Surface microstructure measurement method based on gray depth

Abstract

The invention relates to the technical field of micro-topography measurement, in particular to a surface micro-topography measurement method based on gray depth, which comprises the following steps: acquiring the reflective rate of the surface of a material, determining the number of shooting points, and symmetrically setting the shooting points; acquiring image information of a single shooting point location, and preprocessing the image information to determine the gray gradient of the symmetrical shooting point locations for the same pixel block and determine whether the pixel block has defects or not; determining a gray contrast by taking the pixel block with the defect as a center and a preset pixel distance as an interval so as to determine a defect range; fitting the defect ranges determined by all the shooting points to determine the reliability, determining whether the identification precision of the defect is qualified according to the reliability, and adjusting the identification parameters according to the unqualified identification precision; and determining the morphology of the surface of the material according to the gray gradient for the defect with qualified identification precision. According to the invention, the identification precision of microscopic morphology detection and the industrial detection efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microscopic topography measurement, and particularly to a method for measuring surface microscopic topography based on gray depth. Background Art

[0002] In the field of surface microscopic topography measurement, traditional calibration methods based on gray depth infer the surface topography of materials by establishing a linear or non-linear mapping relationship between gray values and actual physical depths (such as gray-height calibration curves). However, their accuracy is limited by the reflection characteristics of materials, lighting conditions, and non-linear interference of the imaging system. For example, highly reflective materials such as metals and ceramics can cause a serious deviation between gray values and actual depths due to specular reflection effects. Especially on inclined surfaces or in regions with complex curvatures, gray saturation or local shadows will further distort depth information, and the error can reach more than 30% of the nominal value. In addition, factors such as ambient light fluctuations, camera dynamic range limitations, and lens distortions will introduce systematic biases, making it impossible for a single gray value to stably represent the actual microscopic topography. Research shows that at the same depth, the gray difference between a polished steel surface and an alumina coating can reach 50 - 80 levels (8-bit gray scale), resulting in the need for frequent recalibration of traditional methods, which greatly limits the industrial inspection efficiency.

[0003] Chinese Patent Publication No.: CN110726380A discloses a multi-spectral microscopic three-dimensional topography detection device and method. The device includes: a compound light illumination module and a longitudinal dispersion enhanced optical imaging module. The compound light illumination module is sequentially provided with: a compound light source, a condenser lens, a uniform collimating lens group, a semi-reflective and semi-transmissive beam splitter, and an objective lens with longitudinal dispersion in the light path propagation direction; the longitudinal dispersion enhanced optical imaging module is sequentially provided with: a longitudinally height-adjustable sample stage, an objective lens with longitudinal dispersion, a semi-reflective and semi-transmissive beam splitter, a tube lens with longitudinal dispersion, a multi-spectral image sensor, and an image display and analysis module. This method can achieve microscopic three-dimensional topography detection with high lateral resolution, sub-micron level high longitudinal measurement accuracy, and millimeter level large longitudinal measurement range in a single imaging without mechanical movement, combining high measurement efficiency and high measurement accuracy.

[0004] There are also the following problems in the prior art: Calibrating physical depth using gray depth requires targeted calibration according to different materials, and for products of the same material in different batches, it may still be necessary to recalibrate to improve calibration accuracy, resulting in low industrial inspection efficiency. Summary of the Invention

[0005] Therefore, the present invention provides a method for measuring surface microscopic topography based on gray depth to overcome the problem of low industrial inspection efficiency caused by the need for recalibration for different materials in the prior art.

[0006] To achieve the above object, the present invention provides a method for measuring the surface micro-topography based on gray depth, including:

[0007] Obtain the reflectance of the material surface to determine the number of shooting points based on the reflectance, and symmetrically set the shooting points with the vertical line where the center point of the material surface is located as the center axis;

[0008] Obtain the image information of a single shooting point and preprocess the image information to determine the gray gradient of the symmetric shooting points for the same pixel block, and determine whether there are defects in the pixel block according to the gray gradient;

[0009] Taking the pixel block with defects as the center, determine the gray contrast with a preset pixel distance as the spacing, and determine the defect range according to the gray contrast;

[0010] Fit the defect ranges determined by all shooting points to determine the reliability, and determine whether the recognition accuracy of the defect is qualified according to the comparison result between the reliability and the preset reliability, and adjust the preset reflectance or the preset reliability according to the unqualified recognition accuracy;

[0011] Based on the defects with qualified recognition accuracy, determine the topography of the material surface according to the gray gradient.

