Tire Surface Inspection Zone Segmentation
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Solution Overview
Problem
Existing tire inspection methods using image processing techniques are costly in terms of calculation time, especially when applied to the entire tire surface, due to their complexity and inability to efficiently identify and process distinct zones of interest.
Innovation Solution
The method involves capturing two- and three-dimensional images of a reference tire, dividing the surface into distinct zones of interest, and assigning specific registration and checking algorithms to each zone, allowing for optimized processing and reduced calculation time by using algorithms suited to each zone's characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing algorithms are applied to the entire tire surface, then comprehensive inspection coverage is achieved, but calculation time becomes excessively long
Solution Approach 1:
The tire surface is divided into multiple distinct zones of interest (tread, sidewalls, beads, shoulder regions) based on their specific characteristics. Each zone is processed independently with appropriate algorithms, avoiding unnecessary processing of entire surface and reducing overall calculation time while maintaining comprehensive inspection coverage.
Solution Approach 2:
Different processing algorithms and complexity levels are applied to different zones based on their specific characteristics. For example, simple uniformity checks for smooth sidewall zones versus complex pattern matching for tread zones with intricate designs. This localized approach optimizes processing efficiency for each region while maintaining overall inspection quality.
2Measurement precision
If high-performance registration algorithms are used for complex zones, then accuracy is improved, but processing time for simple zones becomes unnecessarily long
Solution Approach 1:
The patent applies different complexity levels of registration algorithms to different zones based on their characteristics. Simple zones use basic uniformity checks with minimal processing, while complex zones with intricate patterns use advanced pattern matching and feature recognition algorithms. This ensures high accuracy where needed while maintaining fast processing for simpler areas.
Solution Approach 2:
Instead of applying full-complexity algorithms uniformly across all zones, the patent uses partial processing appropriate to each zone's requirements. Simple zones receive minimal necessary processing, avoiding excessive computation, while complex zones receive the full attention needed for accurate inspection.
3Device complexity
If uniform processing algorithms are applied to all tire zones, then processing simplicity is maintained, but inspection accuracy for specific zones deteriorates
Solution Approach 1:
The patent implements zone-specific processing algorithms tailored to the characteristics of each tire region. Smooth sidewalls use simple uniformity and striation analysis, while tread zones with complex patterns use advanced pattern matching and feature recognition. This localized approach maintains processing simplicity within each zone while achieving high inspection accuracy for zone-specific characteristics.
4Reliability
If the entire tire surface is processed with high-complexity algorithms, then comprehensive anomaly detection is achieved, but processing time exceeds industrial manufacturing tempo
Solution Approach 1:
The tire surface is segmented into distinct zones that are processed independently with appropriate algorithm complexity. This segmentation allows comprehensive anomaly detection within each zone using tailored algorithms while avoiding the excessive computation time that would result from applying high-complexity algorithms uniformly across the entire surface, thus meeting industrial manufacturing tempo requirements.
Solution Approach 2:
Different complexity levels of anomaly detection algorithms are applied locally to different zones based on their risk profiles and characteristic features. High-risk zones with complex patterns receive intensive processing, while low-risk uniform zones receive streamlined processing. This ensures comprehensive anomaly detection capability where needed while maintaining overall inspection speed compatible with industrial production rates.
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 significantly reduces calculation time required for tire inspection, enabling conformity checking at a rate compatible with industrial manufacturing tempo by focusing on low-complexity algorithms for uniform zones and using high-performance methods where necessary.
Implementation Method 1
The methods used to perform this processing usually consist in comparing a two- or preferably three-dimensional image of the surface of the tire to be inspected with a two- or preferably three-dimensional reference image of the surface of said tire. In a known way, one of the steps of this process has the aim of acquiring the three-dimensional image of the surface of the tire, for example with the aid of means based on the principle of optical triangulation, using, for example, a 2D sensor coupled to a laser illumination source.
Data Source
AI summary
A method for inspecting a tire's surface includes: capturing a reference image of a reference tire's surface relief elements and transmitting data of the reference image to a processor; parameterizing main characteristics of the reference image via interaction of an operator with the processor; producing a reference map of the surface relief elements by dividing the reference image into a plurality of reference zones of interest; assigning a specific registration and checking algorithm to each of the reference zones of interest; capturing an inspection image of a tire under inspection; and, after completion of pre-processing of the inspection image, automatically: superimposing the reference map on the inspection image, and, for each of a plurality of zones of interest of the inspection image, running the specific registration and checking algorithm for a corresponding one of the reference zones of interest, to determine a conformity of the tire under inspection.