Intelligent quality evaluation method for waterproof cloth

Abstract

The invention relates to the technical field of image processing, and provides a waterproof cloth quality intelligent evaluation method, which comprises the steps of collecting a waterproof cloth coating image of a waterproof cloth coating surface to be evaluated; marking a target pixel point, determining a local direction change rate and a local gray scale difference of the target pixel point, and calculating a crack feature value of the waterproof cloth coating image; determining the LBP distribution entropy of each edge and the closing degree of the edge in the waterproof cloth coating image, and determining the pore feature value of the waterproof cloth coating image; identifying a bubbling center point and a bubbling defect area, and determining a bubbling characteristic value of the waterproof cloth coating image; and obtaining a waterproof cloth quality evaluation result according to the crack characteristic value, the small hole characteristic value and the bubbling characteristic value of the waterproof cloth coating image. The method aims at improving the accuracy of the evaluation result of the tiny flaws of the waterproof cloth.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, in particular to a tarpaulin quality intelligent evaluation method. BACKGROUND

[0002] A tarpaulin is a kind of solid and durable fabric made of waterproof material and coated with a waterproof coating on one side of the surface. In order to guarantee the waterproof effect and durability of the waterproof project using the tarpaulin, the quality of the tarpaulin needs to be evaluated. Generally, the tarpaulin coating is prone to crack defects, pinhole defects and bubble defects. The prior art usually extracts the texture features in the image of the tarpaulin coating surface through texture extraction algorithms such as gray level co-occurrence matrix, and identifies the crack defects, pinhole defects and bubble defects of the tarpaulin coating according to the extracted texture features.

[0003] However, the crack defects, pinhole defects and bubble defects of the tarpaulin coating are all tiny defects, which are easy to be submerged in the background texture during evaluation, thereby causing missed detection and false detection of the tiny defects and affecting the accuracy of the tarpaulin quality evaluation result. SUMMARY

[0004] The present application provides a tarpaulin quality intelligent evaluation method to solve the problem that tiny defects are easy to be submerged in the background texture, affecting the accuracy of the tarpaulin quality evaluation result. The technical solution adopted is as follows: One embodiment of the present application provides a tarpaulin quality intelligent evaluation method, which comprises the following steps: Collecting a tarpaulin coating image of a tarpaulin coating surface to be evaluated; Regarding any one pixel point in the tarpaulin coating image as a target pixel point, establishing a local analysis window of the target pixel point, determining a local direction change rate and a local gray level difference of the target pixel point according to the gradient difference of all edge pixel points and the pixel value difference of all pixel points in the local analysis window of the target pixel point, and calculating a crack feature value of the tarpaulin coating image according to the local direction change rate and the local gray level difference of all pixel points in the tarpaulin coating image; Determining an LBP distribution entropy of the same edge according to the difference of the LBP values of all edge pixel points of the same edge in the tarpaulin coating image, determining the closure degree of the edge in the tarpaulin coating image according to the difference in the number of pixel points contained in the divided region and the circumscribed circle of the divided region, and determining a pinhole feature value of the tarpaulin coating image according to the closure degree and the LBP distribution entropy of all edges in the tarpaulin coating image; According to the gray value of the pixel point in the tarpaulin coating image and the position of the edge in the tarpaulin coating image, the bubble center point and the bubble defect area of the bubble center point are identified, and according to the gray value difference of the pixel point in the bubble defect area of each bubble center point in the tarpaulin coating image and the bubble center point, the bubble feature value of the tarpaulin coating image is determined. According to the crack feature value, the hole feature value and the bubble feature value of the tarpaulin coating image, the tarpaulin quality evaluation result is obtained.

[0005] Further, the determination method of the local direction change rate of the target pixel point is: The tarpaulin coating image is grayed and edge detected to identify the edge and the edge pixel point. Any pixel point in the tarpaulin coating image is recorded as a target pixel point, a local analysis window with a first preset length is established with the target pixel point as the center, and the standard deviation of the gradient value of all edge pixel points in the local analysis window is recorded as the local direction change rate of the target pixel point.

[0006] Further, the acquisition method of the local gray difference is: The range of the gray value of all pixel points in the local analysis window is recorded as the local gray difference of the target pixel point.

