CN113516608A - Tire defect detection method and device, and tire detection device - Google Patents
Tire defect detection method and device, and tire detection device Download PDFInfo
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Abstract
The invention provides a defect detection method and a defect detection device for a tire and tire detection equipment, wherein the method comprises the following steps: acquiring an original tire image; extracting an image of a belt area according to an original tire image; extracting a left shoulder area image and a right shoulder area image according to the belt area image; and positioning the belt lamination difference level area according to the width of the left shoulder area image and the width of the right shoulder area image. The detection method can effectively detect the difference level defect of the belted layer by detecting the edge image of the tire belt shoulder of the tire.
Description
Technical Field
The present invention relates to the field of defect detection technologies, and in particular, to a method and an apparatus for detecting defects of a tire, a tire detection device, and an electronic device.
Background
At present, a semi-steel tire defect identification algorithm mainly focuses on identifying impurities, air bubbles and defects of steel wire cords at a tire crown part, and is few in identification algorithm aiming at a belt area, wherein the belt area comprises shoulder areas at two sides and a middle multi-layer steel wire area, the multi-layer steel wire area generates a steel wire crossing phenomenon in an image due to different inclination directions of steel wires at different layers, and the shoulder area does not have the steel wire crossing phenomenon.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first object of the present invention is to provide a method for detecting a tire defect, which can effectively detect a belt step defect by detecting an edge image of a tire shoulder of a belt.
A second object of the present invention is to provide a tire defect detecting apparatus.
A third object of the present invention is to provide a tire testing device.
A fourth object of the invention is to propose an electronic device.
A fifth object of the present invention is to propose a computer-readable storage medium.
In order to achieve the above object, a first aspect of the present invention provides a method for detecting a defect of a tire, including: acquiring an original tire image; extracting an image of a belt area according to the original tire image; extracting a left shoulder area image and a right shoulder area image according to the belt area image; and positioning a belt lamination difference level region according to the width of the left shoulder region image and the width of the right shoulder region image.
According to the defect detection method of the tire, the original tire image is obtained, the belt region image is extracted according to the original tire image, the left shoulder region image and the right shoulder region image are extracted according to the belt region image, and the belt difference region is positioned according to the width of the left shoulder region image and the width of the right shoulder region image. Therefore, the method can effectively detect the belt layer difference defect by detecting the edge image of the tire belt layer shoulder.
In addition, the method for detecting the defect of the tire according to the above embodiment of the present invention may further have the following additional technical features:
according to an embodiment of the present invention, the extracting a belt region image from the original tire image includes: carrying out image enhancement processing on the original tire image to obtain an enhanced tire image; and extracting the image of the belt area according to the enhanced tire image.
According to an embodiment of the present invention, the image enhancement processing on the original tire image to obtain an enhanced tire image includes: and carrying out image enhancement processing on the original tire image by adopting an unsharp masking method to obtain the enhanced tire image.
According to an embodiment of the present invention, the extracting the belt region image from the enhanced tire image includes: carrying out relative binarization processing on the enhanced tire image to obtain a relative binarization tire image; positioning the outer edge of the belt layer according to the relative binaryzation tire image; and extracting the belt region image from the enhanced tire image according to the outer edge of the belt layer.
According to an embodiment of the present invention, the performing a relative binarization process on the enhanced tire image to obtain a relative binarization tire image includes: obtaining the relatively binarized tire image for each pixel in the enhanced tire image by: setting a first window with a fixed size by taking the pixel as a center; if the gray value of the pixel in the first window meets a set condition, marking the gray value of the central pixel as 0; if the gray value of the pixel in the first window does not meet the set condition, marking the gray value of the central pixel as 255; the set condition is that the gray value of the central pixel is smaller than a preset first gray threshold value, and the gray difference between the gray value of the central pixel and the maximum gray value of the pixel in the first window is larger than a preset second gray threshold value.
