Intelligent identification method for consistency of teeth of bulldozer tooth block

Abstract

The present application relates to the technical field of image processing, in particular to a bulldozer tooth block tooth consistency intelligent identification method, comprising: obtaining a reference connected domain according to the gray scale distribution of all pixel points in a preset local window of each defect connected domain, and the difference between data in an edge chain code sequence of each defect connected domain; obtaining a target connected domain according to the gradient change vector of each reference connected domain and the difference between all edge pixel points in each reference connected domain; obtaining a projection vector of each reference connected domain according to the decreasing vector; and obtaining a bulldozer tooth block tooth consistency identification result according to the difference between the projection vectors and the gradient change vectors of any two target connected domains, and the distance between any two target connected domains. The present application improves the accuracy of the bulldozer tooth block tooth crack detection, and also improves the accuracy of the bulldozer tooth block tooth consistency identification result.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and more specifically to an intelligent recognition method for tooth consistency of bulldozer tooth blocks. Background Technology

[0002] Bulldozer teeth are a key component of bulldozer blocks, directly contacting and acting on the ground or working materials, undertaking important tasks such as digging, pushing, and loading. During operation, the teeth are subjected to significant wear and impact. When cracks appear in the teeth of bulldozer blocks, it may lead to safety hazards such as breakage or detachment of the bulldozer during operation. Therefore, crack identification and detection of teeth is particularly important during the tooth production process.

[0003] When identifying and detecting tooth cracks in the production process, edge detection can be used to detect tooth cracks. However, since there are uneven areas on the teeth in addition to cracks, the presence of uneven areas will affect the detection of cracks, which will reduce the accuracy of the detection of tooth cracks in bulldozer blocks, resulting in low accuracy of the tooth consistency identification results of bulldozer blocks. Summary of the Invention

[0004] This invention provides an intelligent recognition method for tooth consistency in bulldozer tooth blocks to solve existing problems.

[0005] The intelligent identification method for tooth consistency of bulldozer tooth blocks of the present invention adopts the following technical solution:

[0006] One embodiment of the present invention provides a method for intelligent recognition of tooth consistency in bulldozer tooth blocks, the method comprising the following steps:

[0007] Obtain images of the teeth of the bulldozer's tooth block;

[0008] Obtain several defect connected components in the tooth image, and obtain the edge chain code sequence for each defect connected component; based on the grayscale distribution of pixels within a preset local window of all pixels in each defect connected component and the differences between different data in each edge chain code sequence, select reference connected components from all defect connected components;

[0009] Obtain the gradient change vector of each reference connected component, and obtain the decreasing vector of each edge pixel in each reference connected component; based on the difference between the gradient change vector of each reference connected component and the decreasing vector of all edge pixels in each reference connected component, select the target connected component from all reference connected components.

[0010] The projection vector of each reference connected component is obtained by using the decreasing vector of all edge pixels in each reference connected component. Based on the difference between the projection vectors of any two target connected components, the difference between the gradient change vectors of any two target connected components, and the distance between the center points of any two target connected components, all target connected components are merged to obtain the tooth consistency recognition result of the bulldozer tooth block.

[0011] Furthermore, the specific steps for obtaining several defect connected regions in the tooth image and obtaining the edge chain code sequence of each defect connected region are as follows:

[0012] The edge detection of the bulldozer tooth block image is performed using the Canny edge detection algorithm. Then, the edge detection results of the bulldozer tooth block image are processed by binarization dilation to obtain several defect connected components.

[0013] The edge chain code sequence of each defective connected component is obtained using the Freeman chain code algorithm.

[0014] Furthermore, the specific steps for selecting reference connected components from all defective connected components based on the grayscale distribution of pixels within a preset local window of all pixels in each defective connected component and the differences between different data in each edge chain code sequence are as follows:

[0015] Based on the grayscale distribution of pixels within the local window of all pixels in each defect connected region, the probability that each defect connected region belongs to a crack defect can be obtained.

[0016] The degree to which each defective connected region is a crack defect is obtained by considering the differences between adjacent data in the edge chain code sequence of each defective connected region and the differences between each data in the edge chain code sequence and the mean of all data in the edge chain code sequence.

[0017] Based on the probability that each defect connected region belongs to a crack defect and the degree to which each defect connected region is a crack defect, the crack degree of each defect connected region is obtained. Among them, the probability that each defect connected region belongs to a crack defect is positively correlated with the crack degree of each defect connected region, and the degree to which each defect connected region is a crack defect is positively correlated with the crack degree of each defect connected region.