[0007] Further, the calculation method of the crack feature value of the tarpaulin coating image is: The mean value of the local gray difference of all pixel points in the tarpaulin coating image is recorded as the gray difference of the tarpaulin coating image. The mean value of the local direction change rate of all pixel points in the tarpaulin coating image is recorded as the direction change rate of the tarpaulin coating image. The positive correlation processing result of the gray difference and the direction change rate of the tarpaulin coating image is recorded as the crack feature value of the tarpaulin coating image.

[0008] Further, the determination method of the closure degree of the edge in the tarpaulin coating image is: The number of pixel points contained in the region divided by the edge in the tarpaulin coating image is recorded as the area of the region divided by the edge, the number of pixel points contained in the minimum circumscribed circle of the region divided by the edge is recorded as the minimum circumscribed circle area of the region divided by the edge, the difference value between the minimum circumscribed circle area of the region divided by the edge and the area of the region divided by the edge is recorded as the first difference value of the edge, the ratio of the first difference value of the edge to the area of the region divided by the edge is recorded as the first ratio of the edge, and the negative correlation processing result of the first ratio of the edge is recorded as the closure degree of the edge.

[0009] Further, the calculation method of the hole feature value of the tarpaulin coating image is: The normalized value of the mean value of the LBP distribution entropy of all edges in the tarpaulin coating image is recorded as the edge direction uniformity of the tarpaulin coating image; The mean value of the closure degree of all edges in the tarpaulin coating image is recorded as the edge comprehensive closure degree of the tarpaulin coating image. The mean value of the edge direction uniformity and the edge comprehensive closure degree of the tarpaulin coating image is recorded as the small hole characteristic value of the tarpaulin coating image.

[0010] Further, the identification method of the bubble center point is: All local maximum points in the tarpaulin coating image are recorded as candidate bubble center points. Radial gray scale verification is performed on the candidate bubble center points: a verification window with a second preset length as the radius is established with the candidate bubble center point as the center, and the gray scale values in the verification window are verified whether they show a trend of decreasing from the center to the periphery; the candidate bubble center points that do not meet the trend are removed, and the remaining candidate bubble center points are recorded as bubble center points.

[0011] The identification method of the bubble defect area of the bubble center point is: A circular ring is established with the bubble center point as the center, with an inner radius r1 and an outer radius r2, and the area corresponding to the circular ring is recorded as the bubble defect area of the bubble center point, wherein 0 < r1 < r2 <= R; Wherein, , Wherein And is a preset proportion coefficient, satisfying .

[0012] The determination method of the bubble characteristic value of the tarpaulin coating image is: Any pixel point on the outer radius of the bubble defect area of the bubble center point is recorded as a target outer radius point, the part of the line segment with the bubble center point and the target outer radius point as two end points in the bubble defect area of the bubble center point is recorded as the circular ring radius of the bubble center point and the target outer radius point, and the mean value of the gray scale values of all pixel points on the circular ring radius of the bubble center point and the target outer radius point is recorded as the first gray scale mean value of the bubble center point and the target outer radius point. The part of the straight line passing through the blister center point and the target outer radius point in the blister defect area of the blister center point is selected, the annular radius of the blister center point and the target outer radius point is removed, the remaining part is recorded as the opposite annular radius of the blister center point and the target outer radius point, the average value of the gray values of all pixel points on the opposite annular radius of the blister center point and the target outer radius point is recorded as the second gray average value of the blister center point and the target outer radius point; the absolute value of the difference between the first gray average value and the second gray average value of the blister center point and the target outer radius point is recorded as the brightness symmetry value of the blister center point and the target outer radius point. The average value of the brightness symmetry characteristic values of all blister center points in the tarpaulin coating image is recorded as the blister feature value of the tarpaulin coating image.

[0013] Further, the tarpaulin quality evaluation result is obtained according to the crack feature value, the hole feature value and the blister feature value of the tarpaulin coating image, and the specific method comprises: The crack feature value, the hole feature value and the blister feature value of the tarpaulin coating image are arranged in sequence as a column vector to obtain a defect feature vector of the tarpaulin coating image. The defect feature vector of the tarpaulin coating image of the tarpaulin coating surface to be evaluated is input into the decision tree model to obtain the identification results of the crack defect, the hole defect and the blister defect. When the identification results of the crack defect, the hole defect and the blister defect are all no defects, it is determined that the tarpaulin has no defects; otherwise, it is determined that the tarpaulin has defects.