According to one embodiment of the present invention, said positioning of the belt outer edge from said relatively binarized tire image comprises: carrying out reverse color transformation processing on the relative binary tire image; sequentially performing expansion and corrosion treatment on the image subjected to the reverse color transformation treatment; performing vertical projection calculation on the image subjected to expansion and corrosion treatment to obtain a vertical projection gray value of each row of pixels; determining a left edge column and a right edge column which are closest to a vertical center line of the image after the expansion and corrosion treatment and of which the vertical projection gray values are smaller than a projection gray threshold value, wherein the projection gray threshold value is a first set multiple of a maximum value in the vertical projection gray values, and the first set multiple is smaller than 1; marking the gray value of the pixel positioned on the left side of the left edge column in the image after the expansion and corrosion treatment as 0, and marking the gray value of the pixel positioned on the right side of the edge column in the image after the expansion and corrosion treatment as 0 to obtain a marked image; in each row, left and right edge points are determined that are farthest from the vertical centerline of the marked image and have gray values other than 0, resulting in a left belt outer edge of the belt outer edges that consists of the determined left edge points and a right belt outer edge of the belt outer edges that consists of the determined right edge points.
According to one embodiment of the present invention, said positioning of the belt outer edge from said relatively binarized tire image comprises: and carrying out edge detection on the relative binarization tire image according to a trained edge detection model to obtain the outer edge of the belted layer.
According to an embodiment of the present invention, the extracting a left shoulder region image and a right shoulder region image according to the belt region image includes: carrying out local binarization processing on the belt area image to obtain a local binarization tire image; and extracting the left shoulder area image and the right shoulder area image according to the local binarization tire image.
According to an embodiment of the present invention, the performing the local binarization processing on the belt region image to obtain a local binarization tire image includes: setting a second window with a fixed size; traversing the belt region image by using the second window; if the gray value of the central pixel in the second window is larger than a local gray threshold, setting the gray value of the central pixel to be 255, wherein the local gray threshold is a second set multiple of the average gray value of the pixels in the second window, and the second set multiple is larger than 1; and if the gray value of the central pixel in the second window is equal to or smaller than the local gray threshold, setting the gray value of the central pixel to be 0.
According to one embodiment of the invention, the extracting the left shoulder area image and the right shoulder area image according to the local binary tire image comprises: deleting the part of the local binary tire image, of which the area of the connected domain is smaller than a set area threshold value, so as to obtain a tire shoulder area image; sequentially performing expansion and corrosion treatment on the tire shoulder area image; and performing median filtering processing on the edge of the image subjected to the expansion erosion processing to obtain the left shoulder area image and the right shoulder area image.
In order to achieve the above object, a second aspect of the present invention provides a defect detecting apparatus for a tire, including: the image acquisition module is used for acquiring an original tire image; the image extraction module is used for extracting a belt area image according to the original tire image and extracting a left shoulder area image and a right shoulder area image according to the belt area image; and the area positioning module is used for positioning a belt layer difference area according to the width of the left shoulder area image and the width of the right shoulder area image.
According to the defect detection device of the tire, the image acquisition module is used for acquiring an original tire image, the image extraction module is used for extracting the belt region image according to the original tire image, the image extraction module is used for extracting the left shoulder region image and the right shoulder region image according to the belt region image, and the region positioning module is used for positioning the belt difference region according to the width of the left shoulder region image and the width of the right shoulder region image. Therefore, the device can effectively detect the belt layer difference defect by detecting the edge image of the tire belt layer shoulder.
In order to achieve the above object, a tire testing apparatus is provided according to a third embodiment of the present invention, which includes the above tire defect detecting device.
According to the tire detection device provided by the embodiment of the invention, the belt layer difference level defect can be effectively detected through the tire defect detection device.
To achieve the above object, a fourth aspect of the present invention provides an electronic device, including: the tire defect detection system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the tire defect detection method.
The electronic equipment of the embodiment of the invention can effectively detect the belt layer difference level defect by executing the defect detection method of the tire.
In order to achieve the above object, a fifth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the above-mentioned defect detecting method for a tire.
