Surface quality analysis method of floor coating based on image processing

Abstract

The present invention relates to the field of image processing technology, and in particular to a floor construction paint surface quality analysis method based on image processing. The method obtains a suspected bulge area and its corresponding suspected bulge edge according to a grayscale image of the floor construction paint surface; obtains a first characteristic index according to the grayscale value of each pixel point in the suspected bulge area and the gradient value of each pixel point in the suspected bulge edge; obtains a central area pixel point in the suspected bulge area, and obtains a second characteristic index according to the grayscale value difference between the central area pixel point and other pixel points in the suspected bulge area; obtains a lighting influence index of the suspected bulge area according to the first characteristic index and the second characteristic index, detects the quality of the floor construction paint surface according to the lighting influence index, and improves the accuracy of identifying the bulge area on the floor construction paint surface.

Description

Technical Field

[0001] The invention relates to the technical field of image processing, and in particular to a method for analyzing the surface quality of floor construction coatings based on image processing. Background Art

[0002] The ridges on the surface of floor construction paint are usually caused by uneven construction and insufficient paint performance. The durability and safety of the floor construction paint surface often decrease due to the ridges, and some accidents may occur during use due to the ridges. Therefore, in the analysis of the surface quality of floor coatings, it is very important to be able to effectively detect the ridges.

[0003] Since the contour of the raised area is generally irregular, appearing as waves, bulges or lumps, the traditional way to detect the raised area is to use the Sobel operator to identify the entire floor surface image, obtain the raised edge information of the image according to the gradient direction, and connect the determined raised edges to obtain the raised area. However, the surface image of the floor construction paint is collected by using an industrial camera to shoot the floor surface vertically. The captured image may be affected by light spots or diffuse reflections, forming features similar to the raised area, making it difficult to accurately determine whether it is a raised area or affected by light, which greatly reduces the accuracy of the detection and may lead to false detection or missed detection.

[0004] Therefore, how to improve the accuracy of identifying raised areas on the surface of floor construction paint through the Sobel operator edge detection algorithm has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a floor construction paint surface quality analysis method based on image processing to solve the problem of how to improve the accuracy of identifying raised areas on the floor construction paint surface through the Sobel operator edge detection algorithm.

[0006] An embodiment of the present invention provides a method for analyzing the surface quality of floor construction coatings based on image processing, the method comprising the following steps:

[0007] Collecting a grayscale image of the surface of the floor construction paint, performing edge detection on the grayscale image using a Sobel operator, and obtaining at least one suspected raised area in the grayscale image and a suspected raised edge corresponding to each of the suspected raised areas;

[0008] For any suspected uplift region, calculate the gradient value of each pixel in the suspected uplift edge corresponding to the suspected uplift region, and obtain the first characteristic index of the suspected uplift region according to the gray value of each pixel in the suspected uplift region and the gradient value of each pixel in the suspected uplift edge;

[0009] According to the number of pixels in the suspected bulge area, at least one central region pixel in the suspected bulge area is obtained, and in the suspected bulge area, according to the gray value difference between each central region pixel and each other pixel, a second characteristic index of the suspected bulge area is obtained;

[0010] According to the first characteristic index and the second characteristic index, the illumination influence index of the suspected raised area is obtained, the illumination influence index of each suspected raised area in the grayscale image is obtained, and the quality of the floor construction paint surface is detected according to all illumination influence indicators.

[0011] Furthermore, obtaining the first characteristic index of the suspected ridge area according to the grayscale value of each pixel in the suspected ridge area and the gradient value of each pixel in the suspected ridge edge includes:

[0012] Obtaining an average grayscale value of all pixels in the suspected bulge area, normalizing the average grayscale value, and obtaining a grayscale distribution index of the suspected bulge area;

[0013] Normalizing the gradient value of each pixel in the suspected raised edge to obtain a corresponding normalized gradient value, calculating the average of the normalized gradient values ​​of all pixels in the suspected raised edge, and subtracting the average of the normalized gradient values ​​from a constant 1 to obtain the edge influence degree of the suspected raised edge;

[0014] A weighted sum is performed on the grayscale distribution index and the edge influence degree to obtain a first characteristic index of the suspected ridge area.

