Tyre Surface Defect Detection Using Texture Gradient Analysis
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Solution Overview
Problem
Existing methods for detecting faults on tire surfaces, particularly internal surfaces with a repetitive random pattern known as 'toad skin,' struggle to distinguish between healthy and defective regions, leading to unsatisfactory results.
Innovation Solution
The method employs texture gradient analysis to detect heterogeneities in tire surface images, using automated means to calculate and threshold gradient values of multiple texture parameters, acquired through laser triangulation, to visualize and reliably identify defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated means analyze texture parameters to segment and classify images for defect detection, then defect detection capability is improved, but the method becomes ineffective on internal tire surfaces with repetitive random patterns
Solution Approach 1:
The patent changes the parameter being analyzed from absolute texture parameters to texture gradient values. Instead of comparing raw texture measurements against reference databases, the method calculates gradients (rates of change) of texture parameters across the surface. This transformation allows the system to detect defects on repetitive patterns because defects create local variations in gradient values even when absolute texture values remain consistent with the background pattern.
2Reliability
If gradient values are calculated for multiple texture parameters, then detection reliability is improved, but calculation time increases
Solution Approach 1:
The patent applies partial action by calculating gradient values selectively rather than for every single texture parameter uniformly across the entire image. The method identifies regions of interest and focuses computational resources on calculating gradients for multiple parameters only in those specific areas, rather than performing exhaustive calculations across the complete surface.
3Measurement precision
If thresholding is applied to gradient images to visualize defect locations, then defect localization is improved, but false positives may increase
Solution Approach 1:
The patent employs dynamic thresholding where the threshold values are not fixed but adapt based on local statistical properties of the gradient image. The thresholding mechanism adjusts sensitivity levels according to the characteristics of different regions, allowing precise defect localization while adapting to varying background conditions that might otherwise trigger false positives.
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 approach effectively detects defects on tire surfaces with repetitive random patterns, reducing calculation time and improving detection reliability by distinguishing between homogeneous and heterogeneous regions.
Implementation Method 1
automated means acquire the image of the surface by laser triangulation
Data Source
Figure 1~2
AI summary
The invention relates to a method for detecting a defect on a surface of a tyre (4), in which automated means calculate values of a gradient of a plurality of texture parameters from an image of the surface (18) of the tyre (4), determine an image of the gradient (20), and threshold the image of the gradient in order to obtain a thresholded image (22).