The computer-readable storage medium of the embodiment of the invention can effectively detect the belt layer difference level defect by executing the defect detection method of the tire.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a method of defect detection of a tire according to an embodiment of the present invention;
FIGS. 2a-2b are schematic illustrations of an original image and a tire image after enhancement processing of the original tire image according to one embodiment of the present invention;
FIG. 3 is a schematic illustration of a tire image after a relative binarization process in accordance with one embodiment of the present invention;
4 a-4 c are schematic diagrams of recognition results of an edge detection model according to an embodiment of the invention;
fig. 5a and 5b are schematic diagrams of images after local binarization processing is performed on a band-layer area image according to an embodiment of the present invention;
FIGS. 6a and 6b are schematic illustrations of a left shoulder region image and a right shoulder region image according to one embodiment of the invention;
FIG. 7 is a schematic of the boundaries of the left shoulder region image and the right shoulder region image according to one embodiment of the invention;
FIG. 8 is a block schematic view of a defect detection apparatus for a tire according to an embodiment of the present invention;
FIG. 9 is a block schematic view of a tire testing apparatus according to an embodiment of the present invention;
FIG. 10 is a block schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A defect detection method of a tire, a defect detection device of a tire, a tire detection apparatus, and an electronic apparatus according to an embodiment of the present invention will be described below with reference to the drawings.
Fig. 1 is a flowchart of a method of detecting a defect of a tire according to an embodiment of the present invention.
As shown in fig. 1, the method for detecting a defect of a tire according to an embodiment of the present invention may include the steps of:
s1, obtaining an original tire image, wherein the original tire image can be obtained by transmission imaging and can display the internal structure of the tire, for example, can be the tire image shot by an X-ray imaging device.
And S2, extracting a belt region image according to the original tire image.
In one embodiment of the present invention, extracting a belt region image from an original tire image includes:
and S21, carrying out image enhancement processing on the original tire image to obtain an enhanced tire image.
In one embodiment of the present invention, performing image enhancement processing on an original tire image to obtain an enhanced tire image includes: and performing image enhancement processing on the original tire image by adopting an unsharp masking method to obtain an enhanced tire image.
Specifically, the method for enhancing the image of the input original tire image by using the USM (Unsharp Masking) method can remove some fine interference details and noise, and comprises the following main steps: the original tire image is gaussian blurred, and then the original image is subtracted by a coefficient multiplied by the gaussian blur, where the coefficient is an empirical value and can be set according to a desired enhancement effect, for example, the USM sharpening formula is: (original tire image-w gaussian blur)/(1-w); wherein w represents the weight, i.e. the empirical value, and the general value range is: 0.1 to 0.9. Finally, scaling (normalizing) the image to be between 0 and 255, for example, if the gray value of the subtraction result is less than 0, taking 0; if the gray value of the subtraction result is greater than 255, 255 is taken, wherein the original tire image and the tire image after USM sharpening are shown in fig. 2a and 2b, respectively.
And S22, extracting a belt region image according to the enhanced tire image.
In one embodiment of the present invention, extracting a belt region image from an enhanced tire image includes: carrying out relative binarization processing on the enhanced tire image to obtain a relative binarization tire image; positioning the outer edge of the belt layer according to the relative binaryzation tire image; and extracting the belt region image from the enhanced tire image according to the outer edge of the belt layer.
In one embodiment of the present invention, performing a relative binarization process on the enhanced tire image to obtain a relative binarization tire image includes: obtaining a relatively binarized tire image for each pixel in the enhanced tire image by: setting a first window with a fixed size by taking the pixel as a center; if the gray value of the pixel in the first window meets the set condition, marking the gray value of the central pixel as 0; if the gray value of the pixel in the first window does not meet the set condition, marking the gray value of the central pixel as 255; the setting conditions are that the gray value of the central pixel is smaller than a preset first gray threshold, and the gray difference between the gray value of the central pixel and the maximum gray value of the pixel in the first window is larger than a preset second gray threshold, wherein the first gray threshold can be set according to the gray value of the steel wire (the gray value of the steel wire can be determined according to an empirical value or a mode of counting the average gray value of the steel wire), and the second gray threshold can be set according to the difference value between the gray value of the steel wire and the white area.
Specifically, the method for extracting a relatively dark area by performing a relative binarization process on the tire image after the USM sharpening includes: traversing each gray value on the image, setting a window (a first window) with a fixed size by taking the gray value of the pixel as a center, and performing the following operations on the image in the window: when the gray value of the central pixel is smaller than a preset first gray threshold, the gray difference between the gray value of the central pixel and the maximum gray value of the pixel in the window is calculated, when the gray difference is larger than a preset second gray threshold, the gray value of the central pixel is marked as 0, otherwise, the gray value of the central pixel is marked as 255. A region (belt region) of relatively dark color can be extracted as a result, in which the image after the relative binarization processing is as shown in fig. 3.