[0014] The beneficial effects of the present application are: The present application considers that the crack defect, the hole defect and the blister defect of the tarpaulin coating are all small defects. Firstly, the features of the crack defect are extracted according to the local contrast around the pixel points to obtain the crack feature value of the tarpaulin coating image. Secondly, the features of the hole defect are extracted according to the closeness and uniformity of the direction of the edges in the tarpaulin coating image to obtain the hole feature value of the tarpaulin coating image. Then, the features of the blister defect are extracted according to the radial brightness change and symmetry in the tarpaulin coating image to obtain the blister feature value of the tarpaulin coating image. Finally, the tarpaulin quality evaluation result is obtained according to the crack feature value, the hole feature value and the blister feature value of the tarpaulin coating image, which solves the problem that small defects are easily submerged in the background texture and affect the accuracy of the tarpaulin quality evaluation result, and improves the accuracy of identification of the three small defects of the crack defect, the hole defect and the blister defect. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0016] Figure 1 A waterproof cloth quality intelligent evaluation method flow chart provided by an embodiment of the present application; Figure 2 A crack feature value acquisition flow chart provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0018] Please refer to Figure 1 which shows a waterproof cloth quality intelligent evaluation method flow chart provided by an embodiment of the present application. The method comprises the following steps: Step S001, collecting a waterproof cloth coating image of a waterproof cloth coating surface to be evaluated.

[0019] An industrial camera is used to collect a waterproof cloth coating image of the waterproof cloth coating surface to be evaluated, and the waterproof cloth coating image is subjected to denoising processing and image enhancement processing to reduce the influence of noise on subsequent crack defect, small hole defect and bubble defect identification.

[0020] In this embodiment, a median filter algorithm is used to denoise the waterproof cloth coating image, and a CLAHE histogram equalization algorithm is used to enhance the waterproof cloth coating image. The median filter algorithm for denoising and the histogram equalization algorithm for image enhancement are both well-known technologies and will not be described in detail. As other implementation manners, other methods such as Gaussian filter and bilateral filter can be used for image denoising on the basis of achieving the purpose of image denoising, and the present application does not make special limitation.

[0021] At this point, the waterproof cloth coating image is obtained.

[0022] Step S002, record any one pixel point in the tarpaulin coating image as a target pixel point, establish a local analysis window of the target pixel point, determine the local direction change rate and the local gray difference of the target pixel point according to the gradient difference of all edge pixel points and the pixel value difference of all pixel points in the local analysis window of the target pixel point, and calculate the crack feature value of the tarpaulin coating image according to the local direction change rate and the local gray difference of all pixel points in the tarpaulin coating image.

[0023] The crack defect, the pinhole defect and the bubble defect of the tarpaulin coating are all micro defects, and the characteristics of these defects are different. The crack defect refers to the fracture defect generated on the surface of the tarpaulin coating, which is represented as a small connected domain in an irregular direction in the image; the pinhole defect is represented as a regular shape in the image, and the gradient direction distribution of the edge pixel point corresponding to the pinhole defect is concentrated; the bubble defect refers to the convex on the tarpaulin coating, which is represented as a spherical reflection with a bright center and a dark edge in the image. Therefore, the characteristics of the crack defect, the pinhole defect and the bubble defect are extracted respectively, and the tarpaulin quality evaluation result is obtained according to the feature fusion result.

[0024] Firstly, the characteristics of the crack defect are extracted according to the local contrast around the pixel point.

[0025] The tarpaulin coating image is grayed to obtain a tarpaulin coating gray image, and the tarpaulin coating gray image is edge detected to obtain a tarpaulin coating edge image, through which the edges and edge pixel points in the tarpaulin coating image can be identified. In this embodiment, the Canny edge detection algorithm is used for edge detection, and the use of the Canny edge detection algorithm for edge detection is a known technology and will not be described in detail.

[0026] Any one pixel point in the tarpaulin coating image is recorded as a target pixel point, and a local window with a side length of 2L is established with the target pixel point as the center, to obtain a local analysis window of the target pixel point, and the gradient values of all edge pixel points in the local analysis window of the target pixel point are calculated. The standard deviation of the gradient values of all edge pixel points in the local analysis window of the target pixel point is recorded as the local direction change rate of the target pixel point.