[0018] If the degree of crack is greater than a preset first threshold All defective connected components are denoted as reference connected components.

[0019] Furthermore, the specific calculation method for determining the probability that each defect connected region belongs to a crack defect based on the grayscale distribution of pixels within a local window of all pixels in each defect connected region is as follows:

[0020]

[0021] In the formula, Indicates the first The first defective connected component The pixels within the local window of the nth pixel belong to the nth pixel. The average grayscale value of all pixels in a defective connected region. Indicates the first The first defective connected component Pixels within a local window of pixel number 1 do not belong to the 1st pixel. The average grayscale value of all pixels in a defective connected region. Indicates the first The number of all pixels in a defective connected domain. It is the absolute value symbol. This represents an exponential function with the natural constant as its base. Indicates the first The probability that a defective connected domain belongs to a crack defect.

[0022] Furthermore, the specific calculation method for determining the degree to which each defective connected region is a crack defect based on the differences between adjacent data in the edge chain code sequence of each defective connected region and the differences between each data in the edge chain code sequence of each defective connected region and the mean of all data in the edge chain code sequence is as follows:

[0023]

[0024] In the formula, Indicates the first The first edge chain code sequence of the defective connected component One data point, Indicates the first The first edge chain code sequence of the defective connected component One data point, Indicates the first The mean of all data in the edge chain code sequence of a defective connected component. Indicates the first The number of all data in the edge chain code sequence of a defective connected component It is the absolute value symbol. Represents a linear normalization function. Indicates the first Each defect connected region represents the degree of the crack defect.

[0025] Furthermore, the specific steps for obtaining the gradient change vector of each reference connected component are as follows:

[0026] Obtain the minimum bounding rectangle of each reference connected component; draw a line segment through the center point of the minimum bounding rectangle that is parallel to the short side and has the same length as the short side, and denote it as the first line segment of the minimum bounding rectangle of each reference connected component; draw a line segment through the center point of the minimum bounding rectangle that is parallel to the long side and has the same length as the long side, and denote it as the second line segment of the minimum bounding rectangle of each reference connected component.

[0027] Divide the minimum bounding rectangle of each reference connected component into two target regions based on the first line segment of the minimum bounding rectangle. Calculate the mean gradient magnitude of all pixels in each target region. The target region with the largest mean gradient magnitude is designated as the first target region, and the target region with the smallest mean gradient magnitude is designated as the second target region.

[0028] Based on the second line segment of the minimum bounding rectangle of each reference connected region, the first target region, and the second target region, the gradient change vector of each reference connected region is obtained. The magnitude of the gradient change vector of each reference connected region is equal to the length of the second line segment of the minimum bounding rectangle of each reference connected region, and the direction of the gradient change vector of each reference connected region points from the first target region to the second target region.

[0029] Furthermore, the specific steps for obtaining the decreasing vector of each edge pixel in each reference connected component are as follows:

[0030] Obtain the edge pixels in each reference connected component. Find the pixel with the smallest gradient magnitude in its eight-neighborhood that is less than the edge pixel itself, and denote it as the target pixel of that edge pixel. Use the direction from each edge pixel to its corresponding target pixel as the direction of its decreasing vector. If the target pixel of each edge pixel belongs to one of its four neighboring pixels, the magnitude of the decreasing vector is 1 unit. If the target pixel belongs to one of its D neighboring pixels, the magnitude of the decreasing vector is... ;

[0031] If there is no matching target pixel in the eight neighborhoods of each edge pixel, then the decreasing vector of each edge pixel is replaced with a 0 vector.

[0032] Furthermore, the specific calculation method for selecting the target connected component from all reference connected components based on the difference between the gradient change vector of each reference connected component and the decreasing vector of all edge pixels within each reference connected component is as follows:

[0033]

[0034] In the formula, Indicates the first The first reference connected component A decreasing vector of edge pixels, Indicates the first Gradient change vectors of each reference connected component Indicates the first The first reference connected component The magnitude of the decreasing vector of each edge pixel. Indicates the first The first reference connected component The decreasing vector of the nth edge pixel and the nth edge pixel Cosine similarity between gradient transformation vectors of reference connected components Indicates the first The number of all edge pixels in a reference connected region Represents a linear normalization function. Indicates the first The probability that a reference connected region is a cracked connected region;

[0035] The probability that the reference connected component is a fractured connected component is greater than a preset second threshold. All reference connected components are denoted as the target connected component.