Note that, when the gradation value of a pixel in the first window satisfies the setting condition, the gradation value of the pixel may be marked as 0, or when the gradation value of the center pixel satisfies the setting condition, the gradation value of the pixel may be marked as 0.
Further, in one embodiment of the present invention, the positioning of the belt layer outer edge with respect to the binarized tire image comprises: carrying out reverse color transformation processing on the relative binary tire image; sequentially performing expansion and corrosion treatment on the image subjected to the reverse color transformation treatment; performing vertical projection calculation on the image subjected to expansion and corrosion treatment to obtain a vertical projection gray value of each row of pixels; determining a left edge column and a right edge column which are closest to the vertical center line of the image after the expansion and corrosion treatment and have vertical projection gray values smaller than a projection gray threshold, wherein the projection gray threshold is a first set multiple of the maximum value of the vertical projection gray values, and the first set multiple is smaller than 1; marking the gray value of the pixel positioned on the left side of the left edge column in the image after the expansion and corrosion treatment as 0, and marking the gray value of the pixel positioned on the right side of the right edge column in the image after the expansion and corrosion treatment as 0 to obtain a marked image; in each row, left and right edge points that are farthest from the vertical centerline of the marked image and whose gray value is not 0 are determined, resulting in a left belt outer edge of the belt outer edges composed of the determined left edge points and a right belt outer edge of the belt outer edges composed of the determined right edge points.
Wherein determining the left edge column and the right edge column that are closest to the vertical center line of the expanded and eroded image and whose vertical projection gray values are less than the projection gray threshold may include: traversing from the middle of the image after expansion and corrosion treatment to two sides according to columns, determining a left edge column and a right edge column, traversing leftwards, when the vertical projection gray value is smaller than the projection gray threshold value after the first traversal, taking the column as the left edge column, traversing rightwards, when the vertical projection gray value is smaller than the projection gray threshold value after the first traversal, taking the column as the left edge column, wherein the projection gray threshold value is a first set multiple of the maximum value in the vertical projection gray value, and the first set multiple is smaller than 1.
Determining left and right edge points that are farthest from the vertical centerline of the marked image and have gray values other than 0 in each row, resulting in a left belt outer edge of the belt outer edges consisting of the determined left edge points and a right belt outer edge of the belt outer edges consisting of the determined right edge points, may include: traversing from the leftmost side to the middle of the marked image according to lines, taking the position of the pixel with the gray value not being 0 traversed for the first time in each line as the outer edge of the left belt in the outer edge of the belt, traversing from the rightmost side to the middle of the marked image according to lines, and taking the position of the pixel with the gray value not being 0 traversed for the first time in each line as the outer edge of the right belt in the outer edge of the belt. The first preset multiple may be calibrated according to an actual situation, for example, the first preset multiple may be 0.03 to 0.06.
It should be noted that the left and right edge rows are linear, and the left and right belt outer edges among the belt outer edges may be linear or curved, and the latter is a relatively true edge line.
Specifically, the relatively binarized tire image is subjected to inverse color conversion, that is, the original gradation value is 255 to 0, and the original gradation value is 0 to 255. And then, carrying out expansion and corrosion treatment on the image after the reverse color transformation. Performing vertical projection gray value calculation on the image subjected to the expansion corrosion treatment (specifically, calculating a gray average value of each column of pixels, and taking the average value as a vertical projection gray value of the column of pixels), calculating a maximum projection gray value, traversing the image from the middle of the column to two sides, and when the projection value is smaller than a maximum projection gray value coefficient (the value may be a fixed value, for example, 0.03-0.06, and if not, the projection value is close to 0.), listing the image as an edge column of the belt layer, thereby obtaining edge columns (a left edge column and a right edge column) of the two belt layers, wherein gray values of pixels outside the left edge column and outside the right edge column are all marked as 0, so that the marked image can be obtained. Traversing according to lines from the leftmost side to the middle of the marked image, wherein if the projection gray value is not 0, the position is the outer edge of the left belt; by analogy, if the projection gray value is not 0 from the rightmost side to the middle of the image, the position is the outer edge of the right belt layer, and therefore the outer edge area of the belt layer can be obtained.
According to another embodiment of the invention, positioning the belt outer edge with respect to the binarized tire image comprises: and carrying out edge detection on the relative binary tire image according to the trained edge detection model to obtain the outer edge of the belted layer.