[0027] Wherein, L represents the first preset length, and the value of the first preset length in this embodiment is 15.

[0028] The local direction change rate of each pixel point in the tarpaulin coating image can be obtained in the same way.

[0029] ​​It should be noted that when edge pixels and edges are not identified in the waterproof fabric coating image, the local direction change rate of the target pixel is directly assigned to 0.

[0030] The local grayscale difference of the target pixel is determined based on the pixel value differences of all pixels within the local analysis window of the target pixel.

[0031] The range of gray values ​​of all pixels within the local analysis window of the target pixel is denoted as the local gray-level difference of the target pixel.

[0032] The same method can be used to obtain the local grayscale difference of each pixel in the waterproof fabric coating image.

[0033] Crack feature values ​​of the waterproof fabric coating image are calculated based on the local directional change rate and local grayscale difference of all pixels in the image.

[0034] The mean of the local grayscale differences of all pixels in the waterproof fabric coating image is denoted as the grayscale difference of the waterproof fabric coating image; the mean of the local directional change rate of all pixels in the waterproof fabric coating image is denoted as the directional change rate of the waterproof fabric coating image; the positive correlation result between the grayscale difference and the directional change rate of the waterproof fabric coating image is denoted as the crack feature value of the waterproof fabric coating image.

[0035] The flowchart for obtaining crack feature values ​​is as follows: Figure 2 As shown.

[0036] It is understood that a positive correlation is applied to the grayscale difference and the rate of change of direction, ensuring that the grayscale difference and the rate of change of direction are positively correlated with the crack characteristic value. It is understood that the positive correlation in this application refers to the relationship between the independent and dependent variables, where the independent variables are the grayscale difference and the rate of change of direction, and the dependent variable is the crack characteristic value. The positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and can be an additive or multiplicative relationship.

[0037] Preferably, as an embodiment of this application, the normalized value of the product of the grayscale difference and the rate of change of direction of the waterproof fabric coating image is recorded as the crack feature value of the waterproof fabric coating image.

[0038] In this embodiment, the sigmoid function is used to calculate the normalized value. The sigmoid function is a well-known technique and will not be described in detail here. As other implementation methods, implementers can use other methods of the prior art, such as the tanh arctangent function.

[0039] In practical applications, as another implementation method, the average of the normalized values ​​of the grayscale difference and the normalized values ​​of the direction change rate of the waterproof fabric coating image is recorded as the crack feature value of the waterproof fabric coating image.

[0040] The larger the crack feature value of the waterproof fabric coating image, the greater the possibility of crack defects appearing in the waterproof fabric coating image.

[0041] At this point, the crack feature values ​​of the waterproof fabric coating image are obtained.

[0042] Step S003: Based on the difference in LBP values ​​of all edge pixels along the same edge in the waterproof fabric coating image, determine the LBP distribution entropy of the same edge; based on the difference in the number of pixels contained in the region divided by the edge and the circumcircle of the divided region in the waterproof fabric coating image, determine the degree of closure of the edge in the waterproof fabric coating image; based on the degree of closure of all edges and the LBP distribution entropy in the waterproof fabric coating image, determine the pinhole feature value of the waterproof fabric coating image.

[0043] Secondly, considering that the pinhole defects are regular-shaped holes, the features of the pinhole defects are extracted based on the closure degree and direction uniformity of the edges in the waterproof fabric coating image.

[0044] The edge direction uniformity of the waterproof fabric coating image is determined by the difference in LBP values ​​of all edge pixels along the same edge in the waterproof fabric coating image.

[0045] Calculate the LBP value of each edge pixel in the waterproof fabric coating image. Based on the LBP values ​​of all edge pixels along the same edge, generate an LBP histogram for that edge. Calculate the LBP distribution entropy of that edge based on the LBP histogram. The normalized value of the mean LBP distribution entropy of all edges in the waterproof fabric coating image is denoted as the edge direction uniformity of the waterproof fabric coating image.

[0046] Calculating the LBP value of a pixel, generating an LBP histogram, and calculating the LBP distribution entropy are all well-known techniques and will not be elaborated further.