[0036] Furthermore, the specific steps for obtaining the projection vector of each reference connected region based on the decreasing vector of all edge pixels in each reference connected region are as follows:

[0037] Will Recorded as the number The first reference connected component The decreasing projection vector of the i-th edge pixel, for the i-th edge pixel Perform vector operations on the decreasing projection vectors of all edge pixels in the nth reference connected region, and denote the result of the vector operation as the nth... Projection vectors of each reference connected component;

[0038] In the formula, Indicates the first The magnitude of the gradient change vector of a reference connected region.

[0039] Furthermore, the specific steps for merging all target connected components based on the difference between the projection vectors of any two target connected components, the difference between the gradient change vectors of any two target connected components, and the distance between the center points of any two target connected components to obtain the tooth consistency recognition result of the bulldozer tooth block are as follows:

[0040] The probability of merging any two target connected components is obtained based on the difference between the projection vectors of any two target connected components, the difference between the gradient change vectors of any two target connected components, and the distance between the center points of any two target connected components. The specific calculation method is as follows:

[0041]

[0042] In the formula, Indicates the first The target connected component and the first The distance between the center points of each target connected component Indicates the first The target connected component and the first The angle between the projection vectors of the target connected components. Indicates the first The target connected component and the first The angle between the gradient change vectors of the target connected components. This represents an exponential function with the natural constant as its base. Indicates the first The target connected component and the first The possibility of merging between target connected components;

[0043] The probability of merging is greater than the preset third threshold. Merge the two target connected components and denote all the target connected components after the merger as the crack connected components;

[0044] The ratio between the area of ​​all connected regions of cracks and the area of ​​the bulldozer tooth image is denoted as the crack area ratio; when the crack area ratio is less than a preset fourth threshold... If the identified bulldozer teeth are consistent, then the crack area ratio is greater than or equal to the preset fourth threshold. If so, the identified bulldozer teeth are inconsistent.

[0045] The beneficial effects of the technical solution of this invention are as follows: This invention obtains the crack degree of each defective connected region based on the difference between the gray values ​​of pixels in each defective connected region and the gray values ​​of surrounding pixels, and the smoothness of the shape of each defective connected region. This reduces the influence of uneven regions on the detection of crack defects. Based on the difference between the gradient change vector of each reference connected region and the decreasing vector of all edge pixels within each reference connected region, the probability that each reference connected region is a cracked connected region is obtained. This eliminates the influence of some connected regions on the tooth surface related to crack types on the cracked connected region. Based on any two target connected regions... The difference between the projection vector and the gradient change vector, and the distance between the center points of any two target connected regions are used to obtain the merging probability between any two target connected regions. The merging probability between any two target connected regions reduces the interference of noise. All target connected regions are merged according to the merging probability between any two target connected regions to obtain all crack connected regions, thus completing the crack defect detection in the teeth of the bulldozer tooth block. The final consistency recognition result is obtained according to the proportion of the area of ​​all crack connected regions in the tooth image area, which improves the accuracy of crack detection in the teeth of the bulldozer tooth block and also improves the accuracy of the consistency recognition result of the bulldozer tooth block. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the steps of the intelligent identification method for tooth consistency of bulldozer tooth blocks according to the present invention.

[0048] Figure 2 This is a flowchart for identifying the tooth consistency of bulldozer tooth blocks. Detailed Implementation

[0049] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the intelligent recognition method for tooth consistency in bulldozer tooth blocks proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0051] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent identification method for tooth consistency of bulldozer tooth blocks provided by the present invention.

[0052] Please see Figure 1 The diagram illustrates a flowchart of a method for intelligent recognition of tooth consistency in bulldozer tooth blocks according to an embodiment of the present invention. The method includes the following steps:

[0053] Step S001: Acquire images of the teeth of the bulldozer's tooth block.

[0054] It should be noted that in order to improve the durability of the bulldozer teeth and ensure the safety of the bulldozer during operation, crack defect detection is carried out on the teeth of the bulldozer during the production process. Before crack detection, images of the teeth need to be collected.

[0055] Specifically, color images of the bulldozer teeth are acquired using an industrial camera, and grayscale preprocessing is performed on the color images to obtain grayscale images of the teeth. Then, the foreground region of the teeth in the grayscale images is extracted using a semantic segmentation algorithm and recorded as the tooth image.

[0056] Among them, grayscale preprocessing of color images of teeth is a well-known technique, and will not be described in detail here;

[0057] It should be noted that in this embodiment, the DeepLabV3 neural network is used as the neural network model for the semantic segmentation algorithm, and the cross-entropy loss function is used as the loss function of the DeepLabV3 neural network. This neural network is a well-known technology, and the specific results and training methods of this network will not be described in detail in this embodiment.