Specifically, in order to improve the versatility and the detection accuracy of the method, an rcf (cycle robust Features for Edge detection) Edge detection method may be used for detection, an Edge detection model may be established in advance, for example, the outer Edge of the belt layer may be labeled to obtain the Edge detection model, and then the detection model may be used to directly detect the outer Edge of the belt layer. For example, the model is trained by using a plurality of collected labeled tire samples (as shown in the labeled image) to obtain a detection model capable of identifying the belt edge of the tire, wherein the belt outer edge result is shown in fig. 4, fig. 4a is the original tire image, fig. 4b is the tire labeled sample, and fig. 4c is the tire detection result.
And S3, extracting a left shoulder area image and a right shoulder area image according to the belt area image.
In one embodiment of the present invention, extracting a left shoulder region image and a right shoulder region image from a belt region image includes: carrying out local binarization processing on the image of the belted area to obtain a local binarization tire image; and extracting a left shoulder area image and a right shoulder area image according to the local binarization tire image.
In one embodiment of the present invention, the local binarization processing is performed on the bundling layer region image to obtain a local binarization tire image, and the method includes: setting a second window with a fixed size; traversing the banding layer area image by the second window; if the gray value of the central pixel in the second window is larger than the local gray threshold, setting the gray value of the pixel to be 255, wherein the local gray threshold is a second set multiple of the average gray value of the pixel in the second window, and the second set multiple is larger than 1; and if the gray value of the pixel in the second window is equal to or smaller than the local gray threshold, setting the gray value of the pixel to be 0, wherein the second preset multiple can be 1.26-1.37.
Specifically, as shown in fig. 5a and 5b, where fig. 5a is the belt region in the extracted USM image, and fig. 5b is the image after the local binarization processing, the outer edge of the belt layer can be obtained in the above manner, the belt region in the USM image is extracted, the region is locally binarized, that is, the region is traversed by a window (second window) with a certain fixed size, when the gray value of the central pixel in the window is greater than the average gray value of the window pixels by a second set multiple (the value is an empirical value, and may be 1.35, for example), the gray value of the pixel is set to 255, otherwise, the gray value of the pixel is 0.
Further, according to an embodiment of the present invention, extracting a left shoulder region image and a right shoulder region image from a local binarized tire image includes: deleting the part of the local binary tire image, of which the area of the connected domain is smaller than a set area threshold value, so as to obtain a tire shoulder area image; sequentially performing expansion and corrosion treatment on the tire shoulder area image; and performing median filtering processing on the edge of the image subjected to the expansion erosion processing to obtain a left shoulder area image and a right shoulder area image.
Specifically, the connected domain extraction is performed on the image after the local binarization processing, and the connected domain with a small area is set to be 0, so that the approximate area of the tire shoulder can be obtained, as shown in fig. 6a, the area of the connected domain at the steel wire intersection part and the area of the tire shoulder steel wire area can be roughly counted, and deletion is generally set to be 15-25 pixels or less (i.e. the area of the connected domain at the steel wire intersection part). And performing swelling and corroding operation on the image of the tire shoulder area to obtain the tire shoulder area, such as a white area shown in fig. 6b, then performing median filtering on the edges of the tire shoulder at the left side and the right side, and eliminating the interference of abnormal points to obtain the image of the left tire shoulder area and the image of the right tire shoulder area.
And S4, positioning a belt step area according to the width of the left shoulder area image and the width of the right shoulder area image.
Specifically, as shown in fig. 7, two boundary lines of the left shoulder region image and two boundary lines of the right shoulder region image are obtained, where the two boundary lines of the left shoulder region image are denoted as a and B, and the two boundary lines of the right shoulder region image are denoted as C and D.
The set positioning criteria may include determining whether the tire is defective based on the width of the left shoulder area image, and then considering the tire as defective when the width between a and B is not within the first preset threshold range. The final positioning result may include one or more or all regions between a and B having a width not within the first preset threshold.
The set positioning criteria may include determining whether the tire is defective based on the width of the right shoulder area image, and then considering the tire as defective when the width between C and D is not within a second preset threshold range. If such defects are present, the result of the final localization may include one or more or all regions between C and D having a width not within the second preset threshold.
