Dynamic Luminance Thresholding for Metal Strip Rolling Defect Detection
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Solution Overview
Problem
Conventional surface inspection systems for detecting rolling abnormalities in metal strips, such as rolled strip pinching, face challenges in accurately setting determination thresholds due to variations in illumination and water vapor effects, leading to reduced detection accuracy.
Innovation Solution
An abnormality detecting apparatus and method that uses cameras and an image processing computer to capture images of the metal strip, calculate luminance standard values by considering RGB components, and detect abnormalities based on luminance differences, with adaptive threshold settings to account for variations in illumination and water vapor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed determination threshold is used for abnormality detection, then the detection process is simple and fast, but the detection accuracy decreases due to variations in illumination and water vapor effects
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously monitoring the luminance distribution of the metal strip surface and adapting the determination threshold based on real-time conditions. Instead of using a fixed threshold, the system calculates the standard deviation of luminance values and sets the threshold as a function of this statistical parameter, allowing the threshold to automatically adapt to variations in illumination and environmental conditions while maintaining detection accuracy
Solution Approach 2:
The system employs feedback mechanisms by continuously analyzing the luminance histogram of the strip surface and using the calculated standard deviation to adjust the determination threshold. The detection results feed back into the threshold adjustment process, creating a closed-loop system that maintains optimal detection performance under varying conditions without requiring manual intervention
2Measurement precision
If the determination threshold is adjusted to account for illumination and water vapor variations, then detection accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent applies partial action by focusing computational resources only on the necessary statistical analysis (calculating mean and standard deviation of luminance values) rather than performing complete image processing. The system extracts only the essential features (luminance distribution characteristics) needed for threshold determination, avoiding unnecessary processing steps while maintaining detection accuracy
Solution Approach 2:
The system changes the parameter representation from raw pixel values to statistical parameters (mean and standard deviation of luminance). This transformation simplifies the data while preserving the essential information needed for threshold determination, reducing computational complexity and processing time while maintaining detection accuracy under varying illumination conditions
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
Improves the detection accuracy of rolling abnormalities by dynamically adjusting thresholds, reducing false positives and enhancing the ability to detect abnormalities on the metal strip surface.
Implementation Method 1
an image of the surface of the strip is monitored by a camera
Implementation Method 2
capture images of the workpiece (1) to be rolled
Data Source
Figure 1
Figure 2
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AI summary
An abnormality detecting apparatus includes cameras 81 and 82 for capturing images of a workpiece 1 to be rolled that is an object to be detected for a rolling abnormality while the workpiece 1 is being rolled, a luminance standard value setting section 91 for extracting the workpiece 1 from at least one image captured by the cameras 81 and 82 and determining a luminance standard value by which the workpiece 1 is assessed as lacking an abnormality from luminance data of pixels within a range of the workpiece 1, and an image processing section 92 for extracting the workpiece 1 from each of one or more comparative images among one or more of the captured images and detecting a rolling abnormality on a surface on the basis of the luminance difference between the luminance data within the range of the workpiece 1 and the luminance standard value. 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.