CN112557400B - System and method for detecting surface defect contour of lens of satellite telescope - Google Patents

System and method for detecting surface defect contour of lens of satellite telescope Download PDF

Info

Publication number
CN112557400B
CN112557400B CN202011373483.1A CN202011373483A CN112557400B CN 112557400 B CN112557400 B CN 112557400B CN 202011373483 A CN202011373483 A CN 202011373483A CN 112557400 B CN112557400 B CN 112557400B
Authority
CN
China
Prior art keywords
lens
pixel
contour
image
satellite telescope
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011373483.1A
Other languages
Chinese (zh)
Other versions
CN112557400A (en
Inventor
殷春
陈薇
程玉华
冯怡婷
张博
陈凯
张阔
苟轩
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN202011373483.1A priority Critical patent/CN112557400B/en
Publication of CN112557400A publication Critical patent/CN112557400A/en
Application granted granted Critical
Publication of CN112557400B publication Critical patent/CN112557400B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/958—Inspecting transparent materials or objects, e.g. windscreens
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques

Abstract

The invention discloses a system and a method for detecting surface defect contours of lens of satellite telescope. The position of a direct light source is determined through the first sliding assembly and the vertical calibrator, the height of the scale slide rail is adjusted, the measured satellite telescope lens can be completely irradiated, the portable handheld camera shoots the measured satellite telescope lens at a certain angle, the shot image (optical defect image) is transmitted to the computer through the data line, the outline is extracted, no size constraint exists, the system is simple to assemble and convenient to carry, and the equipment cost is greatly saved. Adding a weight item to a traditional clustering function to optimize the function, and then performing pixel point classification processing; and extracting a rough defect outline by adopting an edge detection algorithm, namely optimizing the edge outline, and further refining the outline by using discrete Chebyshev polynomial fitting sub-pixel points, so that the fitting precision is improved, and the defect detection precision is improved, thereby facilitating the subsequent quantitative analysis of the defects of the satellite telescope lens.

Description

System and method for detecting surface defect contour of lens of satellite telescope
Technical Field
The invention belongs to the technical field of surface defect detection, and particularly relates to a system and a method for detecting a surface defect contour of a lens of a satellite telescope.
Background
Ultra-precise optical elements are an important component of many high-precision instruments and equipment systems. In the field of aerospace, a large number of optical elements are used in satellites, most notably satellite telescopes, and are typically on the order of meters in diameter. For the satellite, the main functions of the satellite are shooting, reconnaissance and monitoring on the ground, so that the space telescope used by the satellite is required to have high imaging sensitivity, high precision and strong resolving power. The satellite telescope is in atmospheric environment, and gas can not cause the influence to the shooting process. However, in space, due to the low temperature, gases (such as hydrogen and methane) in the astral cloud can be solidified into particles and attached to the lens to form surface defects. To prevent gas-solidified particles from adhering to the lens, an adsorption device is typically added near the lens. Similarly, in a processing or simulation experiment, scratches caused by external objects, impacts of the external environment and improper subsequent operation treatment inevitably leave various surface defects such as pits, cracks, scratches, bubbles, broken edges and the like on the surface. For a satellite telescope lens, the existence of surface defects such as surface scratches can cause scattering of light beams incident to the surface, and the size of the surface defects is small, so that the surface of an element is damaged due to a severe diffraction phenomenon, the use efficiency of the element is influenced, and the element is even scrapped. Therefore, the satellite usually performs a space environment simulation experiment on the ground before transmitting so as to ensure that the shooting function of the satellite telescope normally operates in space.
In order to verify the efficacy of the adsorption device or to detect the loss of the lens during the manufacturing process, defect detection is required. Although the traditional precision system for detecting the surface defects has high detection precision, the equipment is complex to assemble and high in cost, the position relation between parts, the motion condition and the like have strict requirements, and if the specific posture is changed, the directional indication is correspondingly changed, so that an operator is required to have a certain optical field knowledge base. The biggest defect of the precision system is that the size of an optical element is limited, and a measured object is usually in the centimeter or decimeter order, so that the in-situ non-contact defect detection cannot be carried out on a large-size optical device.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a system and a method for detecting the surface defect outline of a satellite telescope lens, so as to realize the in-situ non-contact defect detection of the large-size satellite telescope lens, improve the detection precision, reduce the structural complexity and the cost of the detection system and ensure that the detection system is convenient to carry.
