Abstract: A computer implemented method for generating a region of interest on a digital image of a sidewall of a tyre, the sidewall having one or more embossed and/or engraved markings, is provided. The method comprises generating a histogram of oriented gradients feature map of the digital image, inputting the histogram of oriented gradients feature map into a trained convolutional neural network, wherein said trained convolutional neural network is configured to output a first probability based on the input histogram of oriented gradients feature map that a region of pixels of the digital image contains the embossed and/or engraved markings, and if the first probability is at or above a first predetermined threshold, accepting said region of pixels as said region of interest.

Abstract: A computer implemented method is proposed for classifying one or more embossed and/or engraved markings on a sidewall of a tyre into one or more classes comprising digital image data of the sidewall of the tyre. The method comprises generating a first image channel from a first portion of the digital image data relating to a corresponding first portion of the sidewall of the tyre. Generating the first image channel comprises performing histogram equalisation on the first portion of the digital image data to generate the first image channel. The method further comprises generating a first feature map using the first image channel and applying a first classifier to the first feature map to classify said embossed and/or engraved markings into one or more first classes.

Abstract: A computer implemented method is proposed for classifying one or more embossed and/or engraved markings on a sidewall of a tyre into one or more classes comprising digital image data of the sidewall of the tyre. The method comprises generating a first image channel from a first portion of the digital image data relating to a corresponding first portion of the sidewall of the tyre. Generating the first image channel comprises performing histogram equalisation on the first portion of the digital image data to generate the first image channel. The method further comprises generating a first feature map using the first image channel and applying a first classifier to the first feature map to classify said embossed and/or engraved markings into one or more first classes.

Abstract: A computer implemented method for generating a region of interest on a digital image of a sidewall of a tyre, the sidewall having one or more embossed and/or engraved markings, is provided. The method comprises generating a histogram of oriented gradients feature map of the digital image, inputting the histogram of oriented gradients feature map into a trained convolutional neural network, wherein said trained convolutional neural network is configured to output a first probability based on the input histogram of oriented gradients feature map that a region of pixels of the digital image contains the embossed and/or engraved markings, and if the first probability is at or above a first predetermined threshold, accepting said region of pixels as said region of interest.

Abstract: A system for measuring the depth of tread of a tyre (5) on a wheel (2) of a vehicle (1) whilst the wheel is rotating and moving along the ground. A camera (3) captures images whilst the tyre rotates for at least the major part of its circumference. Light sources (L1 to L4) are spaced longitudinally and are directed at an acute angle to the path of the tyre, to illuminate the tyre whilst images are captured. The images are analysed by data processing apparatus (8) and the tread depth is determined from the length of shadows (12, 13) in the gaps (11) between tread blocks (10). The light sources are activated and de-activated sequentially in accordance with signals from longitudinally spaced sensors (S1 to S4) which detect the presence of the tyre, so that when an image is captured of a portion of the tyre tread, only one light source is activated to illuminate that portion of the tyre tread.

Abstract: The condition of a tyre (5) on a wheel (2) is assessed while the wheel (2) is mounted on a vehicle (1) and while the vehicle (1) is moving. As the vehicle (1) moves, the tyre (5) rotates and moves longitudinally along a path of movement. An imaging device (3, 4) captures images of a plurality of different portions (7) of the periphery of the tyre (5), which has tread portions (10) separated by tread gaps (11), whilst the tyre (5) revolves. While the images are being captured, longitudinally spaced flash units (F1, F2, F3, F4) are activated to illuminate portions (7) of the periphery of the tyre (5). The flash units (F1, F2, F3, F4) are positioned to one side of the path of movement of the tyre (5) and direct light at an acute angle to the path of movement of the tyre (5) so that the light causes shadows to be cast in the tread gaps (11) between tread portions (10).

Abstract: The condition of a tyre (5) on a wheel (2) is assessed while the wheel (2) is mounted on a vehicle (1) and while the vehicle (1) is moving. As the vehicle (1) moves, the tyre (5) rotates and moves longitudinally along a path of movement. An imaging device (3, 4) captures images of a plurality of different portions (7) of the periphery of the tyre (5), which has tread portions (10) separated by tread gaps (11), whilst the tyre (5) revolves. While the images are being captured, longitudinally spaced flash units (F1, F2, F3, F4) are activated to illuminate portions (7) of the periphery of the tyre (5). The flash units (F1, F2, F3, F4) are positioned to one side of the path of movement of the tyre (5) and direct light at an acute angle to the path of movement of the tyre (5) so that the light causes shadows to be cast in the tread gaps (11) between tread portions (10).

Abstract: A system for measuring the depth of tread of a tyre (5) on a wheel (2) of a vehicle (1) whilst the wheel is rotating and moving along the ground. A camera (3) captures images whilst the tyre rotates for at least the major part of its circumference. Light sources (L1 to L4) are spaced longitudinally and are directed at an acute angle to the path of the tyre, to illuminate the tyre whilst images are captured. The images are analysed by data processing apparatus (8) and the tread depth is determined from the length of shadows (12, 13) in the gaps (11) between tread blocks (10). The light sources are activated and de-activated sequentially in accordance with signals from longitudinally spaced sensors (S1 to S4) which detect the presence of the tyre, so that when an image is captured of a portion of the tyre tread, only one light source is activated to illuminate that portion of the tyre tread.