[0058] At this point, we have obtained the image of the bulldozer's teeth.

[0059] Step S002: Obtain several defect connected components in the tooth image, and obtain the edge chain code sequence of each defect connected component; based on the grayscale distribution of pixels within a preset local window of all pixels in each defect connected component and the differences between different data in each edge chain code sequence, select reference connected components from all defect connected components.

[0060] It should be noted that when detecting cracks in the teeth of bulldozer blocks, the presence of uneven areas can cause edge detection to detect uneven areas in the image of the bulldozer blocks' teeth. Therefore, this embodiment distinguishes between crack areas and uneven areas based on the grayscale and shape differences between them.

[0061] Specifically, the edge detection of the bulldozer tooth block image is performed using the Canny edge detection algorithm, and then the edge detection results of the bulldozer tooth block image are processed by binarization dilation to obtain several defect connected regions.

[0062] Among them, the Canny edge detection algorithm and binarized dilation are well-known techniques, and will not be described in detail here.

[0063] It should be noted that the protruding parts of the uneven area may have higher gray values, while the recessed parts may have lower gray values. Therefore, the gray values ​​of the uneven area differ significantly from the gray values ​​of the pixels in the bulldozer tooth image itself. On the other hand, cracks usually represent breaks or fissures on the surface of an object. Because cracks may cause light scattering or shadows, the gray values ​​of the crack area are lower, as they may reflect or absorb less light. Therefore, the gray values ​​of the crack area differ less from the gray values ​​of the pixels in the bulldozer tooth image itself.

[0064] Specifically, a parameter is preset. In this embodiment, This example is used for illustration; no specific limitations are set in this embodiment. This can be determined based on the specific implementation. Using any pixel in the image of the bulldozer tooth block as the center point of the local window, ... If the size is set to the local window size, then a local window of any pixel in the image of the bulldozer tooth block is obtained.

[0065] Based on the grayscale distribution of pixels within the local window of all pixels in each defect connected region, the probability that each defect connected region belongs to a crack defect is obtained. As an example, the specific calculation method is as follows:

[0066]

[0067] In the formula, Indicates the first The first defective connected component The pixels within the local window of the nth pixel belong to the nth pixel. The average grayscale value of all pixels in a defective connected region. Indicates the first The first defective connected component Pixels within a local window of pixel number 1 do not belong to the 1st pixel. The average grayscale value of all pixels in a defective connected region. Indicates the first The number of all pixels in a defective connected domain. It is the absolute value symbol. This represents an exponential function with the natural constant as its base. Indicates the first The probability that a defective connected domain belongs to a crack defect.

[0068] in, This represents the difference between the gray values ​​of all pixels in each defective connected region and the gray values ​​of the pixels surrounding the defective connected region. The larger the difference, the greater the probability that the defective connected region is a concave-convex region, and the smaller the probability that it is a crack defect; the smaller the difference, the smaller the probability that the defective connected region is a concave-convex region, and the greater the probability that it is a crack defect.

[0069] Thus, we obtain the probability that each defect connected domain belongs to a crack defect.

[0070] It should be noted that cracked areas are usually linear or irregular, and they may have a slender shape or a tortuous outline; while uneven areas are usually protrusions or depressions on the surface of an object, and may form various shapes such as circles, ovals, and squares. The edges of uneven areas are usually relatively smooth and do not show characteristics of breakage or forking; therefore, uneven areas are relatively smooth compared to cracked areas.

[0071] Specifically, the edge chain code sequence of each defective connected domain is obtained through the Freeman chain code algorithm; the Freeman chain code algorithm is a well-known technique and will not be described in detail here.

[0072] The degree to which each defective connected region is a crack defect is obtained based on the differences between adjacent data in the edge chain code sequence of each defective connected region, and the differences between each data in the edge chain code sequence of each defective connected region and the mean of all data in the edge chain code sequence. As an example, the specific calculation method is as follows:

[0073]

[0074] In the formula, Indicates the first The first edge chain code sequence of the defective connected component One data point, Indicates the first The first edge chain code sequence of the defective connected component One data point, Indicates the first The mean of all data in the edge chain code sequence of a defective connected component. Indicates the first The number of all data in the edge chain code sequence of a defective connected component It is the absolute value symbol. Represents a linear normalization function. Indicates the first Each defect connected region represents the degree of the crack defect.

