Method of checking the appearance of the surface of a tyre

1. A method of detecting an anomaly on an internal surface of a tyre through digital processing of an image of the internal surface of the tyre, the method comprising steps of:

capturing a grey-level original image (f) of the internal surface of the tyre and transforming the original grey-level image into an orthonormal space, in which an x-axis (OX) represents a circumferential direction, and in which a y-axis (OY) represents a radial direction;

transforming the original grey-level image using a predetermined series of filters (F 1 f , F 2 f , . . . F L f ), to obtain a multivariate original image;

splitting the multivariate original image into multivariate sub-images (W F k ), to obtain predefined tiling in the axial direction and the circumferential direction;

transforming each of the multivariate sub-images is transformed into one-dimensional vectors using a given descriptor (H, G, . . . N), to obtain a simplified multivariate image (K W F , G W F . . . , H W F ) formed from a collation of the vectors;

reducing a number of dimensions of the simplified multivariate image data using a data analysis tool, to obtain a reduced factorial space; and,

in the reduced factorial space, evaluating a distribution of sub-images containing an anomaly in relation to a distribution of sub-images containing no anomaly, in order to assess a separating power of the given descriptor.

2. The method according to claim 1 ,

wherein the original grey-level image is captured using a sensor capable of evaluating a relief of the surface, and

wherein the method further comprises a step of, for each pixel of the original grey-scale image, assigning a grey-level value proportional to a topographical elevation of a point corresponding to the pixel, to obtain a two-dimensional original image.

3. The method according claim 1 , further comprising a step of, after the step of capturing the grey-level image, carrying out a pre-processing of the grey-level image using a polynomial interpolation of grey levels by line or by column, in order to lessen effects of lighting variations during capture of the grey-level image.

4. The method according to claim 1 , wherein the series of filters is formed from morphological operators corresponding to swelling, wear, opening, and closure operations, or from residuals of one or more of the operations.

5. The method according to claim 1 , wherein the tiling is performed in the circumferential direction and the radial direction in such a way that the multivariate sub-images have a shape of a square.

6. The method according to claim 5 , wherein a surface of the multivariate sub-images is greater, by a factor of between five and ten times, than a Representative Elementary Surface (RES) of a textured base of a tyre having no anomalies.

7. The method according to claim 6 , wherein sides of a square of a multivariate sub-image are formed by a number of pixels corresponding to a length of between 0.01*Φ and 0.1*Φ, with Φ corresponding to a diameter at a seat of the tyre.

8. The method according to claim 1 , wherein the data analysis tool is chosen from a set of factorial analysis tools that include:

a principal component factorial analysis (PCA) tool,

a correspondence factorial analysis (CFA) tool, and

a independent component analysis (ICA) tool.

9. The method according to claim 1 , wherein a plurality of relevant descriptors is selected by performing the method multiple times utilizing a descriptor chosen from a set of descriptors that includes:

a histogram (H W F (x k )) of grey levels,

a covariance

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of the grey levels in the circumferential direction or the radial direction, and

by line or by column, a mean, a variance, a standard deviation, extreme values, a deviation at maximum or minimum amplitude, a mean positive or negative crossing of the grey levels in the circumferential direction or the radial direction.

10. The method according to claim 9 , wherein the reduced factorial spaces of the relevant descriptors of which the separating power has been evaluated are combined into a common reduced factorial space (c H WF , c G WF , . . . , c N WF ).

11. The method according to claim 10 , wherein

the grey-level original image (f) of the inner surface of a tyre is captured and is transformed into the orthonormal space in which the x-axis (OX) represents the circumferential direction and the y-axis (OY) represents the radial direction,

the predetermined series of filters (F 1 f , F 2 f , . . . F L f ) is applied to the grey-level original image to obtain the multivariate original image,

the multivariate original image is split according to the predefined tiling in the axial direction and the circumferential directions, to obtain multivariate sub-images (W F k ) of the inner surface of the tyre,

each of the multivariate sub-images is transformed into one-dimensional vectors, using each of the selected relevant descriptors (H, G, . . . N), to obtain a simplified multivariate image of the inner surface of the tyre,

the simplified multivariate image is transformed into the common reduced factorial space (c H WF , c G WF , . . . , c N WF ), and

the sub-images containing an anomaly are located using a classifier suitable for identifying areas of the common reduced factorial space containing anomalies.

12. The method according to claim 11 , wherein the classifier is constructed using a Linear Discriminant Analysis (LDA) analysis tool or a Separators with Vast Margins (SVM) analysis tool.