[0012] Further, the process of determining the number of shooting points based on the reflectance includes:

[0013] Compare the reflectance with a preset reflectance;

[0014] Based on the comparison result that the reflectance is greater than the preset reflectance, determine the number of shooting points as the first number;

[0015] Based on the comparison result that the reflectance is less than or equal to the preset reflectance, determine the number of shooting points as the second number.

[0016] Further, the process of determining whether there are defects in the pixel block according to the gray gradient includes:

[0017] Compare the gray gradient with a preset gray gradient;

[0018] Based on the comparison result that the gray gradient is greater than the preset gray gradient, determine that there are defects in the pixel block.

[0019] Further, the process of determining the defect range includes:

[0020] Taking the pixel block with defects as the center, determine the gray contrast with a preset pixel distance as the spacing;

[0021] Compare the gray contrast with a preset contrast;

[0022] Determine that the preset pixel distance range is the defect range based on the comparison result that the grayscale contrast is greater than or equal to the preset contrast;

[0023] Determine to increase the preset pixel distance by a preset amplitude to obtain a second pixel distance based on the comparison result that the grayscale contrast is less than the preset contrast;

[0024] Taking the pixel block with defects as the center, determine the second grayscale contrast with the second pixel distance;

[0025] Compare the second grayscale contrast with the preset contrast until the second grayscale contrast is greater than the preset contrast, and determine the second pixel distance range as the defect range.

[0026] Further, the determination process of the reliability includes:

[0027] Taking any one of the defect ranges as a reference, fit the remaining defect ranges to determine the goodness of fit;

[0028] Compare the goodness of fit with the preset goodness of fit;

[0029] Determine that the defect ranges overlap based on the comparison result that the goodness of fit is greater than or equal to the preset goodness of fit;

[0030] Determine the comparison between the number of overlapping defect ranges and the number of all defect ranges as the reliability.

[0031] Further, the process of determining whether the recognition accuracy of the defect is qualified based on the reliability includes:

[0032] Compare the reliability with the preset reliability;

[0033] Determine that the recognition accuracy of the defect is qualified based on the comparison result that the reliability is greater than or equal to the preset reliability;

[0034] Determine that the recognition accuracy of the defect is unqualified based on the comparison result that the reliability is less than the preset reliability.

[0035] Further, the process of determining the adjustment parameter according to the unqualified recognition accuracy includes:

[0036] Subtract the reliability from the preset reliability to obtain a difference percentage;

[0037] Determine to increase the preset reflectance based on the comparison result that the difference percentage is greater than the preset percentage;

[0038] Determine to increase the preset reliability based on the comparison result that the difference percentage is less than or equal to the preset percentage.

[0039] Further, the process of adjusting the preset reflectance includes:

[0040] Determine the gray entropy of the defect range and compare the gray entropy with a preset entropy;

[0041] Based on the comparison result that the gray entropy is greater than the preset entropy, determine to increase the preset reflectance with a first adjustment coefficient.

[0042] Further, the process of adjusting the preset reliability includes:

[0043] Determine the inverse difference moment of the defect range and compare the inverse difference moment with a preset inverse difference moment;

[0044] Based on the comparison result that the inverse difference moment is less than the preset inverse difference moment, determine to increase the preset reliability with a second adjustment coefficient.