[0047] The degree of edge closure in the waterproof fabric coating image is determined by the difference in the number of pixels contained in the region defined by the edge and the circumcircle of the defined region.

[0048] The number of pixels contained in the region defined by the edge in the waterproof fabric coating image is denoted as the area of ​​the region defined by the edge. The number of pixels contained within the smallest circumcircle of the region defined by the edge is denoted as the area of ​​the smallest circumcircle of the region defined by the edge. The difference between the area of ​​the smallest circumcircle of the region defined by the edge and the area of ​​the region defined by the edge is denoted as the first difference of the edge. The ratio of the first difference of the edge to the area of ​​the region defined by the edge is denoted as the first ratio of the edge. The negative correlation result of the first ratio of the edge is denoted as the degree of edge closure.

[0049] It is understood that the first ratio of the edges is negatively correlated, meaning that the first ratio of the edges is negatively correlated with the degree of closure of the edges. It is understood that the negative correlation in this application refers to the relationship between the independent variable and the dependent variable, where the independent variable is the first ratio of the edges and the dependent variable is the degree of closure of the edges. The negative correlation means that the dependent variable decreases (increases) as the independent variable increases (decreases), and can be an inverse relationship, a subtraction relationship, etc.

[0050] Preferably, as an embodiment of this application, the negative of the first ratio of the edges is taken as the exponent of an exponential function with the natural constant as the base, and the calculated value of the exponential function is recorded as the degree of closure of the edges.

[0051] Some other embodiments of this application may use the difference between the normalized value of the number 1 and the first ratio of the edge as the degree of edge closure.

[0052] It should be noted that when the edges of the waterproof fabric coating image cannot be divided into closed regions, the number of pixels contained in the regions divided by the edges in the waterproof fabric coating image cannot be calculated, and the degree of closure of the edges is directly assigned to 0.

[0053] The pinhole feature values ​​of the waterproof fabric coating image are determined based on the degree of closure of all edges in the image and the uniformity of edge direction.

[0054] The average degree of closure of all edges in the waterproof fabric coating image is denoted as the overall edge closure degree of the waterproof fabric coating image. The average value of the overall edge closure degree and the edge direction uniformity of the waterproof fabric coating image is denoted as the pinhole feature value of the waterproof fabric coating image.

[0055] The larger the pore feature value of the waterproof fabric coating image, the greater the possibility of pore defects appearing in the waterproof fabric coating image.

[0056] It should be noted that when edge pixels and edges are not identified in the waterproof fabric coating image, the feature value of the small hole in the waterproof fabric coating image is directly assigned to 0.

[0057] At this point, the pore feature values ​​of the waterproof fabric coating image are obtained.

[0058] Step S004: Based on the grayscale values ​​of pixels in the waterproof fabric coating image and the position of the edges in the waterproof fabric coating image, identify the bubble center point and the bubble defect area of ​​the bubble center point. Based on the difference in grayscale values ​​of pixels within the bubble defect area of ​​each bubble center point in the waterproof fabric coating image that are centrally symmetrical with respect to the bubble center point, determine the bubble feature value of the waterproof fabric coating image.

[0059] Then, considering the spherical-like reflection of the bubbling defect with a bright center and a dark edge, the characteristics of the bubbling defect are extracted based on the radial brightness change and symmetry in the waterproof cloth coating image.

[0060] Process the waterproof cloth coating image using the LoG operator to identify the local maximum points in the waterproof cloth coating image, and mark the local maximum points as candidate bubbling center points; and perform radial gray-scale verification on the candidate bubbling center points: taking this candidate point as the center, establish a verification window with a radius of the second preset length, and verify whether the gray-scale values within the verification window show a decreasing trend from the center to the periphery; eliminate the candidate bubbling center points that do not meet the trend, and mark the remaining candidate bubbling center points as bubbling center points.

[0061] Among them, the verification process specifically may include: taking this candidate bubbling center point as the center, within the verification window, extract the gray-scale value sequences from the center point outward along multiple preset radial directions (for example, along 8 directions); then, perform trend analysis on the gray-scale value sequences in each direction to determine whether their gray-scale values decrease steadily as the distance from the center point increases (for example, by analyzing their correlation coefficients or first-order differences); finally, count the number of radial directions that meet the decreasing trend. When this number is greater than a preset proportion threshold (for example, more than 75% of the directions meet), it is determined that this candidate point is a real bubbling center point and is retained, otherwise it is eliminated.