[0075] in, The mean of the differences between all adjacent data in the edge chain code sequence of each defective connected region is represented. The larger the mean of the difference, the less smooth the defective connected region is, and the greater the degree to which the defective connected region is a crack defect. The smaller the mean of the difference, the smoother the defective connected region is, and the less the degree to which the defective connected region is a crack defect, that is, the greater the degree to which the defective connected region is a concave-convex region. This represents the sum of the differences between all data in the edge chain code sequence of each defective connected region and the mean of all data. The larger the sum of the differences, the less smooth the defective connected region is, and the greater the degree to which the defective connected region is a crack defect. The smaller the sum of the differences, the smoother the defective connected region is, and the less the degree to which the defective connected region is a crack defect, that is, the greater the degree to which the defective connected region is a concave-convex region.

[0076] At this point, the degree to which each defect connected region is a crack defect is obtained.

[0077] Based on the probability that each defect connected region belongs to a crack defect and the degree to which each defect connected region is a crack defect, the crack degree of each defect connected region is obtained. As an example, the specific calculation method is as follows:

[0078]

[0079] In the formula, Indicates the first The probability that a defective connected region belongs to a crack defect. Indicates the first Each defect connected region represents the degree of the crack defect. Indicates the first The degree of cracking in a defective connected domain.

[0080] Preset a first threshold In this embodiment, This example is used for illustration; no specific limitations are set in this embodiment. This can be determined based on the specific implementation situation. The crack severity should be greater than a preset first threshold. All defective connected components are denoted as reference connected components.

[0081] At this point, all the reference connected components have been obtained.

[0082] Step S003: Obtain the gradient change vector of each reference connected component and the decreasing vector of each edge pixel in each reference connected component; based on the difference between the gradient change vector of each reference connected component and the decreasing vector of all edge pixels in each reference connected component, select the target connected component from all reference connected components.

[0083] It should be noted that due to the surface structure of the teeth, some connected regions with small gray-scale differences and nearly straight edges, similar to the cracks, will be formed on the tooth surface. Therefore, it is necessary to filter from all the reference connected regions to obtain the accurate crack connected regions.

[0084] It should be further noted that the gradient changes of the edge pixels in the crack region exhibit a decreasing regularity. Therefore, further analysis can be conducted by examining the grayscale changes of the edge pixels in the reference connected region.

[0085] Specifically, obtain the minimum bounding rectangle of each reference connected component; draw a line segment parallel to the short side and of the same length as the short side through the center point of the minimum bounding rectangle, and denote it as the first line segment of the minimum bounding rectangle of each reference connected component; draw a line segment parallel to the long side and of the same length through the center point of the minimum bounding rectangle, and denote it as the second line segment of the minimum bounding rectangle of each reference connected component; divide the minimum bounding rectangle into two target regions based on the first line segment of the minimum bounding rectangle of each reference connected component, calculate the mean gradient magnitude of all pixels in each target region, denote the target region with the largest mean gradient magnitude as the first target region, and denote the target region with the smallest mean gradient magnitude as the second target region.

[0086] Based on the second line segment of the minimum bounding rectangle of each reference connected region, the first target region, and the second target region, the gradient change vector of each reference connected region is obtained. The magnitude of the gradient change vector of each reference connected region is equal to the length of the second line segment of the minimum bounding rectangle of each reference connected region, and the direction of the gradient change vector of each reference connected region points from the first target region to the second target region.

[0087] Obtain the edge pixels at the edges of each reference connected component. Find the pixel with the smallest gradient magnitude in its eight-neighborhood that is less than the edge pixel itself, and denote it as the target pixel of that edge pixel. Use the direction from each edge pixel to its corresponding target pixel as the direction of the decreasing vector for that edge pixel. If the target pixel of each edge pixel belongs to one of its four neighboring pixels, the magnitude of the decreasing vector is 1 unit. If the target pixel belongs to one of its D neighboring pixels, the magnitude of the decreasing vector is... Thus, the decreasing vector of each edge pixel is obtained. When there is no matching target pixel within the eight neighbors of an edge pixel, the decreasing vector of that edge pixel is replaced with a vector of 0.

[0088] Based on the difference between the gradient change vector of each reference connected region and the decreasing vector of all edge pixels within each reference connected region, the probability that each reference connected region is a cracked connected region is obtained. As an example, the specific calculation method is as follows:

[0089]

[0090] In the formula, Indicates the first The first reference connected component A decreasing vector of edge pixels, Indicates the first Gradient change vectors of each reference connected component Indicates the first The first reference connected component The magnitude of the decreasing vector of each edge pixel. Indicates the first The first reference connected component The decreasing vector of the nth edge pixel and the nth edge pixel Cosine similarity between gradient transformation vectors of reference connected components Indicates the first The number of all edge pixels in a reference connected region Represents a linear normalization function. Indicates the first The probability that a reference connected region is a cracked connected region.