[0045] Further, for defects with qualified recognition accuracy, determine the three-dimensional morphology of the defect according to the gray gradient, and determine the three-dimensional morphology of the material surface with the defect as the reference according to the gray gradient.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows. The present invention determines the number of shooting points according to the reflectivity. A high reflectivity indicates that under the same lighting conditions, the image obtained by the camera is clearer. For materials with a high reflectivity, a smaller number of shooting points are set, which can improve the detection efficiency on the premise of ensuring the detection accuracy. For materials with a low reflectivity, a larger number of shooting points are set to improve the detection accuracy. The shooting points are symmetrically distributed based on the central axis of the material surface. The gray level gradient of the symmetric shooting points for the same pixel block is determined. The gray level gradient is the absolute difference in the gray level depth of the images obtained by the symmetric shooting points for the same pixel block. A large gray level gradient indicates that there are defects in the pixel block, and a small gray level gradient indicates that there are no defects in the pixel block. Judging the defects according to the gray level gradient eliminates the inaccuracy caused by objective factors when calibrating solely based on the gray level depth. Under the condition of determining the pixel block with defects, based on the pixel block, the gray level contrast of the local area is further determined. The larger the gray level contrast, the clearer the edge. The edge of the defect is determined according to the gray level contrast to determine the defect range. On the basis of further determining the defect range, the defect ranges determined by each symmetric shooting point are obtained, and horizontal comparison is carried out and the recognition accuracy of the defect is determined according to the comparison result to further eliminate the problem of low detection accuracy caused by objective reasons. Under the condition that the recognition accuracy of the defect is determined to be qualified, that is, under the condition that the corresponding relationship between the gray level gradient and the physical depth is determined to be qualified, the specific morphology of the defect is accurately determined according to the gray level gradient, and the three-dimensional morphology of the remaining part of the image is converted according to the gray level gradient, thereby further improving the recognition accuracy of the microscopic morphology detection and further improving the industrial detection efficiency.

[0047] Furthermore, the present invention symmetrically sets a number of shooting points, and determines whether there are defects in the pixel block according to the gray level gradient of the same pixel block by two symmetric shooting points. The gray level gradient of the pixel block without defects is a fixed value. When there are defects, the gray level depth will become smaller, resulting in a larger gray level gradient. Confirming the existence of defects according to the gray level gradient can eliminate the error caused by the deviation between the gray level depth and the actual depth, thereby further improving the recognition accuracy of the microscopic morphology detection and further improving the industrial detection efficiency.

[0048] Furthermore, under the condition that the recognition accuracy of the defect is determined to be unqualified, the preset reflectivity or the preset reliability is adjusted according to the difference amplitude of the reliability of the defect range, which improves the accuracy of the defect detection benchmark, thereby further improving the recognition accuracy of the microscopic morphology detection and further improving the industrial detection efficiency.

[0049] Furthermore, on the premise that the recognition accuracy of defects is qualified, the present invention determines the microscopic topography of the remaining part of the image according to the gray gradient. The qualified recognition accuracy of defects means that the physical height corresponding to the gray gradient is accurate, thereby further improving the recognition accuracy of microscopic topography detection and further improving the industrial detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flowchart of the method for measuring surface microscopic topography based on gray depth according to an embodiment of the present invention;

[0051] Figure 2 is a flowchart of determining the number of shooting points according to an embodiment of the present invention;

[0052] Figure 3 is a flowchart of determining whether there are defects in a pixel block according to an embodiment of the present invention;

[0053] Figure 4 is a flowchart of determining whether the recognition accuracy of defects is qualified according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0056] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0057] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0058] Please refer to Figures 1-4 as shown Figure 1Flow chart of the surface micro-topography measurement method based on gray depth according to an embodiment of the present invention; Figure 2 Flow chart of determining the number of shooting points according to an embodiment of the present invention; Figure 3 Flow chart of determining whether there are defects in a pixel block according to an embodiment of the present invention; Figure 4 Flow chart of determining whether the recognition accuracy of defects is qualified according to an embodiment of the present invention.