The set positioning criterion may include determining whether the tire has a defect based on a difference between the width of the left shoulder area image and the width of the right shoulder area image, and then considering the tire as having a defect when the difference between the width between a and B and the width between C and D is not within a third preset threshold range. If such defects are present, the results of the final positioning may include: and the width difference between A and B and the width difference between C and D are not in one or more or all of the third preset threshold range.
It should be noted that the set criteria may include any one of the three criteria described above, or any combination of a plurality of criteria, and if no defective area is located according to all the criteria set, the tire may be considered normal.
It should be noted that the first preset threshold range, the second preset threshold range, and the third preset threshold range may be calibrated according to actual situations. In addition, in calculating the difference between the two boundary lines, the average value of each of all the differences between the two boundary lines may be used as the width difference between the final two boundary lines.
In summary, according to the defect detection method of the tire of the embodiment of the present invention, the original tire image is obtained, the belt region image is extracted according to the original tire image, the left shoulder region image and the right shoulder region image are extracted according to the belt region image, and the belt difference region is located according to the width of the left shoulder region image and the width of the right shoulder region image. Therefore, the method can effectively detect the belt layer difference defect by detecting the edge image of the tire belt layer shoulder.
Fig. 8 is a block schematic diagram of a defect detecting apparatus of a tire according to an embodiment of the present invention.
As shown in fig. 8, the defect detecting apparatus of a tire according to an embodiment of the present invention may include: an image acquisition module 10, an image extraction module 20 and an area location module 30.
The image acquisition module 10 is used for acquiring an original tire image. The image extraction module 20 is configured to extract a belt region image according to the original tire image, and extract a left shoulder region image and a right shoulder region image according to the belt region image. The region location module 30 is used to locate the band step difference region according to the width of the left shoulder region image and the width of the right shoulder region image.
According to an embodiment of the present invention, when extracting the belt region image from the original tire image, the image extraction module 20 is specifically configured to perform image enhancement processing on the original tire image to obtain an enhanced tire image; and extracting the image of the belt area according to the enhanced tire image.
According to an embodiment of the present invention, when the image extraction module 20 performs the image enhancement processing on the original tire image to obtain the enhanced tire image, it is specifically configured to perform the image enhancement processing on the original tire image by using the unsharp masking method to obtain the enhanced tire image.
According to an embodiment of the present invention, when extracting the belt region image from the enhanced tire image, the image extraction module 20 is specifically configured to perform a relative binarization process on the enhanced tire image to obtain a relative binarization tire image; positioning the outer edge of the belt layer according to the relative binaryzation tire image; and extracting the belt region image from the enhanced tire image according to the outer edge of the belt layer.
According to an embodiment of the present invention, the image extraction module 20 performs a relative binarization process on the enhanced tire image to obtain a relative binarization tire image, and is specifically configured to perform the following operations on each pixel in the enhanced tire image to obtain the relative binarization tire image: setting a first window with a fixed size by taking the pixel as a center; if the gray value of the pixel in the first window meets the set condition, marking the gray value of the central pixel as 0; if the gray value of the pixel in the first window does not meet the set condition, marking the gray value of the central pixel as 255; the setting condition is that the gray value of the pixel is smaller than a preset first gray threshold value, and the gray difference between the gray value of the central pixel and the maximum gray value of the pixel in the first window is larger than a preset second gray threshold value.
According to one embodiment of the present invention, the image extraction module 20 is specifically configured to perform inverse color transformation processing on the relatively binarized tire image when the outer edge of the belt layer is located according to the relatively binarized tire image; sequentially performing expansion and corrosion treatment on the image subjected to the reverse color transformation treatment; determining a left edge column and a right edge column which are closest to the vertical center line of the image after the expansion and corrosion treatment and have vertical projection gray values smaller than a projection gray threshold, wherein the projection gray threshold is a first set multiple of the maximum value of the vertical projection gray values, and the first set multiple is smaller than 1; marking the gray value of the pixel positioned on the left side of the left edge column in the image after the expansion and corrosion treatment as 0, and marking the gray value of the pixel positioned on the right side of the right edge column in the image after the expansion and corrosion treatment as 0 to obtain a marked image; in each row, left and right edge points that are farthest from the vertical centerline of the marked image and whose gray value is not 0 are determined, resulting in a left belt outer edge of the belt outer edges composed of the determined left edge points and a right belt outer edge of the belt outer edges composed of the determined right edge points.