[0015] Furthermore, acquiring at least one central area pixel point in the suspected bulge area according to the number of pixel points in the suspected bulge area includes:

[0016] Taking the lower left corner vertex of the grayscale image as the origin, constructing a two-dimensional rectangular coordinate system corresponding to the grayscale image, obtaining the coordinates of all pixel points in the suspected bulge area according to the two-dimensional rectangular coordinate system, respectively calculating the average values ​​of the horizontal coordinates and the average values ​​of the vertical coordinates of all pixel points in the suspected bulge area, respectively rounding up the average values ​​of the horizontal coordinates and the average values ​​of the vertical coordinates to obtain the central coordinates, and taking the pixel point located at the central coordinates as the morphological central pixel point of the suspected bulge area;

[0017] Half of the number of all pixel points in the suspected uplift area is rounded up to obtain the target number, and the first pixel point located below the morphological center pixel point is taken as the starting point. The pixel points in the suspected uplift area are spirally traversed in a clockwise direction, and the traversed pixel points and the morphological center pixel point are taken as the central area pixel points until the number of central area pixel points reaches the target number, thereby obtaining all the central area pixel points in the suspected uplift area.

[0018] Furthermore, in the suspected bulge area, according to the gray value difference between each pixel point in the central area and each other pixel point, a second characteristic index of the suspected bulge area is obtained, including:

[0019] The grayscale value average of the pixels in the central area is calculated to obtain the grayscale value average of the central area, and the grayscale value average of all pixels in the suspected bulge area except all the pixels in the central area is calculated to obtain the grayscale value average of the remaining area;

[0020] Calculate the absolute value of the difference between the mean grayscale value of the central area and the mean grayscale value of the remaining area, take the product of the absolute value of the difference and the preset inverse proportional normalization coefficient as the target variable, and take the target variable as an exponent with the natural constant e as the base to obtain the second characteristic indicator of the suspected raised area.

[0021] Furthermore, obtaining the illumination influence index of the suspected raised area according to the first characteristic index and the second characteristic index includes:

[0022] The first characteristic index and the second characteristic index are weightedly summed to obtain the illumination influence index of the suspected ridge area.

[0023] Further, the quality of the floor construction coating surface is detected according to all illumination impact indicators, including:

[0024] If any of the illumination impact indicators among all illumination impact indicators is less than or equal to the preset illumination impact indicator threshold, then there is a quality problem on the surface of the floor construction coating;

[0025] If all lighting impact indicators are greater than the preset lighting impact indicator threshold, there is no quality problem on the surface of the floor construction paint.

[0026] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0027] The present invention collects a grayscale image of the surface of a floor construction coating, performs edge detection on the grayscale image using a Sobel operator, and obtains at least one suspected uplift area in the grayscale image and a suspected uplift edge corresponding to each of the suspected uplift areas; for any suspected uplift area, calculates the gradient value of each pixel point in the suspected uplift edge corresponding to the suspected uplift area, and obtains a first characteristic index of the suspected uplift area according to the grayscale value of each pixel point in the suspected uplift area and the gradient value of each pixel point in the suspected uplift edge; obtains at least one central area pixel point in the suspected uplift area according to the number of pixels in the suspected uplift area, and obtains a second characteristic index of the suspected uplift area according to the grayscale value difference between each central area pixel point and each other pixel point in the suspected uplift area; obtains a lighting influence index of the suspected uplift area according to the first characteristic index and the second characteristic index, obtains the lighting influence index of each suspected uplift area in the grayscale image, and detects the quality of the floor construction coating surface according to all lighting influence indexes. Among them, the Sobel operator is first used to obtain the suspected raised area on the surface of the floor construction paint, and then the characteristics of the suspected raised area are identified in multiple dimensions, that is, the first characteristic index and the second characteristic index. Combining these two characteristic indicators, the lighting influence index of the suspected raised area is obtained, which can effectively distinguish the raised areas belonging to quality problems and the areas affected by lighting, thereby improving the accuracy of identifying the raised areas on the surface of the floor construction paint. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0029] Figure 1 This is a method flow chart of a method for analyzing the surface quality of floor construction coatings based on image processing provided in Embodiment 1 of the present invention;