[0059] An embodiment of the present invention provides a surface micro-topography measurement method based on gray depth, including:

[0060] Step S1: Obtain the reflectivity of the material surface, determine the number of shooting points based on the reflectivity, and symmetrically set the shooting points with the vertical line where the center point of the material surface is located as the center;

[0061] Step S2: Obtain the image information of a single shooting point and preprocess the image information to determine the gray gradient of the symmetric shooting points for the same pixel block, and determine whether there are defects in the pixel block according to the gray gradient;

[0062] Step S3: Take the pixel block with defects as the center, determine the gray contrast with a preset pixel distance as the spacing, and determine the defect range according to the gray contrast;

[0063] Step S4: Fit the defect ranges determined by all shooting points to determine the reliability, determine whether the recognition accuracy of the defects is qualified according to the comparison result between the reliability and the preset reliability, and adjust the preset reflectivity or the preset reliability according to the unqualified recognition accuracy;

[0064] Step S5: Determine the topography of the material surface based on the defects with qualified recognition accuracy according to the gray gradient.

[0065] Specifically, the reflectivity refers to the ability of an object's surface to reflect light. Under the same light source, the higher the reflectivity of an object, the more light it reflects, making the object brighter and more prominent under the illumination conditions, which will increase the recognition and clarity of the object. Therefore, the number of shooting points is determined according to the reflectivity of the material surface. For those with low clarity under the same light source, more shooting points are set, and for those with high clarity under the same light source, fewer shooting points are set, which can reduce the amount of data processing as much as possible while ensuring the detection accuracy and improve the detection efficiency.

[0066] Specifically, the light source can be set at any position, but the best position is the one that can make the clarity of the material reach the highest. The specific brightness and model of the light source are not limited. Using the gray gradient can eliminate the problem of insufficient recognition accuracy caused by the light source.

[0067] Specifically, the preprocessing process of the image information includes but is not limited to grayscale conversion, denoising, filtering, and contrast enhancement.

[0068] Specifically, the present invention measures the overall microscopic morphology based on the recognition of defects on the material surface. Under the condition that the recognition accuracy of the defects is qualified, the pixel coordinates are converted according to the gray gradient to convert the gray image into a three-dimensional image.

[0069] Specifically, the process of determining the number of shooting points based on the reflectance includes:

[0070] Comparing the reflectance with a preset reflectance;

[0071] Based on the comparison result that the reflectance is greater than the preset reflectance, determining the number of shooting points as the first number;

[0072] Based on the comparison result that the reflectance is less than or equal to the preset reflectance, determining the number of shooting points as the second number.

[0073] Specifically, the value range of the preset reflectance is set to [10%, 95%], and preferably 40% in the embodiments of the present invention.

[0074] Specifically, the calculation formula for the first number is:

[0075]

[0076] Wherein, N1 represents the number of shooting points, L represents the length of the material surface to be photographed, W represents the width covered by a single shooting point, O represents the overlap percentage, it is set that 0 = 20%, and ρ represents the reflectance.

[0077] The calculation formula for the second number is:

[0078]

[0079] Wherein, N2 represents the number of shooting points, L represents the length of the material surface to be photographed, W represents the width covered by a single shooting point, O represents the overlap percentage, it is set that 0 = 20%, and ρ represents the reflectance.

[0080] Specifically, the process of determining whether there are defects in the pixel block according to the gray gradient includes:

[0081] Comparing the gray gradient with a preset gray gradient;

[0082] Based on the comparison result that the gray gradient is greater than the preset gray gradient, determining that there are defects in the pixel block.

[0083] Specifically, the value of the preset gray gradient is determined according to the reflectivity of the material surface, and a gray gradient corresponding to the reflectivity is preset.

[0084] It can be understood that when there are no defects in the pixel blocks, the gray values of the same pixel block are different for shootings in different directions because the received light is different due to different distances between the camera and the pixel block, but the gray gradient is fixed. Therefore, when it is determined that the gray gradient is greater than the preset gray gradient, it can be determined that there are defects in the pixel block.