In order to achieve the above object, the present invention provides a system for detecting surface defect contours of a lens of a satellite telescope, comprising:
a scale slide rail;
the vertical calibrator is connected to the scale slide rail through the first sliding assembly, and the first sliding assembly can move on the scale slide rail;
the direct light source is connected to the scale slide rail through the second sliding assembly, and the second sliding assembly can move on the scale slide rail;
portable handheld cameras and computers;
opening the vertical calibrator, and moving the first sliding assembly to enable the laser point emitted by the vertical calibrator to irradiate the center of the lens of the tested satellite telescope which is horizontally placed;
moving a second sliding assembly, moving a direct light source to the position of the vertical calibrator, and enabling the center of the direct light source and the center of the lens of the tested satellite telescope to be on the same axis;
adjusting the height of the scale slide rail to enable the measured satellite telescope lens to be completely irradiated by a direct light source;
the portable handheld camera is arranged below the direct light source and is higher than the lens of the satellite telescope to be measured, and the lens of the satellite telescope to be measured is shot at a certain angle, so that the shooting area of the portable handheld camera can cover the whole lens of the satellite telescope to be measured; the portable handheld camera transmits the shot image (optical defect image) to the computer through a data line;
the computer receives the transmitted image, extracts the outline of the defect on the surface of the lens of the satellite telescope by using image processing, and displays the specific outline characteristic information of the defect.
The invention discloses a method for detecting the surface defect outline of a lens of a satellite telescope, which is characterized by comprising the following steps:
(1) shooting the lens of the tested satellite telescope to obtain an optical defect image;
(2) pixel point classification
2.1), setting an iteration threshold epsilon, a class number G, an adjusting parameter m, and setting an initialization iteration number k to be 0;
2.2) constructing an optimized clustering objective function JFCM：
Figure BDA0002807507430000031
Wherein, Wxy，iAs a weight, Wxy，i＝(Mxy，i×Gxy，i)/Zxy，Mxy，iIs a coefficient of membership, Gxy，iIs the coefficient of intensity of light, ZxyNormalized constant, whose value is:
Figure BDA0002807507430000032
Mxy，i＝exp(-(u(x-1，y)，i×u(x+1，y)，i+u(x，y-1)，i×u(x，y+1)，i))，u(x-1，y)，i、u(x+1，y)，i、u(x，y1)，iu(x，y+1)，ithe pixel values of the pixel points (x-1, y), (x +1, y), (x, y-1) and (x, y +1) belong to the membership grade of the category i;
Gxy，i＝1-exp(-Ixy×gi)1/i，
Figure BDA0002807507430000033
Nxyset of neighborhood pixels, p, of pixel (x, y)rIs the pixel value, I, of the r-th pixel in the neighborhood set of pixelsxyIs used to measure the pixel value of the pixel neighborhood, if the pixel neighborhood has high pixel value, then prWill become high while IxyWill become smaller, and IxyWill also makeTo obtain Gxy，iThe size is reduced; a isiThe average pixel value of the ith type pixel point is obtained;
wherein u isxy，iIs the pixel value p of the pixel point (x, y)xyMembership degrees belonging to the i-th class, wherein x and y are horizontal and vertical coordinates of the pixel points respectively; h is the height of the optical defect image, and L is the width of the optical defect image; c. CiThe cluster center of the pixel value of the ith type pixel point is obtained;
2.3) in
Figure BDA0002807507430000034
Under the constraint condition of (1), initializing a membership degree u by using a random number with a value within a range of 0,1xy，iCalculating the clustering center ci：
Figure BDA0002807507430000035
2.4) calculating a clustering objective function J according to the formula (1) in the step 2.2)FCMAnd is represented by JFCM(0)，k＝k+1；
2.5) calculating membership degree uxy，i：
Figure BDA0002807507430000041
2.6) calculating and calculating the clustering center c according to the formula (2) of the step 2.3)i；
2.7) calculating a clustering objective function J according to the formula (1) in the step 2.2)FCMAnd is represented by JFCM(k) And judging whether the clustering is terminated:
if the obtained clustering objective function J is calculatedFCM(k) Clustering objective function J with last iterationFCM(k-1) the difference is less than or equal to a set iteration threshold epsilon, namely | | JFCM(k)-JFCM(k-1) if | | | is less than or equal to epsilon, finishing clustering, otherwise, skipping to the step 2.5);
2.8) degree of membership u according to the criterion of maximum degree of membershipxy，iThe pixel points of the optical defect image are classified,i.e. in class G, degree of membership uxy，iThe largest category is the category of the pixel point (x, y); removing the pixel points belonging to the background category (setting the pixel value to be 0) to obtain an image I representing the edge contour information*The pixel points of other categories are contour points, the number of the contour points is Q, and the coordinate is (x)j，yj)，j＝1，2，...，Q；
(3) Edge profile optimization
3.1) image I with Gaussian Filtering*Carrying out smoothing treatment;
3.2) computing images by Sobel operator
Figure BDA0002807507430000042
Gradient magnitude and gradient direction of (c):
using Sobel horizontal operator SobelxVertical operator SobelyRespectively associated with the images
Figure BDA0002807507430000043