[0030] Figure 2 It is a schematic diagram of performing a spiral traversal on pixel points in a suspected ridge area to obtain a central pixel point of the area, provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0032] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0033] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0034] See also Figure 1 , is a method flow chart of a method for analyzing the surface quality of floor construction coatings based on image processing provided in the first embodiment of the present invention, such as Figure 1 As shown, the method may include:

[0035] Step S101, collecting a grayscale image of the floor construction paint surface, performing edge detection on the grayscale image using a Sobel operator, and obtaining at least one suspected raised area in the grayscale image and a suspected raised edge corresponding to each suspected raised area.

[0036] In an example of the present invention, a surface image of the floor construction paint is first collected by a high-resolution camera device, and the surface image is preprocessed. Then, the preprocessed surface image is transformed into grayscale to obtain a grayscale image of the floor construction paint surface, so as to improve the accuracy of the surface quality analysis of the floor construction paint. The image preprocessing includes but is not limited to: using image enhancement techniques such as contrast enhancement and sharpening to ensure that surface irregularities are clearer; standardizing the brightness and color of the image to increase the robustness of the algorithm and ensure that images under different acquisition conditions can be processed consistently. The image preprocessing technology is a prior art and will not be described in detail here.

[0037] Since the Sobel operator can detect the gray value change of each pixel in the gray image in the horizontal direction and the vertical direction respectively, the Sobel operator is used to perform edge detection on the gray image to obtain at least one suspected ridge area and its corresponding suspected ridge edge in the gray image. The Sobel operator uses two The convolution kernel and the edge detection using the Sobel operator are prior arts and will not be described in detail here.

[0038] Step S102, for any suspected uplift area, calculate the gradient value of each pixel in the suspected uplift edge corresponding to the suspected uplift area, and obtain the first characteristic index of the suspected uplift area according to the grayscale value of each pixel in the suspected uplift area and the gradient value of each pixel in the suspected uplift edge.

[0039] Since the surface image of the floor construction paint collected may be affected by light spots or diffuse reflections, forming features similar to the raised areas, the suspected raised areas obtained by edge detection using the Sobel operator may be areas affected by lighting rather than raised areas with quality problems. Therefore, further analysis of the suspected raised areas is required to improve the accuracy of identifying raised areas on the surface of floor construction paint.

[0040] Considering that the raised area with quality problems is a physical defect, the gradient value at its edge is large, while the area affected by illumination is affected by diffuse reflection, and the gradient value at its edge is relatively small. At the same time, the grayscale value of the area affected by illumination is larger than that of the raised area with quality problems. Therefore, for any suspected raised area in the grayscale image, the first characteristic index of the suspected raised area is obtained according to the grayscale value of each pixel in the suspected raised area and the gradient value of each pixel in the suspected raised edge corresponding to the suspected raised area. The specific acquisition method is:

[0041] Obtaining an average grayscale value of all pixels in the suspected bulge area, normalizing the average grayscale value, and obtaining a grayscale distribution index of the suspected bulge area;

[0042] Normalizing the gradient value of each pixel in the suspected raised edge to obtain a corresponding normalized gradient value, calculating the average of the normalized gradient values ​​of all pixels in the suspected raised edge, and subtracting the average of the normalized gradient values ​​from a constant 1 to obtain the edge influence degree of the suspected raised edge;

[0043] A weighted sum is performed on the grayscale distribution index and the edge influence degree to obtain a first characteristic index of the suspected ridge area.