[0062] Taking the bubbling center point as the center, radially search for the local minimum points of the gray-scale values (i.e., the dark edge of the bubble) around; record the average Euclidean distance between the bubbling center point and the local minimum points as the bubble radius R of this bubbling center point. [[ID=X]] [[ID=X]]

[0063] Taking this bubbling center point as the center, establish a circular ring with an inner radius r1 and an outer radius r2 (where 0 < r1 < r2 <= R), and record the area corresponding to this circular ring as the bubbling defect area of the bubbling center point.

[0064] Among them, the inner radius r1 and the outer radius r2 are adaptively determined according to the bubble radius R, aiming to ensure that the analysis area (circular ring) can effectively capture the spherical-like reflection characteristics of the bubble while avoiding interference areas. Specifically, the inner radius r1 aims to avoid the specular reflection highlight area that may exist near the bubbling center point, and the outer radius r2 aims to ensure that the analysis area is located inside the bubble radius R (i.e., the dark edge of the gray-scale local minimum).

[0065] Preferably, the inner radius r1 and the outer radius r2 can be set as proportional values of the bubble radius R, that is , , where and are preset proportional coefficients, satisfying In this embodiment, it can be set The value range is [0.2, 0.4]. The value range is [0.7, 0.9].

[0066] Among them, the LoG operator is the Laplacian of Gaussian operator. The use of the LoG operator to identify local maxima in an image is a well-known technique and will not be elaborated further.

[0067] It is understandable that the center point of the bubble corresponds to the brightest point in the center of the bubble defect, and the bubble defect area (ring) at the center point corresponds to the main part of the spherical reflection at the location of the bubble defect.

[0068] Let any pixel on the outer radius of the bubble defect area at the bubble center point be the target outer radius point. Let the portion of the line segment with the bubble center point and the target outer radius point as its two endpoints within the bubble defect area at the bubble center point be the radius of the annulus between the bubble center point and the target outer radius point. Let the average gray value of all pixels on the annulus radius between the bubble center point and the target outer radius point be the first gray value of the bubble center point and the target outer radius point. Select the portion of the straight line passing through both the bubble center point and the target outer radius point within the bubble defect area at the bubble center point, remove the portion containing the annulus radius between the bubble center point and the target outer radius point, and let the remaining portion be the radius of the annulus opposite the bubble center point and the target outer radius point. Let the average gray value of all pixels on the annulus radius opposite the bubble center point and the target outer radius point be the second gray value of the bubble center point and the target outer radius point. The absolute value of the difference between the first grayscale mean and the second grayscale mean between the bubble center point and the target outer radius point is recorded as the brightness symmetry value between the bubble center point and the target outer radius point.

[0069] Using the same method, we can obtain the brightness symmetry value of any pixel on the outer radius of the bubble defect area at the bubble center point. In other words, every pixel on the outer radius of the bubble defect area at the bubble center point has a corresponding brightness symmetry value. The normalized value of the mean of the brightness symmetry values ​​of all pixels on the outer radius of the bubble defect area at the bubble center point is denoted as the first mean of the bubble center point. The difference between the number 1 and the first mean of the bubble center point is denoted as the brightness symmetry feature value of the bubble center point.

[0070] The mean value of the brightness symmetry feature value of all bubble center points in the waterproof fabric coating image is denoted as the bubble feature value of the waterproof fabric coating image.

[0071] The larger the bubbling feature value of the waterproof fabric coating image, the greater the possibility of bubbling defects appearing in the waterproof fabric coating image.

[0072] It should be noted that if the center point of the bubble is not identified in the image of the waterproof fabric coating, the bubble feature value of the waterproof fabric coating image should be directly assigned to 0.

[0073] At this point, the bubbling feature values ​​of the waterproof fabric coating image are obtained.

[0074] Step S005: Obtain the quality assessment results of the waterproof fabric based on the crack feature values, pinhole feature values, and blister feature values ​​of the waterproof fabric coating image.

[0075] The crack feature values, pinhole feature values, and blister feature values ​​of the waterproof fabric coating image are arranged sequentially into a column vector to obtain the defect feature vector of the waterproof fabric coating image.

