Tire Surface Defect Detection via Orientation Index Comparison
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Solution Overview
Problem
Current methods for detecting defects on the surface of tires, especially those intersecting with patterns, are inefficient due to the masking effect of the pattern, leading to increased computational costs and reduced sensitivity in defect detection.
Innovation Solution
A method and apparatus that utilize the difference in orientation between elongated defects and pattern sections by acquiring digital images, creating a digital model of the pattern, and calculating orientation indices to distinguish defect sections from pattern sections, allowing for accurate detection even in the presence of surface patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional defect detection methods are used on patterned tire surfaces, then the detection process becomes complex and computationally expensive, but the pattern masking effect reduces measurement precision and defect detection accuracy
Solution Approach 1:
The method segments the detection process into distinct stages: acquiring digital images of the tire surface, creating a digital model of the pattern, calculating orientation indices for both pattern sections and defect sections, and comparing these indices to identify defects. This segmentation allows each stage to be optimized independently, reducing overall complexity while maintaining high detection accuracy
Solution Approach 2:
The invention introduces orientation indices as a new parameter to characterize both pattern sections and defect sections. By calculating and comparing orientation indices rather than directly analyzing image pixel values, the method transforms the detection problem into a simpler parameter comparison task, reducing computational complexity while improving measurement precision
2Reliability
If conventional image processing methods are used to detect defects on patterned surfaces, then computational costs increase due to the need to process entire images, but defect detection sensitivity decreases due to pattern masking
Solution Approach 1:
The method extracts the essential characteristic (orientation index) from both pattern sections and defect sections, separating the defect detection task from the complex pattern background. By working with extracted orientation parameters rather than full images, computational costs are reduced while defect detection sensitivity is maintained through focused comparison of the extracted features
Solution Approach 2:
The invention transforms the detection problem from analyzing complex image data to comparing simple orientation index parameters. This parameter transformation reduces computational energy requirements while maintaining high detection sensitivity, as the orientation index provides a concise representation that captures the essential difference between pattern and defect sections
Data Source
AI summary
Method and related apparatus for detecting defects on a surface of a tyre, comprising: providing the tyre (200); acquiring a digital image comprising a structure comprising sections representative of linear elements of a pattern in a surface portion and representative of possible elongated defects, said sections of the structure having a respective orientation; providing a model of the pattern in the surface portion, wherein each pixel is associated with a first index representative of whether the pixel belongs or not to a pattern section and a second index representative of an at least local orientation of the pattern section passing through said pixel; calculating for each pixel of the structure a third index representative of the orientation of the structure section passing through said pixel; and establishing, for each pixel of the structure having a corresponding pixel in the pattern model belonging to the pattern, whether said pixel of the structure belongs to a proposed defect on the basis of the comparison between the third index and the second index associated with the corresponding pixel in the pattern model.