[0091] in, Indicates the first The first reference connected component The projection of the magnitude of the decreasing vector of the edge pixels on the th edge pixel is... The length of the gradient change vector in the direction corresponding to each reference connected region is such that the longer the length, the more the gradient change of the edge pixels of the reference connected region conforms to the decreasing trend, and the greater the probability that the reference connected region is a cracked connected region; the shorter the length, the less the gradient change of the edge pixels of the reference connected region conforms to the decreasing trend, and the smaller the probability that the reference connected region is a cracked connected region.

[0092] Thus, we obtain the probability that each reference connected region is a cracked connected region.

[0093] Preset a second threshold In this embodiment, This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0094] The probability that the reference connected component is a fractured connected component is greater than a preset second threshold. All reference connected components are denoted as the target connected component.

[0095] At this point, all target connected components have been obtained.

[0096] Step S004: Obtain the projection vector of each reference connected component based on the decreasing vector of all edge pixels in each reference connected component; merge all target connected components based on the difference between the projection vectors of any two target connected components, the difference between the gradient change vectors of any two target connected components, and the distance between the center points of any two target connected components to obtain the tooth consistency recognition result of the bulldozer tooth block.

[0097] It should be noted that, due to the possibility of noise interference, the cracked connected domains belonging to the same connected domain may be divided into two. Therefore, it is necessary to merge them based on the differences between the two target connected domains.

[0098] Specifically, Recorded as the number The first reference connected component The decreasing projection vector of the i-th edge pixel, for the i-th edge pixel Perform vector operations on the decreasing projection vectors of all edge pixels in the nth reference connected region, and denote the result of the vector operation as the nth... The projection vectors of the reference connected components. Where, in the formula, Indicates the first The first reference connected component A decreasing vector of edge pixels, Indicates the first Gradient change vectors of each reference connected component Indicates the first The first reference connected component The magnitude of the decreasing vector of each edge pixel. Indicates the first The magnitude of the gradient change vector of each reference connected region. Indicates the first The first reference connected component The decreasing vector of the nth edge pixel and the nth edge pixel Cosine similarity between gradient transformation vectors of a reference connected region.

[0099] Thus, the projection vector of each reference connected component is obtained; and the projection vector of each target connected component is also obtained.

[0100] Based on the difference between the projection vectors of any two target connected components, the difference between the gradient change vectors of any two target connected components, and the distance between the center points of any two target connected components, the merging probability between any two target connected components is obtained. As an example, the specific calculation method is as follows:

[0101]

[0102] In the formula, Indicates the first The target connected component and the first The distance between the center points of each target connected component Indicates the first The target connected component and the first The angle between the projection vectors of the target connected components. Indicates the first The target connected component and the first The angle between the gradient change vectors of the target connected components. This represents an exponential function with the natural constant as its base. Indicates the first The target connected component and the first The possibility of merging between target connected components.

[0103] Specifically, the smaller the distance between the center points of two target connected components, the greater the likelihood of them merging; conversely, the greater the distance, the less likely they are to merge. Similarly, the smaller the angle between the projection vectors of two target connected components, the greater the likelihood of them merging; and the greater the angle, the less likely they are to merge. Finally, the smaller the angle between the gradient change vectors of two target connected components, the greater the likelihood of them merging; and the greater the angle, the less likely they are to merge.

[0104] Preset a third threshold In this embodiment, This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0105] The probability of merging is greater than the preset third threshold. The two target connected components are merged, and all the target connected components after the merger are denoted as the crack connected components.

[0106] At this point, all the connected domains with cracks have been obtained.

[0107] Preset a fourth threshold In this embodiment, This example is used for illustration; no specific limitations are set in this embodiment. This can be determined based on the specific implementation. The ratio between the area of ​​all connected regions of cracks and the area of ​​the bulldozer tooth image is recorded as the crack area ratio; when the crack area ratio is less than a preset fourth threshold... If the identified bulldozer teeth are consistent, then the crack area ratio is greater than or equal to the preset fourth threshold. If so, the identified bulldozer teeth are inconsistent.

[0108] At this point, the tooth consistency identification of the bulldozer tooth blocks is complete. The flowchart for the bulldozer tooth consistency identification is as follows: Figure 2 As shown.

[0109] It should be noted that the embodiments used in this example The model is only used to represent negative correlations and the results of the constraint model output are in Within this range, in specific implementations, other models with the same purpose can be substituted; this embodiment is merely an example. The description will be based on a model, without making specific limitations on it. This refers to the input of the model.