Performing convolution operation to obtain a difference value h in the x direction and the y directionxyDifferential value vxyWherein:
Figure BDA0002807507430000044
Figure BDA0002807507430000045
further images can be obtained
Figure BDA0002807507430000046
Gradient magnitude F (x, y) and gradient direction ω (x, y):
Figure BDA0002807507430000047
3.3) according to the gradient direction, carrying out non-maximum suppression treatment on the gradient amplitude
For image I*The contour point (x)j，yj) Substituting the formula (4) to obtain the gradient direction omega (x)j，yj) Gradient amplitude F (x)j，yj) If the contour point (x)j，yj) Gradient amplitude F (x)j，yj) With the gradient direction omega (x) corresponding theretoj，yj) If the contour point (x) is compared with the gradient amplitudes of two adjacent pixel pointsj，yj) Gradient amplitude F (x)j，yj) If it is maximum, the contour point (x) is retainedj，yj) Otherwise, setting the pixel value of the contour point to 0, thus inhibiting the pixel points with insufficient gradient amplitude, only keeping the R contour points with maximum gradient amplitude, and obtaining the optimized edge contour information image
Figure BDA0002807507430000051
(4) Contour refinement fitting
According to the R contour points reserved in the step (3), fitting R sub-pixel points by adopting a discrete Chebyshev polynomial (one contour point fits one sub-pixel point in a set fitting region);
and performing curve fitting on the R contour points and the R sub-pixel points to obtain a contour which is refined and close to the defect image.
The invention aims to realize the following steps:
aiming at the in-situ non-contact defect detection of the lens of the satellite telescope, the system for detecting the defect contour of the surface of the lens of the satellite telescope comprises a scale slide rail, a first sliding assembly, a vertical calibrator, a second sliding assembly, a direct light source, a portable handheld camera and a computer. The position of a direct light source is determined through the first sliding assembly and the vertical calibrator, the centers of the direct light source and the measured satellite telescope lens are ensured to be on the same axis, and the height of the scale slide rail is adjusted, so that the measured satellite telescope lens can be completely irradiated; the portable handheld camera is arranged below the direct light source and is higher than the lens of the satellite telescope to be measured, and the lens of the satellite telescope to be measured is shot at a certain angle, so that the shooting area of the portable handheld camera can cover the whole lens of the satellite telescope to be measured; the portable handheld camera transmits the shot image (optical defect image) to the computer through a data line; the computer receives the transmitted image, extracts the outline of the defects on the surface of the lens of the satellite telescope by using image processing, and displays the specific outline characteristic information, thereby realizing the in-situ non-contact defect detection of the lens of the satellite telescope. The system for detecting the surface defect outline of the lens of the satellite telescope has no restriction on the size of the lens of the satellite telescope to be detected, is simple to assemble and convenient to carry, greatly saves equipment cost, and is less influenced by fields and environments than a precision system. The acquired defect image is processed by a computer, and an image processing algorithm is used for extracting a defect outline, so that the final detection precision is higher than that of a traditional precision system. The acquisition of the defect image is not as strict as that of a precise system, and the direct light source cannot ensure that the defect image is completely and uniformly irradiated on the satellite telescope lens during acquisition, so that the defect image has errors due to the factor, and the surface defect outline detection is negatively influenced. Before the traditional edge detection algorithm is applied to extract the contour, a weight term is added to a traditional clustering function to optimize the function, and the optimized clustering algorithm is used for carrying out pixel point classification on an optical defect image, so that the influence of illumination intensity nonuniformity on the intensity of the shot optical defect image is inhibited, and the defect detection precision is ensured. And extracting the rough defect outline, namely optimizing the edge outline, of the image after the classification processing by adopting an edge detection algorithm. In the traditional image processing method, Canny operators are mostly selected for contour extraction, but when double-threshold fitting is carried out, the fitting precision is often influenced by artificially setting upper and lower thresholds. In addition, in view of the fact that the device of the shooting part of the optical defect image is simple and cannot calibrate the detected satellite telescope lens, the discrete Chebyshev polynomial fitting sub-pixel points further refine the profile, improve the fitting precision and better represent the profile characteristic information. Therefore, the damage characteristics of the defects on the surface of the lens of the satellite telescope are enhanced, the profile characteristic information of the lens of the satellite telescope is easier to show, and the defect detection precision is improved, so that the defects of the lens of the satellite telescope can be quantitatively analyzed in the following process.
