Glass-Ceramic Detection in Cullet Using RGB Colorimetric Analysis
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Solution Overview
Problem
Current methods for detecting glass-ceramic materials in cullet are unreliable, leading to unnecessary removal of non-glass-ceramic fragments, which results in waste of raw materials and potential defects in glass products due to user sorting errors and imperfect detection accuracy.
Innovation Solution
An automated method involving a colorimetric image processing module using the RGB model to verify the presence of glass-ceramic fragments by calculating the ratio of blue to red pixel data, with a threshold value of 0.5 to distinguish glass-ceramic materials from false positives, and optionally using an HSV model for initial detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current detection methods are used to identify glass-ceramic materials in cullet, then detection speed is maintained, but detection accuracy deteriorates leading to false positives and unnecessary removal of non-glass-ceramic fragments
Solution Approach 1:
The patent applies parameter changes by utilizing the distinct blue color characteristic of glass-ceramic materials containing cobalt oxide. The detection system specifically targets this color parameter through image processing algorithms that analyze the RGB values of detected fragments. By changing from general visual detection to specific color parameter analysis, the system achieves higher detection accuracy while reducing false positives that lead to unnecessary removal of viable glass cullet.
Solution Approach 2:
The patent replaces mechanical/manual sorting methods with an automated optical detection system using digital imaging and colorimetric analysis. This substitution enables precise identification of glass-ceramic fragments based on their characteristic blue color, significantly improving detection accuracy over manual methods while reducing the loss of usable glass cullet through false positive removals.
2Productivity
If automated detection systems are implemented to remove glass-ceramic fragments, then productivity is improved, but reliability deteriorates due to false positives from imperfect detection accuracy
Solution Approach 1:
The system maintains high productivity through automated imaging and processing while improving reliability by focusing on the specific color parameter (blue hue from cobalt oxide) that reliably distinguishes glass-ceramic fragments. This parameter-specific approach reduces false positives compared to general visual detection, thereby enhancing detection reliability without sacrificing the speed benefits of automation.
Solution Approach 2:
The detection system incorporates feedback mechanisms through image processing algorithms that analyze the color characteristics of detected fragments. The system uses RGB value analysis and color thresholding to provide feedback on detection confidence, allowing for more reliable identification of glass-ceramic materials while maintaining automated processing speeds. This feedback loop reduces false positives by verifying color characteristics before triggering removal actions.
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
This method significantly improves the accuracy of glass-ceramic detection, reducing false positives and ensuring only genuine glass-ceramic fragments are removed, thereby minimizing waste and maintaining product quality.
Implementation Method 1
a step of colorimetrically processing the image, during which at least the group of pixels of said image corresponding to fragments thought to be glass-ceramic material is processed by a colorimetric image processing module according to an RGB model
Data Source
AI summary
An automated method for detecting glass-ceramic materials in cullet, includes detecting the glass-ceramic material in cullet, during which fragments thought to be glass-ceramic material are identified in the cullet, obtaining a digital image resulting from the detecting of the glass-ceramic material, the image including at least one group of pixels corresponding to a fragment thought to be glass-ceramic material, colorimetrically processing the image, during which at least the group of pixels of the image corresponding to fragments thought to be glass-ceramic material is processed by a colorimetric image processing module according to an RGB model.