecting a rolling abnormality on a surface of a workpiece 1 to be rolled by a rolling mill, includes cameras 81 and 82 for capturing images of the workpiece 1 that is an object to be detected for an abnormality, and an image processing section 92 for dividing luminance data of pixels in a range of the workpiece 1 from the images captured by the cameras 81 and 82 into three components of R values, G values, and B values and detecting a rolling abnormality on the surface on the basis of the relation between the luminance data of two components among the luminance data of the respective components. There are thus provided an abnormality detecting apparatus and an abnormality detecting method that are capable of increasing the accuracy with which to detect a strip rolling abnormality compared with the conventional art.