[0110] This concludes the embodiment.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent recognition of tooth consistency in bulldozer tooth blocks, characterized in that, The method includes the following steps: Obtain images of the teeth of the bulldozer's tooth block; Obtain several defect connected components in the tooth image, and obtain the edge chain code sequence for each defect connected component; based on the grayscale distribution of pixels within a preset local window of all pixels in each defect connected component and the differences between different data in each edge chain code sequence, select reference connected components from all defect connected components; Obtain the gradient change vector of each reference connected component, and obtain the decreasing vector of each edge pixel in each reference connected component; based on the difference between the gradient change vector of each reference connected component and the decreasing vector of all edge pixels in each reference connected component, select the target connected component from all reference connected components. The projection vector of each reference connected component is obtained by using the decreasing vector of all edge pixels in each reference connected component; all target connected components are merged based on the difference between the projection vectors of any two target connected components, the difference between the gradient change vectors of any two target connected components, and the distance between the center points of any two target connected components to obtain the tooth consistency recognition result of the bulldozer tooth block. The specific steps for selecting reference connected components from all defective connected components based on the grayscale distribution of pixels within a preset local window of all pixels in each defective connected component and the differences between different data in each edge chain code sequence are as follows: Based on the grayscale distribution of pixels within the local window of all pixels in each defect connected region, the probability that each defect connected region belongs to a crack defect can be obtained. The degree to which each defective connected region is a crack defect is obtained by considering the differences between adjacent data in the edge chain code sequence of each defective connected region and the differences between each data in the edge chain code sequence and the mean of all data in the edge chain code sequence. Based on the probability that each defect connected region belongs to a crack defect and the degree to which each defect connected region is a crack defect, the crack degree of each defect connected region is obtained. Among them, the probability that each defect connected region belongs to a crack defect is positively correlated with the crack degree of each defect connected region, and the degree to which each defect connected region is a crack defect is positively correlated with the crack degree of each defect connected region. If the degree of crack is greater than a preset first threshold All defective connected components are denoted as reference connected components; The specific steps for obtaining the decreasing vector of each edge pixel in each reference connected component are as follows: Obtain the edge pixels in each reference connected component. Find the pixel with the smallest gradient magnitude in its eight-neighborhood that is less than the edge pixel itself, and denote it as the target pixel of that edge pixel. Use the direction from each edge pixel to its corresponding target pixel as the direction of its decreasing vector. If the target pixel of each edge pixel belongs to one of its four neighboring pixels, the magnitude of the decreasing vector is 1 unit. If the target pixel belongs to one of its D neighboring pixels, the magnitude of the decreasing vector is... ; If there is no matching target pixel in the eight neighborhoods of each edge pixel, then the decreasing vector of each edge pixel is replaced with a 0 vector.

2. The intelligent recognition method for tooth consistency of bulldozer tooth blocks according to claim 1, characterized in that, The specific steps for obtaining several defect connected regions in the tooth image and obtaining the edge chain code sequence of each defect connected region are as follows: The edge detection of the bulldozer tooth block image is performed using the Canny edge detection algorithm. Then, the edge detection results of the bulldozer tooth block image are processed by binarization dilation to obtain several defect connected components. The edge chain code sequence of each defective connected component is obtained using the Freeman chain code algorithm.

3. The intelligent recognition method for tooth consistency of bulldozer tooth blocks according to claim 1, characterized in that, The method for determining the probability that each defect connected region belongs to a crack defect based on the grayscale distribution of pixels within a local window of all pixels in each defect connected region includes the following specific calculation method: In the formula, Indicates the first The first defective connected component The pixels within the local window of the nth pixel belong to the nth pixel. The average grayscale value of all pixels in a defective connected region. Indicates the first The first defective connected component Pixels within a local window of pixel number 1 do not belong to the 1st pixel. The average grayscale value of all pixels in a defective connected region. Indicates the first The number of all pixels in a defective connected domain. It is the absolute value symbol. This represents an exponential function with the natural constant as its base. Indicates the first The probability that a defective connected domain belongs to a crack defect.