The related advantages and innovations of the invention are as follows:
(1) the invention considers the illumination intensity information G in the classification of the pixel pointsxy,iTo suppress the influence of the uneven intensity on the segmentation;
(2) originally, each pixel point is isolated, but the neighborhood condition of the pixel point is considered in the invention, and the membership function value u of the pixel point is calculatedxy,iThe spatial information of the defects is increased;
(3) adding a weight term W in the objective function based on the information of the defects and the membership degreexy,iTo enhance the intensity information characteristics of the optical defect images;
(4) and the discrete Chebyshev polynomial calculates the sub-pixel points according to the contour points, and the condition between two adjacent pixel points is considered, so that the fitting precision of the contour is improved to a great extent.
Drawings
FIG. 1 is a schematic structural diagram of an embodiment of a system for detecting surface defect contours of lens of a satellite telescope according to the present invention;
FIG. 2 is a detailed flowchart of the operation of the system for detecting the surface defect profile of the lens of the satellite telescope shown in FIG. 1;
FIG. 3 is a flow chart of an embodiment of the method for detecting the surface defect profile of the lens of the satellite telescope according to the invention;
FIG. 4 is a diagram illustrating gradient directions and gradient strengths calculated by a Sobel operator;
FIG. 5 is a schematic diagram of a polynomial fitting principle;
FIG. 6 is an original image of a defect on the surface of a lens of a satellite telescope in an embodiment;
FIG. 7 is an image segmented by the optimized clustering algorithm for defects in the image in an exemplary embodiment;
FIG. 8 is a defect edge point detection image in an exemplary embodiment;
FIG. 9 is an approximate outline image of a defect extracted by the edge detection algorithm in an exemplary embodiment;
figure 10 is a discrete chebyshev fit defect profile image in an exemplary embodiment.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
FIG. 1 is a schematic structural diagram of an embodiment of the system for detecting the surface defect profile of the lens of the satellite telescope according to the invention.
In this embodiment, as shown in fig. 1, the system for detecting a defect profile on a surface of a lens of a satellite telescope according to the present invention includes: scale slide rail 1, first sliding component 2, vertical calibration appearance 3, second sliding component 4, direct light source 5, portable handheld camera 7, computer 8.
The vertical alignment device 3 is connected to the scale rail 1 via the first slide assembly 2, and the first slide assembly 2 can move 1 on the scale rail. The direct light source 5 is connected to the scale slide rail 1 through the second sliding member 4, and the second sliding member 4 can move on the scale slide rail 1.
The vertical calibrator 3 is opened, and the first sliding assembly 2 is moved, so that the laser point emitted by the vertical calibrator 3 irradiates the center of the lens 6 of the tested satellite telescope which is horizontally placed. Moving the second sliding assembly 4 moves the direct light source 5 to the position of the vertical calibrator 3 so that the center of the direct light source 5 is on the same axis as the center of the measured satellite telescope lens 6. The height of the scale slide 1 is adjusted so that the measured satellite telescope mirror 6 can be completely illuminated by the direct light source 5.