[0044] In one embodiment, the calculation formula of the first characteristic index of the suspected ridge area is:

[0045]

[0046] Wherein, H represents the first characteristic index of the suspected bulge area, N represents the number of all pixels in the suspected bulge area, represents the gray value of the i-th pixel in the suspected uplift area, M represents the number of all pixels in the suspected uplift edge corresponding to the suspected uplift area, represents the gradient value of the jth pixel point in the suspected uplift edge corresponding to the suspected uplift area, represents the first weight value, represents the second weight value, Represents a linear normalization function, 255 represents the maximum grayscale value, and 1 represents a constant.

[0047] It should be noted that It is the grayscale distribution index of the suspected uplift area, which is used to characterize the size of the grayscale value in the suspected uplift area. The larger the grayscale distribution index, the brighter the suspected uplift area; the smaller the grayscale distribution index, the darker the suspected uplift area. Since the area affected by light is brighter than the uplift area with quality problems, the smaller the grayscale distribution index of the suspected uplift area, the darker the suspected uplift area, and thus the smaller the value of H, the smaller the possibility that the suspected uplift area is the area affected by light, and the greater the possibility that the suspected uplift area is the uplift area with quality problems. That is the normalized value of the gradient, It is the mean of the normalized gradient values, which is used to characterize the average gradient value of the suspected uplift edge corresponding to the suspected uplift area. In order to unify the trend of the overall formula, the constant 1 is subtracted from the mean of the normalized gradient value to obtain the edge influence degree of the suspected uplift edge, that is, . Since the gradient value at the edge of the raised area with quality problems is large, and the gradient value at the edge of the area affected by illumination is relatively small due to the influence of diffuse reflection, the larger the mean of the normalized gradient value, the smaller the edge influence of the suspected raised edge corresponding to the suspected raised area, and the smaller the value of H, the smaller the possibility that the suspected raised area is the area affected by illumination, and the greater the possibility that the suspected raised area is the raised area with quality problems. Settings The value of is 0.5, The value is 0.5. There is no limit here. The implementer can set it according to the implementation scenario.

[0048] Step S103, according to the number of pixels in the suspected uplift area, obtain at least one central area pixel in the suspected uplift area, and in the suspected uplift area, obtain a second characteristic indicator of the suspected uplift area according to the grayscale value difference between each central area pixel and each other pixel.

[0049] Considering that the brightness of the raised area with quality problems shows a trend of gradually darkening from the morphological center to the edge, the grayscale value difference between the pixels in the central area and the pixels in other areas is more obvious in the raised area with quality problems, while the area affected by illumination does not have the feature of brightness gradient due to the influence of diffuse reflection, resulting in a small difference in grayscale value between the pixels in the central area and the pixels in other areas in the area affected by illumination. Therefore, in the suspected raised area, the second characteristic index of the suspected raised area is obtained according to the grayscale value difference between the pixels in the central area and the pixels in other areas. The steps for obtaining the second characteristic index are as follows:

[0050] (1) According to the number of pixel points in the suspected ridge area, at least one central area pixel point in the suspected ridge area is obtained.

[0051] Specifically, taking the lower left corner vertex of the grayscale image as the origin, constructing a two-dimensional rectangular coordinate system corresponding to the grayscale image, obtaining the coordinates of all pixel points in the suspected uplift area according to the two-dimensional rectangular coordinate system, respectively calculating the average values ​​of the horizontal coordinates and the average values ​​of the vertical coordinates of all pixel points in the suspected uplift area, respectively rounding up the average values ​​of the horizontal coordinates and the average values ​​of the vertical coordinates to obtain the center coordinates, and taking the pixel point located at the center coordinates as the morphological center pixel point of the suspected uplift area;

[0052] Half of the number of all pixel points in the suspected uplift area is rounded up to obtain the target number, and the first pixel point located below the morphological center pixel point is taken as the starting point. The pixel points in the suspected uplift area are spirally traversed in a clockwise direction, and the traversed pixel points and the morphological center pixel point are taken as the central area pixel points until the number of central area pixel points reaches the target number, thereby obtaining all the central area pixel points in the suspected uplift area.