[0085] Specifically, the process of determining the defect range includes:

[0086] Taking the pixel block with defects as the center, determining the gray contrast with a preset pixel distance as the spacing;

[0087] Comparing the gray contrast with a preset contrast;

[0088] Based on the comparison result that the gray contrast is greater than or equal to the preset contrast, determining the preset pixel distance range as the defect range;

[0089] Based on the comparison result that the gray contrast is less than the preset contrast, determining to increase the preset pixel distance by a preset amplitude to obtain a second pixel distance;

[0090] Taking the pixel block with defects as the center, determining a second gray contrast with the second pixel distance;

[0091] Comparing the second gray contrast with the preset contrast until the second gray contrast is greater than the preset contrast, and determining the second pixel distance range as the defect range.

[0092] Specifically, the preset pixel distance is 1 pixel, and the gray contrast represents the edge sharpness. Defects usually have a large gray gradient with adjacent pixel blocks, and a smaller gray contrast indicates that the range within the preset pixel distance is still a defect and it is impossible to determine whether the pixels connected to the preset pixel distance are defective. Therefore, the preset pixel distance is increased to make a re - determination until the gray contrast is greater than or equal to the preset contrast, and the range within the second pixel distance is the defect range.

[0093] Specifically, the value range of the preset contrast is set to [5%, 15%], and preferably 18% in the embodiments of the present invention; the preset amplitude is 1 pixel.

[0094] Specifically, the process of determining the reliability includes:

[0095] Taking any one of the defect ranges as a reference, fitting the remaining defect ranges to determine the fitting degree;

[0096] Compare the fitting degree with a preset fitting degree;

[0097] Determine that the defect range coincides based on the comparison result that the fitting degree is greater than or equal to the preset fitting degree;

[0098] Determine that the defect range does not coincide based on the comparison result that the fitting degree is less than the preset fitting degree;

[0099] Determine the reliability by comparing the number of overlapping defect ranges with the number of all defect ranges.

[0100] Specifically, several defect ranges can be obtained at each shooting point. A single defect range corresponds to several images based on all shooting points. The process of fitting for a single defect range includes:

[0101] Taking any image as a reference image, comparing the remaining images with the reference image to determine several pixel blocks with consistent gray gradients, and based on several said pixel blocks, determining the pixel block with the largest gray gradient, and determining the pixel block with the largest gray gradient as the fitting center;

[0102] Establish a rectangular coordinate system with the fitting center as the origin, establish a first gradient curve with the gray gradient of a single pixel block in the horizontal axis direction, and sequentially establish several second gradient curves in the horizontal axis direction for all pixel blocks within the defect range with the vertical axis as the base point;

[0103] Overlap the corresponding gradient curves in several images, and the ratio of the distance of the overlapping part to the total distance of the curves is the coincidence degree;

[0104] Count the number of images with the coincidence degree greater than the preset coincidence degree, and determine the ratio of the number of images to the total number of images as the fitting degree.

[0105] Specifically, the value range of the preset coincidence degree is set to [95%, 98%], and 96% is preferably used in the embodiments of the present invention.

[0106] Specifically, the value range of the preset fitting degree is set to [90%, 99%], and 98% is preferably used in the embodiments of the present invention.

[0107] Specifically, the process of determining whether the recognition accuracy of the defect is qualified based on the reliability includes:

[0108] Compare the reliability with a preset reliability;

[0109] Determine that the recognition accuracy of the defect is qualified based on the comparison result that the reliability is greater than or equal to the preset reliability;

[0110] Determine that the recognition accuracy of the defect is unqualified based on the comparison result that the reliability is less than the preset reliability.

[0111] Specifically, the value range of the preset reliability is set to [94%, 99%], and 96% is preferred in the embodiments of the present invention.

[0112] Specifically, the process of determining the adjustment parameter according to the recognition accuracy of non-conformities includes:

[0113] Subtract the preset reliability from the reliability to obtain a difference percentage;

[0114] Subtract the preset reliability from the reliability to obtain a difference percentage;

[0115] Based on the comparison result that the difference percentage is greater than the preset percentage, determine to increase the preset reflectance rate;

[0116] Based on the comparison result that the difference percentage is less than or equal to the preset percentage, determine to increase the preset reliability.

[0117] Specifically, the value range of the preset percentage is set to [1%, 3%], and 2% is preferred in the embodiments of the present invention.