The portable handheld camera 7 is higher than the measured satellite telescope lens 6 below the direct light source 5, shoots the measured satellite telescope lens 6 at a certain angle, and ensures that the shooting area of the portable handheld camera can cover the whole measured satellite telescope lens 6. The portable hand-held camera 7 transmits the photographed image (optical defect image) to the computer 8 through a data line;
the computer 8 receives the transmitted images, extracts the outline of the defects on the surface of the lens of the satellite telescope by using image processing, and displays the specific outline characteristic information, thereby realizing the in-situ non-contact defect detection of the lens of the satellite telescope. The system for detecting the surface defect outline of the lens of the satellite telescope has no restriction on the size of the lens of the satellite telescope to be detected, is simple to assemble and convenient to carry, greatly saves equipment cost, and is less influenced by fields and environments than a precision system.
FIG. 2 is a detailed operation flow chart of the system for detecting the surface defect profile of the lens of the satellite telescope shown in FIG. 1.
In this embodiment, the specific workflow of the system for detecting the surface defect profile of the lens of the satellite telescope is as follows:
step 1: the measured surface of the satellite telescope lens 6 is upward, the vertical calibrator 3 is opened, the focal length is adjusted to rotate 90 degrees, 180 degrees and 270 degrees, the middle point of the four points is selected, the laser point irradiates the geometric center of the satellite telescope lens 6 which is horizontally arranged,
step 2: and adjusting the position of the sliding component 4 on the scale slide rail 1, and moving the direct light source 5 to the position of the vertical calibrator 3, so that the center of the direct light source 5 and the center of the satellite telescope lens 6 are on the same axis.
And step 3: adjusting the height of the scale slide rail 1 to enable the tested satellite telescope lens 6 to be completely irradiated by the direct light source 5;
and 4, step 4: the focal length of the portable handheld camera 7 is adjusted, the portable handheld camera 7 is positioned at the lower right part of the direct light source 5 and is higher than the measured satellite telescope lens 6, the portable handheld camera 7 keeps a certain angle, the portable handheld camera is focused on the measured surface of the measured satellite telescope lens 6, and the surface information of the measured satellite telescope lens 6 can be completely captured;
and 5: and shooting the optical defect images of the satellite telescope lens 6, transmitting the images to the computer 8 in real time, and selecting the optical defect image with the best shooting effect to process by using an algorithm.
FIG. 3 is a flow chart of an embodiment of the method for detecting the surface defect contour of the lens of the satellite telescope.
In this embodiment, as shown in fig. 3, the method for detecting the surface defect profile of the lens of the satellite telescope of the present invention includes:
step S1: shooting a lens of a tested satellite telescope to obtain an optical defect image;
step S2: pixel point classification
Step S2.1: setting an iteration threshold epsilon, a class number G and an adjustment parameter m, and initializing the iteration number k to be 0;
step S2.2: constructing an optimized clustering objective function JFCM：
Figure BDA0002807507430000081
Wherein, Wxy,iThe weight is the embodiment of membership degree information and illumination intensity information, and the value is as follows: wxy,i＝(Mxy,i×Gxy,i)/Zxy，Mxy,iIs a coefficient of membership, Gxy,iIs the coefficient of intensity of light, ZxyNormalized constant, whose value is:
Figure BDA0002807507430000091
Mxy,i＝exp(-(u(x-1,y),i×u(x+1,y),i+u(x,y-1),i×u(x,y+1),i))，u(x-1,y),i、u(x+1,y),i、u(x,y-1),iu(x,y+1),imembership degrees of the pixel values of the neighborhood pixel points (x-1, y), (x +1, y), (x, y-1) and (x, y +1) belonging to the category i respectively; obviously, when the membership degree of the neighborhood pixel point is increased, the membership degree coefficient M of the pixel point (x, y)xy,iWill be smaller because e-xIs a decreasing function.
The defects of the lens of the satellite telescope have high illumination intensity coefficient Gxy,iThe light intensity factor G, which is also an important prerequisite for defect detection by capturing imagesxy,iComprises the following steps:
Gxy,i＝1-exp(-Ixy×gi)1/i，
Figure BDA0002807507430000092
wherein N isxySet of neighborhood pixels, p, of pixel (x, y)rIs the pixel value, I, of the r-th pixel in the neighborhood set of pixelsxyIs used to measure the pixel value of the pixel neighborhood, if the pixel neighborhood has high pixel value, then prWill become high while IxyWill become smaller, and IxyWill also be Gxy,iThe size is reduced; a isiRepresenting the average pixel value, a, in the i-th class of pixelsiThe purpose of the method is to utilize the characteristic information of all pixel points in one classification to analyze in order to inhibit the nonuniformity of the illumination intensity;
wherein u isxy,iIs the pixel value p of the pixel point (x, y)xyMembership degrees belonging to the i-th class, wherein x and y are horizontal and vertical coordinates of the pixel points respectively; h is the height of the optical defect image, and L is the width of the optical defect image; c. CiAnd the cluster center of the pixel value of the ith type pixel point is obtained.