[0053] Example: See Figure 2 , which is a schematic diagram of spirally traversing the pixels in the suspected ridge area to obtain the center pixel of the area. Figure 2 In the figure, each square represents a pixel in the suspected bulge area, the black square is the morphological center pixel, square 1 is the first pixel below the morphological center pixel, and the direction of the arrow is the clockwise spiral traversal direction. Square 1 is taken as the starting point, and squares 2, 3, and 4 are traversed in the direction of the arrow, and so on. The traversed pixels and the morphological center pixel are taken as the center area pixels until the number of center area pixels reaches the target number, that is, the number of center area pixels is half of the number of all pixels in the suspected bulge area.

[0054] (2) In the suspected bulge area, a second characteristic index of the suspected bulge area is obtained according to the gray value difference between each pixel point in the central area and each other pixel point.

[0055] Specifically, the grayscale value average of the pixels in the central area is calculated to obtain the grayscale value average of the central area, and the grayscale value average of all pixels in the suspected bulge area except all the pixels in the central area is calculated to obtain the grayscale value average of the remaining area;

[0056] Calculate the absolute value of the difference between the mean grayscale value of the central area and the mean grayscale value of the remaining area, take the product of the absolute value of the difference and the preset inverse proportional normalization coefficient as the target variable, and take the target variable as an exponent with the natural constant e as the base to obtain the second characteristic indicator of the suspected raised area.

[0057] In one embodiment, the calculation formula of the second characteristic index of the suspected ridge area is:

[0058]

[0059] Wherein, W represents the second characteristic index of the suspected ridge area, k represents the preset inverse proportional normalization coefficient, and n represents the number of pixels in the central area. Indicates The grayscale value of the pixel in the central area, N represents the number of all pixels in the suspected bulge area, Indicates the number of all pixels in the suspected bulge area except the pixels in the central area. Indicates the pixels in the suspected bulge area other than the central area pixels. The gray value of each pixel, e represents a natural constant, Represents the absolute value symbol.

[0060] It should be noted that the value of k is set to 3, which is not limited here and can be set by the implementer according to the implementation scenario. is the mean gray value of the central area, = is the mean grayscale value of the remaining area. Since the grayscale value difference between the central area and other areas is more obvious in the bulge area with quality problems, the larger the absolute value of the difference between the mean grayscale value of the central area and the mean grayscale value of the remaining area, the greater the difference in grayscale value between the pixels in the central area and the pixels in other areas in the suspected bulge area, and the smaller the value of W, the smaller the possibility that the suspected bulge area is the area affected by light, and the greater the possibility that the suspected bulge area is the bulge area with quality problems.

[0061] Step S104, obtaining the lighting influence index of the suspected raised area according to the first characteristic index and the second characteristic index, obtaining the lighting influence index of each suspected raised area in the grayscale image, and detecting the quality of the floor construction paint surface according to all lighting influence indicators.

[0062] Considering that the first characteristic index shows a small value in the raised area with quality problems, it is impossible to set a reasonable threshold to apply to multiple scenarios based on this dimension alone. Therefore, in order to apply to multiple scenarios and have stronger robustness, in the suspected raised area, the second characteristic index is obtained based on the grayscale value difference between the pixels in the central area and the pixels in other areas. Combining the first characteristic index and the second characteristic index, the illumination influence index of the suspected raised area is obtained, and then it is judged whether there is a quality problem on the surface of the floor construction paint. The illumination influence index of the suspected raised area is obtained as follows:

[0063] The first characteristic index and the second characteristic index are weightedly summed to obtain the illumination influence index of the suspected ridge area.

[0064] In one embodiment, the calculation formula of the illumination impact index of the suspected ridge area is:

[0065]

[0066] Among them, T represents the illumination impact index of the suspected uplift area, represents the first weight coefficient, represents the second weight coefficient, H represents the first characteristic index of the suspected uplift area, and W represents the second characteristic index of the suspected uplift area.

