Method for detecting a defect on a surface of a tire

• US 10,445,868 B2
• Filed: 12/01/2015
• Issued: 10/15/2019
• Est. Priority Date: 12/15/2014
• Status: Active Grant

• Current Assignee: Compagnie générale des établissements Michelin
• Original Assignee: Compagnie Generale DES Etablissements Michelin SCA
• Inventors: Arvis, Vincent, Jeulin, Dominique, Bilodeau, Michel
• Primary Examiner(s): Dulaney, Kathleen Y
• Application Number:
  US15/528,956
• Publication Number:
  US 20170278234A1
• Time in Patent Office: 1,414 Days
• Field of Search: 382141, 382104, 382190, 382266, 356601, 702 34, 73146
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/41 based on statistical description of texture
• CPC Class Codes: G06T 7/44 using image operators, e.g. filters, edge density metrics or local histograms

Abstract

A method for detecting a defect on a surface of a tire includes automated steps of: calculating values of a gradient of a plurality of texture parameters from an image of the surface of the tire, determining an image of the gradient, and thresholding of the image of the gradient to obtain a thresholded image.

Claims

1. A method for detecting a defect on a surface of a tire, the method comprising:

automatically calculating, using a computer, values of a gradient of a plurality of texture parameters from an image of the surface of the tire;

automatically determining, using the computer, an image of the gradient;

automatically thresholding, using the computer, the image of the gradient to obtain a thresholded image; and

displaying the thresholded image, wherein the plurality of texture parameters comprises at least five texture parameters.

2. The method according to claim 1, wherein the image of the surface of the tire is an image of an inner surface of the tire.

3. The method according to claim 1, further comprising using a laser system to automatically acquire the image of the surface of the tire by laser triangulation.

4. The method according to claim 1, wherein the computer calculates the values of the gradient in a selection of pixels of a part of the image of the surface of the tire.

5. The method according to claim 1, further comprising automatically calculating, using the computer, a grey-level gradient in the image of the surface of the tire.

6. The method according to claim 1, wherein the thresholding is performed by the computer using a hysteresis process.

7. The method according to claim 1, wherein the plurality of texture parameters consists of five texture parameters.

8. The method according to claim 1, wherein the plurality of texture parameters comprises Haralick parameters.

9. The method according to claim 1, wherein the plurality of texture parameters comprises a local binary pattern.

10. The method according to claim 1, wherein the plurality of texture parameters comprises 25 texture parameters.

11. A computer-readable storage medium storing a program that when executed by a computer causes the computer to perform a method for detecting a defect on a surface of a tire, wherein the method includes:

automatically calculating values of a gradient of a plurality of texture parameters from an image of the surface of the tire;

automatically determining an image of the gradient;

automatically thresholding the image of the gradient to obtain a thresholded image; and

displaying the thresholded image, wherein the plurality of texture parameters comprises at least five texture parameters.

12. A method for detecting a defect on a surface of a tire, the method comprising downloading to a computer, via a telecommunications network, coded instructions that when executed by the computer cause the computer to:

automatically calculate values of a gradient of a plurality of texture parameters from an image of the surface of the tire,

automatically determine an image of the gradient,

automatically perform a thresholding process on the image of the gradient to obtain a thresholded image, and

display the thresholded image, wherein the plurality of texture parameters comprises at least five texture parameters.

13. An installation for detecting a defect on a surface of a tire, the installation comprising a computer programmed to:

automatically calculate values of a gradient of a plurality of texture parameters from an image of the surface of the tire,

automatically determine an image of the gradient,

automatically perform a thresholding process on the image of the gradient to obtain a thresholded image, and

display the thresholded image, wherein the plurality of texture parameters comprises at least five texture parameters.

14. The installation according to claim 13, further comprising a laser system that automatically acquires the image of the surface of the tire.