Step S2.3: in that
Figure BDA0002807507430000093
Under the constraint condition of (1), initializing a membership degree u by using a random number with a value within a range of 0,1xy,iAnd thus the position of the initialized cluster center is obtained.
The following objective function is solved using the lagrange multiplier method:
Figure BDA0002807507430000094
for the input variable of equation (2-1), i.e. the cluster center ciThe necessary condition for minimizing the equation (1) by calculating the partial derivatives is:
Figure BDA0002807507430000095
the calculation formula of the solvable clustering center is as follows:
Figure BDA0002807507430000101
step S2.4: calculating a clustering objective function J according to the formula (1) in the step S2.2FCMAnd is represented by JFCM(0)，k＝k+1；
Step S2.5: the following objective function is solved using the lagrange multiplier method:
Figure BDA0002807507430000102
wherein in the right item of formula (2-1)
Figure BDA0002807507430000103
For constrained Lagrange multiplier terms, i.e. degree of membership u to the input variablexy,iThe necessary condition for minimizing the equation (1) by calculating the partial derivatives is:
Figure BDA0002807507430000104
lagrange multiplier term lambda for input variables simultaneouslyxyThe derivation comprises:
Figure BDA0002807507430000105
the membership calculation formula can be solved according to the formulas (3-1) and (3-2) as follows:
Figure BDA0002807507430000106
step S2.6: calculating and calculating the clustering center c according to the formula (2) of the step S2.3i；
Step S2.7: calculating a clustering objective function J according to the formula (1) in the step S2.2FCMAnd is represented by JFCM(k) And judging whether the clustering is terminated:
if the obtained clustering objective function J is calculatedFCM(k) Clustering objective function J with last iterationFCM(k-1) the difference is less than or equal to a set iteration threshold epsilon, namely | | JFCM(k)-JFCMAnd (k-1) if | | | is less than or equal to epsilon, finishing clustering, otherwise, skipping to the step S2.5.
Step S2.8: according to the maximum membership criterion and the membership uxy，iClassifying the pixel points of the optical defect image, namely in G class, the membership uxy，iThe largest category is the category of the pixel point (x, y);
removing the pixel points belonging to the background category (setting the pixel value to be 0) to obtain an image I representing the edge contour information*The pixel points of other categories are contour points, the number of the contour points is Q, and the coordinate is (x)j，yj)，j＝1，2，...，Q；
Step S3: edge profile optimization
Step S3.1: using Gaussian filtering on the image I*Carrying out smoothing treatment;
step S3.2: computing images using Sobel operators
Figure BDA0002807507430000111
Gradient magnitude and gradient direction of (c):
using Sobel horizontal operator SobelxVertical operator SobelyRespectively associated with the images
Figure BDA0002807507430000112
Performing convolution operation to obtain a difference value h in the x direction and the y directionxyDifferential value vxyWherein:
Figure BDA0002807507430000113
Figure BDA0002807507430000114
further images can be obtained
Figure BDA0002807507430000115
Gradient magnitude F (x, y) and gradient direction ω (x, y):
Figure BDA0002807507430000116
step S3.3: according to the gradient direction, carrying out non-maximum suppression treatment on the gradient amplitude
In the present embodiment, as shown in fig. 4, for an image I*The contour point (x)j，yj) Substituting the formula (4) to obtain the gradient direction omega (x)j，yj) Gradient amplitude F (x)j，yj) If the contour point (x)j，yj) Gradient amplitude F (x)j，yj) With the gradient direction omega (x) corresponding theretoj，yj) If the contour point (x) is compared with the gradient amplitudes of two adjacent pixel pointsj，yj) Gradient amplitude F (x)j，yj) If it is maximum, the contour point (x) is retainedj，yj) Otherwise, setting the pixel value of the contour point to 0, thus inhibiting the pixel points with insufficient gradient amplitude, only keeping the R contour points with maximum gradient amplitude, and obtaining the optimized edge contour information image
Figure BDA0002807507430000117
Step S4: contour refinement fitting
In this embodiment, as shown in fig. 5, according to the R contour points retained in step S3, R sub-pixel points are fitted by using a discrete chebyshev polynomial (one contour point fits one sub-pixel point in a region for which fitting is set);
and performing curve fitting on the R contour points and the R sub-pixel points to obtain a contour which is refined and close to the defect image.