[0067] It should be noted that setting The value of is 0.4, The value of is 0.6, which is not limited here and can be set by the implementer according to the implementation scenario. The smaller the value of H, the darker the suspected uplift area is, and the gradient value at the edge of the suspected uplift area is larger, and the smaller the illumination impact index is, the smaller the possibility that the suspected uplift area is an area affected by illumination, that is, the greater the possibility that the suspected uplift area is an uplift area with quality problems; the smaller the value of W, the greater the difference in grayscale values ​​between the pixels in the central area and the pixels in other areas in the suspected uplift area, and the smaller the illumination impact index is, the smaller the possibility that the suspected uplift area is an area affected by illumination, that is, the greater the possibility that the suspected uplift area is an uplift area with quality problems.

[0068] Similarly, obtain the lighting influence index of each suspected raised area in the grayscale image of the floor construction paint surface, and set the preset lighting influence index threshold to 0.5. There is no restriction here, and the implementer can set it according to the implementation scenario. If any of the lighting influence indicators of all suspected raised areas is less than or equal to 0.5, then there are raised areas with quality problems in the grayscale image of the floor construction paint surface, indicating that there are quality problems on the floor construction paint surface; if each lighting influence index is greater than 0.5, then the suspected raised areas in the grayscale image of the floor construction paint surface are areas affected by lighting, indicating that there are no quality problems on the floor construction paint surface. Furthermore, after determining that there is a quality problem, the causes of the raised areas on the floor construction paint surface can be evaluated and targeted solutions can be provided.

[0069] It is worth mentioning that the focus of the present invention is how to accurately detect the raised areas caused by quality problems based on the characteristic differences between the raised areas caused by quality problems and the areas affected by light, evaluate the causes of the raised areas on the surface of floor construction paint, and carry out targeted solutions, which belongs to the prior art and will not be repeated here.

[0070] In summary, the present invention collects a grayscale image of the surface of floor construction paint, uses the Sobel operator to perform edge detection on the grayscale image, and obtains at least one suspected uplift area in the grayscale image and a suspected uplift edge corresponding to each of the suspected uplift areas; for any suspected uplift area, calculates the gradient value of each pixel point in the suspected uplift edge corresponding to the suspected uplift area, and obtains a first characteristic index of the suspected uplift area according to the grayscale value of each pixel point in the suspected uplift area and the gradient value of each pixel point in the suspected uplift edge; according to the number of pixels in the suspected uplift area, obtains at least one central area pixel point in the suspected uplift area, and in the suspected uplift area, obtains a second characteristic index of the suspected uplift area according to the grayscale value difference between each central area pixel point and each other pixel point; according to the first characteristic index and the second characteristic index, obtains a lighting influence index of the suspected uplift area, obtains a lighting influence index of each suspected uplift area in the grayscale image, and detects the quality of the floor construction paint surface according to all lighting influence indicators. Among them, the Sobel operator is first used to obtain the suspected raised area on the surface of the floor construction paint, and then the characteristics of the suspected raised area are identified in multiple dimensions, that is, the first characteristic index and the second characteristic index. Combining these two characteristic indicators, the lighting influence index of the suspected raised area is obtained, which can effectively distinguish the raised areas belonging to quality problems and the areas affected by lighting, thereby improving the accuracy of identifying the raised areas on the surface of the floor construction paint.