[0118] Specifically, the process of adjusting the preset reflectance rate includes:

[0119] Determine the gray entropy of the defect range and compare the gray entropy with the preset entropy;

[0120] Based on the comparison result that the gray entropy is greater than the preset entropy, determine to increase the preset reflectance rate with a first adjustment coefficient;

[0121] Based on the comparison result that the gray entropy is less than or equal to the preset entropy, determine not to increase the preset reflectance rate.

[0122] Specifically, the gray entropy represents the texture complexity of the defect. A large entropy value indicates a large texture complexity, indicating that the division of the defect range is inaccurate; a small entropy value indicates a small texture complexity, indicating that the division of the defect range is accurate.

[0123] Specifically, the value range of the preset entropy is set to [5 bits, 7 bits], and 6 bits is preferred in the embodiments of the present invention; the value range of the first adjustment coefficient is set to [1.2, 1.3], and 1.25 is preferred in the embodiments of the present invention;

[0124] Specifically, the process of adjusting the number of shooting points includes:

[0125] Determine the inverse difference moment of the defect range and compare the inverse difference moment with the preset inverse difference moment;

[0126] Determine the inverse difference moment of the defect range and compare the inverse difference moment with the preset inverse difference moment;

[0127] Determine to increase the preset reliability with a second adjustment coefficient based on the comparison result that the reverse difference moment is less than the preset reverse difference moment;

[0128] Determine not to adjust the preset reliability based on the comparison result that the reverse difference moment is greater than or equal to the preset reverse difference moment.

[0129] Specifically, the value range of the preset reverse difference moment is set to [0.1, 0.7], and preferably 0.5 in the embodiments of the present invention; the reverse difference moment reflects the texture uniformity of the defect surface, and a small reverse difference moment indicates inaccurate division of the defect range.

[0130] Specifically, the value range of the second adjustment coefficient is set to [1.01, 1.04], and preferably 1.03 in the embodiments of the present invention.

[0131] Specifically, for defects with qualified recognition accuracy, determine the three-dimensional morphology of the defect according to the gray gradient, and determine the three-dimensional morphology of the material surface with the defect as the reference according to the gray gradient.

[0132] Specifically, the gray gradient represents the physical depth of the defect, and the formula for determining the three-dimensional morphology of the image using the gray gradient is as follows:

[0133]

[0134] Among them, (u, v) represents the pixel coordinates, f x and f y represent the focal lengths, c x and c y represent the optical center coordinates, Z represents the gray gradient, (X c , Y c , Z c ) represents the camera three-dimensional coordinates corresponding to the pixel coordinates.

[0135] Substituting the camera three-dimensional coordinates into the following formula can obtain the world coordinates:

[0136]

[0137] Among them, X w represents the X coordinate of the world coordinates corresponding to the pixel coordinates, Y w represents the Y coordinate of the world coordinates corresponding to the pixel coordinates, Z w represents the Z coordinate of the world coordinates corresponding to the pixel coordinates, and t represents the position of the camera in the world coordinate system.

[0138] Specifically, the pixel coordinates are determined with the upper left corner of the image information as the origin, and the optical center coordinates are parameters inherent to the camera itself. It can be understood that the cameras used at all shooting positions are cameras of the same specification, and the model and parameters of any camera are not specifically limited, as long as they can meet the shooting requirements.

[0139] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A surface micro-topography measurement method based on gray depth, characterized in that, Including: Obtain the reflectance of the material surface to determine the number of shooting points based on the reflectance, and symmetrically set the shooting points with the vertical line passing through the center point of the material surface as the axis; Obtain the image information of a single shooting point and preprocess the image information to determine the gray level gradient of the symmetric shooting point for the same pixel block, and determine whether the pixel block has a defect according to the gray level gradient; Taking the pixel block with a defect as the center, determine the gray level contrast with a preset pixel distance as the spacing, and determine the defect range according to the gray level contrast; Fit the defect ranges determined by all shooting points to determine the reliability, determine whether the recognition accuracy of the defect is qualified according to the comparison result between the reliability and the preset reliability, and adjust the preset reflectance or the preset reliability according to the unqualified recognition accuracy; Based on the defects with qualified recognition accuracy, determine the surface topography of the material according to the gray level gradient.