According to the method for detecting the defect contour on the surface of the lens of the satellite telescope, the collected image is subjected to pixel point classification by using the optimized clustering algorithm, the influence of illumination intensity nonuniformity on the intensity of the shot optical defect image is inhibited, the defect contour is roughly outlined by using the edge detection algorithm, and the defect contour is subjected to refined fitting by using the discrete Chebyshev polynomial, so that the fitting precision is improved, and the contour characteristic information is better represented. Therefore, the damage characteristics of the defects on the surface of the lens of the satellite telescope are enhanced, the profile characteristic information of the lens of the satellite telescope is easier to show, and the defect detection precision is improved, so that the defects of the lens of the satellite telescope can be quantitatively analyzed in the following process.
Examples of the invention
In this example, there is a scratch defect on the surface of the lens of the satellite telescope, a scratch is formed at the upper left edge position in fig. 6, and particles formed by gas solidification are attached to the lens in a random scattered distribution. The system for detecting the surface defect outline of the lens of the satellite telescope shown in the figure 1 is used for collecting the surface defect image of the lens of the satellite telescope, and the vertical calibrator is opened to enable the laser point to irradiate the geometric center of the lens to be tested which is horizontally placed, so that the vertical calibrator is ensured to be vertical to the lens of the satellite telescope to be tested. And adjusting the position of the sliding component on the scale slide rail, and moving the direct light source to the position of the vertical calibrator to enable the center of the direct light source and the center of the measured lens to be on the same axis. The height of adjusting the scale slide rail makes the measured lens can be irradiated by the direct light source completely, and portable handheld camera is higher than the measured lens in the lower right side department of direct light source, is certain angle and shoots the image. And classifying the defects in the collected original image by using an optimized clustering algorithm to obtain an image shown in FIG. 7, wherein the clustering algorithm can distinguish background information and defect outline information in the original image. The classified images are subjected to edge point detection to obtain an image shown in fig. 8, wherein scratch damage, gas solidified particles and edges of the lens are detected, and then the gas solidified particles in the image are partially marked. The defect contour edges are extracted by using an edge detection algorithm to obtain an image shown in fig. 9, and it can be seen that the conventional edge detection algorithm does not completely detect the marked gas solidified particles, and the integrity of the fitted contour information cannot be guaranteed because part of defect contour information is lost. And finally, performing thinning fitting on the profile information image by adopting a discrete Chebyshev polynomial to obtain a scratch damage image as shown in FIG. 10. According to the graph 10, the defect contour obtained by fitting is clear, and meanwhile, the defect contour which is not fitted by the edge detection algorithm is further subjected to thinning fitting by applying polynomial interpolation, so that the integrity of the information of the fitted defect contour is ensured. The defect image obtained by the invention has clear characteristics, and realizes the visual presentation of defects.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (2)

1. A system for detecting surface defect contours of lens of satellite telescope is characterized by comprising:
a scale slide rail;
the vertical calibrator is connected to the scale slide rail through the first sliding assembly, and the first sliding assembly can move on the scale slide rail;
the direct light source is connected to the scale slide rail through the second sliding assembly, and the second sliding assembly can move on the scale slide rail;
portable handheld cameras and computers;
opening the vertical calibrator, and moving the first sliding assembly to enable the laser point emitted by the vertical calibrator to irradiate the center of the lens of the tested satellite telescope which is horizontally placed;
moving a second sliding assembly, moving a direct light source to the position of the vertical calibrator, and enabling the center of the direct light source and the center of the lens of the tested satellite telescope to be on the same axis;
adjusting the height of the scale slide rail to enable the measured satellite telescope lens to be completely irradiated by a direct light source;
the portable handheld camera is arranged below the direct light source and is higher than the lens of the satellite telescope to be measured, and the lens of the satellite telescope to be measured is shot at a certain angle, so that the shooting area of the portable handheld camera can cover the whole lens of the satellite telescope to be measured; the portable handheld camera transmits the shot image to a computer through a data line;
the computer receives the transmitted image, extracts the outline of the defect on the surface of the lens of the satellite telescope by using image processing, and displays the specific outline characteristic information of the defect.