[0071] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the surface quality of floor coatings based on image processing, characterized in that: The floor construction coating surface quality analysis method based on image processing comprises: Collecting a grayscale image of the surface of the floor construction paint, performing edge detection on the grayscale image using a Sobel operator, and obtaining at least one suspected raised area in the grayscale image and a suspected raised edge corresponding to each of the suspected raised areas; For any suspected uplift region, calculate the gradient value of each pixel in the suspected uplift edge corresponding to the suspected uplift region, and obtain the first characteristic index of the suspected uplift region according to the gray value of each pixel in the suspected uplift region and the gradient value of each pixel in the suspected uplift edge; According to the number of pixels in the suspected bulge area, at least one central region pixel in the suspected bulge area is obtained, and in the suspected bulge area, according to the gray value difference between each central region pixel and each other pixel, a second characteristic index of the suspected bulge area is obtained; According to the first characteristic index and the second characteristic index, a lighting influence index of the suspected raised area is obtained, a lighting influence index of each suspected raised area in the grayscale image is obtained, and the quality of the floor construction coating surface is detected according to all lighting influence indicators; The step of obtaining a first characteristic index of the suspected ridge region according to the grayscale value of each pixel in the suspected ridge region and the gradient value of each pixel in the suspected ridge edge includes: Obtaining an average grayscale value of all pixels in the suspected bulge area, normalizing the average grayscale value, and obtaining a grayscale distribution index of the suspected bulge area; Normalizing the gradient value of each pixel in the suspected raised edge to obtain a corresponding normalized gradient value, calculating the average of the normalized gradient values ​​of all pixels in the suspected raised edge, and subtracting the average of the normalized gradient values ​​from a constant 1 to obtain the edge influence degree of the suspected raised edge; Performing a weighted summation on the grayscale distribution index and the edge influence degree to obtain a first characteristic index of the suspected ridge area; In the suspected bulge area, according to the gray value difference between each pixel point in the central area and each other pixel point, a second characteristic index of the suspected bulge area is obtained, including: The grayscale value average of the pixels in the central area is calculated to obtain the grayscale value average of the central area, and the grayscale value average of all pixels in the suspected bulge area except all the pixels in the central area is calculated to obtain the grayscale value average of the remaining area; Calculate the absolute value of the difference between the mean grayscale value of the central area and the mean grayscale value of the remaining area, take the product of the absolute value of the difference and the preset inverse proportional normalization coefficient as the target variable, and take the target variable as an exponent with the natural constant e as the base to obtain the second characteristic indicator of the suspected raised area.

2. The method for analyzing the surface quality of floor construction coatings based on image processing according to claim 1, characterized in that: The acquiring, according to the number of pixels in the suspected bulge area, at least one central area pixel in the suspected bulge area comprises: Taking the lower left corner vertex of the grayscale image as the origin, constructing a two-dimensional rectangular coordinate system corresponding to the grayscale image, obtaining the coordinates of all pixel points in the suspected bulge area according to the two-dimensional rectangular coordinate system, respectively calculating the average values ​​of the horizontal coordinates and the average values ​​of the vertical coordinates of all pixel points in the suspected bulge area, respectively rounding up the average values ​​of the horizontal coordinates and the average values ​​of the vertical coordinates to obtain the central coordinates, and taking the pixel point located at the central coordinates as the morphological central pixel point of the suspected bulge area; Half of the number of all pixel points in the suspected uplift area is rounded up to obtain the target number, and the first pixel point located below the morphological center pixel point is taken as the starting point. The pixel points in the suspected uplift area are spirally traversed in a clockwise direction, and the traversed pixel points and the morphological center pixel point are taken as the central area pixel points until the number of central area pixel points reaches the target number, thereby obtaining all the central area pixel points in the suspected uplift area.

3. The method for analyzing the surface quality of floor construction coatings based on image processing according to claim 1, characterized in that: The obtaining, according to the first characteristic index and the second characteristic index, the illumination influence index of the suspected ridge area includes: The first characteristic index and the second characteristic index are weightedly summed to obtain the illumination influence index of the suspected ridge area.

4. The method for analyzing the surface quality of floor construction coatings based on image processing according to claim 1, characterized in that: The quality of the floor coating surface is detected according to all lighting impact indicators, including: If any of the illumination impact indicators among all illumination impact indicators is less than or equal to the preset illumination impact indicator threshold, then there is a quality problem on the surface of the floor construction coating; If all lighting impact indicators are greater than the preset lighting impact indicator threshold, there is no quality problem on the surface of the floor construction paint.

Images