[0076] The waterproof fabric coating surface was manually inspected for cracks, pinholes, and bubbles. One thousand waterproof fabric coating surfaces were obtained, each containing one, two, or all three defects, and one surface without any defects. Using the same method, images of the waterproof fabric coating and defect feature vectors from these 1000 surfaces were obtained. These feature vectors were then used to create a dataset, which was divided into training, validation, and test sets in a 6:2:2 ratio. A decision tree model was constructed based on these sets. The defect feature vectors were input into the decision tree model to obtain the identification results for cracks, pinholes, and bubbles. Specifically, the identification results for cracks, pinholes, and bubbles were either "defect present" or "no defect present."

[0077] The defect feature vector of the waterproof fabric coating image to be evaluated is input into the decision tree model to obtain the identification results of crack defects, pinhole defects, and blister defects. When the identification results of crack defects, pinhole defects, and blister defects are all "no defects", the waterproof fabric is determined to be defect-free; when the identification results of crack defects, pinhole defects, and blister defects are all "defects present", the waterproof fabric is determined to be defective.

[0078] At this point, the quality assessment results for the waterproof fabric have been obtained.

[0079] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for intelligently evaluating the quality of waterproof fabric, characterized in that, The method includes the following steps: Acquire images of the waterproof fabric coating on the surface to be evaluated; Denote any pixel in the waterproof fabric coating image as the target pixel, establish a local analysis window for the target pixel, and determine the local direction change rate and local grayscale difference of the target pixel based on the gradient difference of all edge pixels and the pixel value difference of all pixels in the local analysis window of the target pixel. Calculate the crack feature value of the waterproof fabric coating image based on the local direction change rate and local grayscale difference of all pixels in the waterproof fabric coating image. Based on the difference in LBP values ​​of all edge pixels along the same edge in the waterproof fabric coating image, the LBP distribution entropy of the same edge is determined. Based on the difference in the number of pixels contained in the region divided by the edge and the outer circle of the divided region in the waterproof fabric coating image, the degree of closure of the edge in the waterproof fabric coating image is determined. Based on the degree of closure of all edges in the waterproof fabric coating image and the LBP distribution entropy, the pinhole feature value of the waterproof fabric coating image is determined. Based on the gray values ​​of pixels in the waterproof fabric coating image and the position of the edges in the waterproof fabric coating image, identify the bubble center point and the bubble defect area of ​​the bubble center point. Based on the difference in gray values ​​of pixels in the bubble defect area of ​​each bubble center point in the waterproof fabric coating image that are centrally symmetrical with respect to the bubble center point, determine the bubble feature value of the waterproof fabric coating image. The quality assessment results of the waterproof fabric are obtained based on the crack feature values, pinhole feature values, and blister feature values ​​of the waterproof fabric coating image.

2. The intelligent quality assessment method for waterproof fabric according to claim 1, characterized in that, The method for determining the local directional change rate of the target pixel is as follows: The image of the waterproof fabric coating is converted to grayscale and edge detected to identify edges and edge pixels; Record any pixel in the waterproof fabric coating image as the target pixel. Establish a local analysis window with a side length of the first preset length centered on the target pixel. Record the standard deviation of the gradient values ​​of all edge pixels within the local analysis window as the local direction change rate of the target pixel.

3. The intelligent quality assessment method for waterproof fabric according to claim 2, characterized in that, The method for obtaining the local grayscale difference is as follows: The range of gray values ​​of all pixels within the local analysis window is recorded as the local gray-level difference of the target pixel.

4. The intelligent quality assessment method for waterproof fabric according to claim 1, characterized in that, The method for calculating the crack feature value of the waterproof fabric coating image is as follows: The average local grayscale difference of all pixels in the waterproof fabric coating image is denoted as the grayscale difference of the waterproof fabric coating image. The mean of the local orientation change rate of all pixels in the waterproof fabric coating image is denoted as the orientation change rate of the waterproof fabric coating image. The positive correlation between the grayscale difference and the rate of change of direction of the waterproof fabric coating image is recorded as the crack feature value of the waterproof fabric coating image.