According to an embodiment of the present invention, when the outer edge of the belt layer is located according to the relatively binarized tire image, the image extraction module 20 is specifically configured to perform edge detection on the relatively binarized tire image according to a trained edge detection model to obtain the outer edge of the belt layer.
According to an embodiment of the present invention, the image extraction module 20 extracts a left shoulder region image and a right shoulder region image according to the belt region image, and is specifically configured to perform local binarization processing on the belt region image to obtain a local binarization tire image; and extracting a left shoulder area image and a right shoulder area image according to the local binarization tire image.
According to an embodiment of the present invention, the image extraction module 20 performs local binarization on the band-to-band layer region image to obtain a local binarization tire image, specifically, is configured to set a second window with a fixed size; traversing the image of the bundling layer area by using a second window; if the gray value of the central pixel in the second window is larger than the local gray threshold, setting the gray value of the central pixel to be 255, wherein the local gray threshold is a second set multiple of the average gray value of the pixels in the second window, and the second set multiple is larger than 1; and if the gray value of the central pixel in the second window is equal to or smaller than the local gray threshold value, setting the gray value of the central pixel to be 0.
According to an embodiment of the present invention, the image extraction module 20 extracts a left shoulder region image and a right shoulder region image according to the local binary tire image, and is specifically configured to delete a portion of the local binary tire image where the area of a connected domain is smaller than a set area threshold value, so as to obtain a shoulder region image; sequentially performing expansion and corrosion treatment on the tire shoulder area image; and performing median filtering processing on the edge of the image subjected to the expansion erosion processing to obtain a left shoulder area image and a right shoulder area image.
It should be noted that details not disclosed in the tire defect detecting device according to the embodiment of the present invention refer to details disclosed in the tire defect detecting method according to the embodiment of the present invention, and detailed description thereof is omitted here.
According to the defect detection device of the tire, the image acquisition module is used for acquiring an original tire image, the image extraction module is used for extracting the belt region image according to the original tire image, the image extraction module is used for extracting the left shoulder region image and the right shoulder region image according to the belt region image, and the region positioning module is used for positioning the belt difference region according to the width of the left shoulder region image and the width of the right shoulder region image. Therefore, the device can effectively detect the belt layer difference defect by detecting the edge image of the tire belt layer shoulder.
FIG. 9 is a block schematic diagram of a tire testing apparatus according to an embodiment of the present invention.
As shown in fig. 9, a tire testing apparatus 100 according to an embodiment of the present invention may include: the tire defect detecting apparatus 110 described above.
According to the tire detection device provided by the embodiment of the invention, the belt layer difference level defect can be effectively detected through the tire defect detection device.
FIG. 10 is a block schematic diagram of an electronic device according to an embodiment of the invention.
As shown in fig. 10, the electronic device 200 of the embodiment of the present invention may include: the memory 210, the processor 220, and a computer program stored in the memory 210 and operable on the processor 220, when the processor 220 executes the program, the method for detecting a defect of a tire as described above is implemented.
The electronic equipment of the embodiment of the invention can effectively detect the belt layer difference level defect by executing the defect detection method of the tire.
Furthermore, an embodiment of the present invention also proposes a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the above-described defect detection method for a tire.
The computer-readable storage medium of the embodiment of the invention can effectively detect the belt layer difference level defect by executing the defect detection method of the tire.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (14)
1. A method of detecting a defect in a tire, comprising:
acquiring an original tire image;
extracting an image of a belt area according to the original tire image;
extracting a left shoulder area image and a right shoulder area image according to the belt area image;
and positioning a belt lamination difference level region according to the width of the left shoulder region image and the width of the right shoulder region image.
2. The method of claim 1, wherein extracting a belt region image from the original tire image comprises:
carrying out image enhancement processing on the original tire image to obtain an enhanced tire image;
and extracting the image of the belt area according to the enhanced tire image.
3. The method of claim 2, wherein the image enhancement of the original tire image to obtain an enhanced tire image comprises:
and carrying out image enhancement processing on the original tire image by adopting an unsharp masking method to obtain the enhanced tire image.