4. The intelligent recognition method for tooth consistency of bulldozer tooth blocks according to claim 1, characterized in that, The degree to which each defective connected region is a crack defect is obtained based on the differences between adjacent data in the edge chain code sequence of each defective connected region, and the differences between each data in the edge chain code sequence of each defective connected region and the mean of all data in the edge chain code sequence. The specific calculation method is as follows: In the formula, Indicates the first The first edge chain code sequence of the defective connected component One data point, Indicates the first The first edge chain code sequence of the defective connected component One data point, Indicates the first The mean of all data in the edge chain code sequence of a defective connected component. Indicates the first The number of all data in the edge chain code sequence of a defective connected component It is the absolute value symbol. Represents a linear normalization function. Indicates the first Each defect connected region represents the degree of the crack defect.

5. The intelligent recognition method for tooth consistency of bulldozer tooth blocks according to claim 1, characterized in that, The specific steps for obtaining the gradient change vector of each reference connected component are as follows: Obtain the minimum bounding rectangle of each reference connected component; draw a line segment through the center point of the minimum bounding rectangle that is parallel to the short side and has the same length as the short side, and denote it as the first line segment of the minimum bounding rectangle of each reference connected component; draw a line segment through the center point of the minimum bounding rectangle that is parallel to the long side and has the same length as the long side, and denote it as the second line segment of the minimum bounding rectangle of each reference connected component. Divide the minimum bounding rectangle of each reference connected component into two target regions based on the first line segment of the minimum bounding rectangle. Calculate the mean gradient magnitude of all pixels in each target region. The target region with the largest mean gradient magnitude is designated as the first target region, and the target region with the smallest mean gradient magnitude is designated as the second target region. Based on the second line segment of the minimum bounding rectangle of each reference connected region, the first target region, and the second target region, the gradient change vector of each reference connected region is obtained. The magnitude of the gradient change vector of each reference connected region is equal to the length of the second line segment of the minimum bounding rectangle of each reference connected region, and the direction of the gradient change vector of each reference connected region points from the first target region to the second target region.

6. The intelligent recognition method for tooth consistency of bulldozer tooth blocks according to claim 1, characterized in that, The specific calculation method for selecting the target connected component from all reference connected components based on the difference between the gradient change vector of each reference connected component and the decreasing vector of all edge pixels within each reference connected component is as follows: In the formula, Indicates the first The first reference connected component A decreasing vector of edge pixels, Indicates the first Gradient change vectors of each reference connected component Indicates the first The first reference connected component The magnitude of the decreasing vector of each edge pixel. Indicates the first The first reference connected component The decreasing vector of the edge pixel and the first Cosine similarity between gradient transformation vectors of reference connected components Indicates the first The number of all edge pixels in a reference connected region Represents a linear normalization function. Indicates the first The probability that a reference connected region is a cracked connected region; The probability that the reference connected component is a fractured connected component is greater than a preset second threshold. All reference connected components are denoted as the target connected component.

7. The intelligent recognition method for tooth consistency of bulldozer tooth blocks according to claim 6, characterized in that, The specific steps for obtaining the projection vector of each reference connected region based on the decreasing vector of all edge pixels in each reference connected region are as follows: Will Recorded as the number The first reference connected component The decreasing projection vector of the i-th edge pixel, for the i-th edge pixel Perform vector operations on the decreasing projection vectors of all edge pixels in the nth reference connected region, and denote the result of the vector operation as the nth... Projection vectors of each reference connected component; In the formula, Indicates the first The magnitude of the gradient change vector of a reference connected region.

8. The intelligent recognition method for tooth consistency of bulldozer tooth blocks according to claim 1, characterized in that, The process of merging all target connected components based on the differences between the projection vectors of any two target connected components, the differences between the gradient change vectors of any two target connected components, and the distance between the center points of any two target connected components to obtain the tooth consistency recognition result of the bulldozer tooth block includes the following specific steps: The probability of merging any two target connected components is obtained based on the difference between the projection vectors of any two target connected components, the difference between the gradient change vectors of any two target connected components, and the distance between the center points of any two target connected components. The specific calculation method is as follows: In the formula, Indicates the first The target connected component and the first The distance between the center points of each target connected component Indicates the first The target connected component and the first The angle between the projection vectors of the target connected components. Indicates the first The target connected component and the first The angle between the gradient change vectors of the target connected components. This represents an exponential function with the natural constant as its base. Indicates the first The target connected component and the first The possibility of merging between target connected components; The probability of merging is greater than the preset third threshold. Merge the two target connected components and denote all the target connected components after the merger as the crack connected components; The ratio between the area of ​​all connected regions of cracks and the area of ​​the bulldozer tooth image is denoted as the crack area ratio; when the crack area ratio is less than a preset fourth threshold... If the identified bulldozer teeth are consistent, then the crack area ratio is greater than or equal to the preset fourth threshold. If so, the identified bulldozer teeth are inconsistent.

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