5. The intelligent quality assessment method for waterproof fabric according to claim 2, characterized in that, The method for determining the degree of edge closure in the waterproof fabric coating image is as follows: The number of pixels contained in the region defined by the edge in the waterproof fabric coating image is denoted as the area of ​​the region defined by the edge. The number of pixels contained within the smallest circumcircle of the region defined by the edge is denoted as the area of ​​the smallest circumcircle of the region defined by the edge. The difference between the area of ​​the smallest circumcircle of the region defined by the edge and the area of ​​the region defined by the edge is denoted as the first difference of the edge. The ratio of the first difference of the edge to the area of ​​the region defined by the edge is denoted as the first ratio of the edge. The negative correlation result of the first ratio of the edge is denoted as the degree of edge closure.

6. The intelligent quality assessment method for waterproof fabric according to claim 2, characterized in that, The method for calculating the pinhole feature value of the waterproof fabric coating image is as follows: The normalized value of the mean LBP distribution entropy of all edges in the waterproof fabric coating image is denoted as the edge orientation uniformity of the waterproof fabric coating image. The average degree of closure of all edges in the waterproof fabric coating image is denoted as the overall edge closure degree of the waterproof fabric coating image. The mean value of the combined edge closure degree and edge direction uniformity of the waterproof fabric coating image is denoted as the pinhole feature value of the waterproof fabric coating image.

7. The intelligent quality assessment method for waterproof fabric according to claim 1, characterized in that, The method for identifying the center point of the bubbling is as follows: All local maxima in the waterproof fabric coating image are recorded as candidate bubble center points; Radial grayscale verification is performed on the candidate bubble center point: a verification window with a radius of a second preset length is established with the candidate bubble center point as the center, and the grayscale value in the verification window is verified to show a decreasing trend from the center to the surrounding area; candidate bubble center points that do not meet the trend are removed, and the remaining candidate bubble center points are recorded as bubble center points.

8. The intelligent quality assessment method for waterproof fabric according to claim 1, characterized in that, The method for identifying the blemish area at the center point of the blemish is as follows: With the center point of the bubble as the center, construct an annulus with inner radius r1 and outer radius r2. The area corresponding to this annulus is denoted as the bubble defect area at the center point of the bubble, where 0 <r1<r2<=R; in, , ,in and The preset proportional coefficient satisfies .

9. The intelligent quality assessment method for waterproof fabric according to claim 1, characterized in that, The method for determining the bubbling feature value of the waterproof fabric coating image is as follows: Let any pixel on the outer radius of the bubble defect area at the bubble center point be the target outer radius point. Let the part of the line segment with the bubble center point and the target outer radius point as the two endpoints in the bubble defect area at the bubble center point be the radius of the ring between the bubble center point and the target outer radius point. Let the average gray value of all pixels on the ring radius between the bubble center point and the target outer radius point be the first gray value of the bubble center point and the target outer radius point. Select the portion of the straight line passing through both the bubble center point and the target outer radius point within the bubble defect area at the bubble center point. Remove the radius of the annulus between the bubble center point and the target outer radius point, and record the remaining portion as the radius of the annulus opposite the bubble center point and the target outer radius point. Record the average gray value of all pixels on the annulus opposite the bubble center point and the target outer radius point as the second average gray value of the bubble center point and the target outer radius point. The absolute value of the difference between the first gray-scale mean and the second gray-scale mean between the bubble center point and the target outer radius point is recorded as the brightness symmetry value between the bubble center point and the target outer radius point. The mean value of the brightness symmetry feature value of all bubble center points in the waterproof fabric coating image is denoted as the bubble feature value of the waterproof fabric coating image.

10. The intelligent quality assessment method for waterproof fabric according to claim 1, characterized in that, The method for obtaining the quality assessment result of the waterproof fabric based on the crack feature values, pinhole feature values, and blister feature values ​​of the waterproof fabric coating image includes the following specific methods: The crack feature values, pinhole feature values, and blister feature values ​​of the waterproof fabric coating image are arranged sequentially into a column vector to obtain the defect feature vector of the waterproof fabric coating image. Input the defect feature vector of the waterproof fabric coating image of the waterproof fabric coating surface to be evaluated into the decision tree model to obtain the identification results of crack defects, pinhole defects and blister defects; If the identification results for crack defects, pinhole defects, and bubbling defects are all "no defects", the waterproof fabric is determined to be without defects; otherwise, the waterproof fabric is determined to be defective.

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