4. The method of claim 2, wherein the extracting the belt region image from the enhanced tire image comprises:
carrying out relative binarization processing on the enhanced tire image to obtain a relative binarization tire image;
positioning the outer edge of the belt layer according to the relative binaryzation tire image;
and extracting the belt region image from the enhanced tire image according to the outer edge of the belt layer.
5. The defect detection method of claim 4, wherein said relatively binarizing the enhanced tire image to obtain a relatively binarized tire image comprises:
obtaining the relatively binarized tire image for each pixel in the enhanced tire image by:
setting a first window with a fixed size by taking the pixel as a center; if the gray value of the pixel in the first window meets a set condition, marking the gray value of the central pixel as 0; if the gray value of the pixel in the first window does not meet the set condition, marking the gray value of the central pixel as 255; the set condition is that the gray value of the central pixel is smaller than a preset first gray threshold value, and the gray difference between the gray value of the central pixel and the maximum gray value of the pixel in the first window is larger than a preset second gray threshold value.
6. The defect detection method of claim 4 wherein said locating a belt outer edge from said relatively binarized tire image comprises:
carrying out reverse color transformation processing on the relative binary tire image;
sequentially performing expansion and corrosion treatment on the image subjected to the reverse color transformation treatment;
performing vertical projection calculation on the image subjected to expansion and corrosion treatment to obtain a vertical projection gray value of each row of pixels;
determining a left edge column and a right edge column which are closest to a vertical center line of the image after the expansion and corrosion treatment and of which the vertical projection gray values are smaller than a projection gray threshold value, wherein the projection gray threshold value is a first set multiple of a maximum value in the vertical projection gray values, and the first set multiple is smaller than 1;
marking the gray value of the pixel positioned on the left side of the left edge column in the image after the expansion and corrosion treatment as 0, and marking the gray value of the pixel positioned on the right side of the right edge column in the image after the expansion and corrosion treatment as 0 to obtain a marked image;
in each row, left and right edge points are determined that are farthest from the vertical centerline of the marked image and have gray values other than 0, resulting in a left belt outer edge of the belt outer edges that consists of the determined left edge points and a right belt outer edge of the belt outer edges that consists of the determined right edge points.
7. The defect detection method of claim 4 wherein said locating a belt outer edge from said relatively binarized tire image comprises:
and carrying out edge detection on the relative binarization tire image according to a trained edge detection model to obtain the outer edge of the belted layer.
8. The defect detection method of claim 1, wherein the extracting a left shoulder region image and a right shoulder region image from the belt region image comprises:
carrying out local binarization processing on the belt area image to obtain a local binarization tire image;
and extracting the left shoulder area image and the right shoulder area image according to the local binarization tire image.
9. The defect detection method according to claim 8, wherein said performing a local binarization process on the belt region image to obtain a local binarization tire image comprises:
setting a second window with a fixed size;
traversing the belt region image by using the second window;
if the gray value of the central pixel in the second window is larger than a local gray threshold, setting the gray value of the central pixel to be 255, wherein the local gray threshold is a second set multiple of the average gray value of the pixels in the second window, and the second set multiple is larger than 1;
and if the gray value of the central pixel in the second window is equal to or smaller than the local gray threshold, setting the gray value of the central pixel to be 0.
10. The defect detection method of claim 8, wherein said extracting the left shoulder region image and the right shoulder region image from the local binarized tire image comprises:
deleting the part of the local binary tire image, of which the area of the connected domain is smaller than a set area threshold value, so as to obtain a tire shoulder area image;
sequentially performing expansion and corrosion treatment on the tire shoulder area image;
and performing median filtering processing on the edge of the image subjected to the expansion erosion processing to obtain the left shoulder area image and the right shoulder area image.
11. A defect detecting apparatus for a tire, comprising:
the image acquisition module is used for acquiring an original tire image;
the image extraction module is used for extracting a belt area image according to the original tire image and extracting a left shoulder area image and a right shoulder area image according to the belt area image;
and the area positioning module is used for positioning a belt layer difference area according to the width of the left shoulder area image and the width of the right shoulder area image.
12. A tire inspection apparatus, comprising: a defect detecting apparatus for a tire according to claim 11.
13. An electronic device, comprising: memory, processor and computer program stored on said memory and executable on said processor, said processor implementing, when executing said program, a method of defect detection of a tyre according to any one of claims 1 to 10.
14. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a method for defect detection of a tyre according to any one of claims 1 to 10.
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