2. The method for measuring surface micro-topography based on gray depth according to claim 1, wherein The process of determining the number of shooting points based on the reflectance includes: Compare the reflectance with the preset reflectance; Based on the comparison result that the reflectance is greater than the preset reflectance, determine that the number of shooting points is the first number; Based on the comparison result that the reflectance is less than or equal to the preset reflectance, determine that the number of shooting points is the second number.

3. The surface micro-topography measurement method based on gray depth according to claim 2, wherein The process of determining whether the pixel block has a defect according to the gray level gradient includes: Compare the gray level gradient with the preset gray level gradient; Based on the comparison result that the gray level gradient is greater than the preset gray level gradient, determine that the pixel block has a defect.

4. The surface micro-topography measurement method based on gray depth according to claim 3, characterized in that The process of determining the defect range includes: Taking the pixel block with a defect as the center, determine the gray level contrast with a preset pixel distance as the spacing; Compare the gray level contrast with the preset contrast; Based on the comparison result that the gray level contrast is greater than or equal to the preset contrast, determine that the preset pixel distance range is the defect range; Based on the comparison result that the gray level contrast is less than the preset contrast, determine to increase the preset pixel distance by a preset amplitude to obtain the second pixel distance; Taking the pixel block with a defect as the center, determine the second gray level contrast with the second pixel distance; Compare the second gray level contrast with the preset contrast until the second gray level contrast is greater than the preset contrast, and determine the second pixel distance range as the defect range.

5. The method for measuring surface micro-topography based on gray depth according to claim 4, characterized in that The process of determining the reliability includes: Taking any one of the defect ranges as a reference, fit the remaining defect ranges to determine the fitting degree; Compare the fitting degree with the preset fitting degree; Based on the comparison result that the fitting degree is greater than or equal to the preset fitting degree, determine that the defect ranges coincide; Determine the comparison between the number of overlapping defect ranges and the number of all defect ranges as the reliability.

6. The method for measuring surface microtopography based on gray depth according to claim 5, characterized in that, The process of determining whether the recognition accuracy of the defect is qualified based on the reliability includes: Compare the reliability with the preset reliability; Based on the comparison result that the reliability is greater than or equal to the preset reliability, determine that the recognition accuracy of the defect is qualified; Based on the comparison result that the reliability is less than the preset reliability, determine that the recognition accuracy of the defect is unqualified.

7. The method for measuring surface micro-topography based on gray depth according to claim 6, wherein The process of determining the adjustment parameters according to the recognition accuracy of non-conformities includes: Subtracting the preset reliability from the reliability to obtain a difference percentage; Determining to increase the preset reflectance rate based on the comparison result that the difference percentage is greater than the preset percentage; Determining to increase the preset reliability based on the comparison result that the difference percentage is less than or equal to the preset percentage.

8. The method for measuring surface micro-topography based on gray depth according to claim 7, wherein The process of adjusting the preset reflectance rate includes: Determining the gray entropy of the defect range and comparing the gray entropy with a preset entropy; Determining to increase the preset reflectance rate by a first adjustment coefficient based on the comparison result that the gray entropy is greater than the preset entropy.

9. The method for measuring surface micro-topography based on gray depth according to claim 7, wherein The process of adjusting the preset reliability includes: Determining the inverse difference moment of the defect range and comparing the inverse difference moment with a preset inverse difference moment; Determining to increase the preset reliability by a second adjustment coefficient based on the comparison result that the inverse difference moment is less than the preset inverse difference moment.

10. The method for measuring surface micro-topography based on gray depth according to claim 9, characterized in that, For defects with qualified recognition accuracy, determine the three-dimensional morphology of the defect according to the gray gradient, and determine the three-dimensional morphology of the material surface according to the gray gradient with the defect as the reference.

Images