2. A method for detecting the surface defect contour of a lens of a satellite telescope is characterized by comprising the following steps:
(1) shooting the lens of the tested satellite telescope to obtain an optical defect image;
(2) pixel point classification
2.1), setting an iteration threshold epsilon, a class number G, an adjusting parameter m, and setting an initialization iteration number k to be 0;
2.2) constructing an optimized clustering objective function JFCM：
Figure FDA0003217689590000011
Wherein, Wxy，iAs a weight, Wxy，i＝(Mxy，i×Gxy，i)/Zxy，Mxy，iIs a coefficient of membership, Gxy,iIs the coefficient of intensity of light, ZxyNormalized constant, whose value is:
Figure FDA0003217689590000012
Mxy，i＝exp(-(u(x-1，y)，i×u(x+1，y)，i+u(x，y-1)，i×u(x，y+1)，i))，u(x-1，y)，i、u(x+1，y)，i、u(x，y-1)，iu(x，y+1)，ithe pixel values of the pixel points (x-1, y), (x +1, y), (x, y-1) and (x, y +1) belong to the membership grade of the category i;
Figure FDA0003217689590000021
Nxyset of neighborhood pixels, p, of pixel (x, y)rIs the pixel value, I, of the r-th pixel in the neighborhood set of pixelsxyIs used to measure the pixel value of the pixel neighborhood, if the pixel neighborhood has high pixel value, then prWill become high while IxyWill become smaller, and IxyWill also be Gxy,iThe size is reduced; a isiThe average pixel value of the ith type pixel point is obtained;
wherein u isxy，iIs the pixel value p of the pixel point (x, y)xyMembership degrees belonging to the i-th class, wherein x and y are horizontal and vertical coordinates of the pixel points respectively; h is the height of the optical defect image, and L is the width of the optical defect image; c. CiThe cluster center of the pixel value of the ith type pixel point is obtained;
2.3) in
Figure FDA0003217689590000022
Under the constraint condition of (1), initializing a membership degree u by using a random number with a value within a range of 0,1xy，iCalculating the clustering center ci：
Figure FDA0003217689590000023
2.4) calculating a clustering objective function J according to the formula (1) in the step 2.2)FCMAnd is represented by JFCM(0)，k＝k+1：
2.5) calculating membership degree uxy，i：
Figure FDA0003217689590000024
2.6) calculating the clustering center c according to the formula (2) of the step 2.3)i；
2.7) calculating a clustering objective function J according to the formula (1) in the step 2.2)FCMAnd is represented by JFCM(k) And judging whether the clustering is terminated:
if the obtained clustering objective function J is calculatedFCM(k) Clustering objective function J with last iterationFCM(k-1) the difference is less than or equal to a set iteration threshold epsilon, namely | | JFCM(k)-JFCM(k-1) if | | | is less than or equal to epsilon, finishing clustering, otherwise, skipping to the step 2.5);
2.8) degree of membership u according to the criterion of maximum degree of membershipxy，iClassifying the pixel points of the optical defect image, namely in G class, the membership uxy，iThe largest category is the category of the pixel point (x, y); removing the pixel points belonging to the background category to obtain an image I representing edge profile information*The pixel points of other categories are contour points, the number of the contour points is Q, and the coordinate is (x)j，yj)，j＝1，2，...，Q；
(3) Edge profile optimization
3.1) image I with Gaussian Filtering*Carrying out smoothing treatment;
3.2) computing images by Sobel operator
Figure FDA0003217689590000035
Gradient magnitude and gradient direction of (c):
using Sobel horizontal operator SobelxVertical operator SobelyRespectively carrying out convolution operation with the image I to obtain difference values h in the x direction and the y directionxyDifferential value vxyWherein:
Figure FDA0003217689590000031
Figure FDA0003217689590000032
further images can be obtained
Figure FDA0003217689590000033
Gradient magnitude F (x, y) and gradient direction ω (x, y):
Figure FDA0003217689590000034
3.3) according to the gradient direction, carrying out non-maximum suppression treatment on the gradient amplitude
For image I*The contour point (x)j，yj) Substituting the formula (4) to obtain the gradient direction omega (x)j，yj) Gradient amplitude F (x)j，yj) If the contour point (x)j，yj) Gradient amplitude F (x)j，yj) With the gradient direction omega (x) corresponding theretoj，yj) If the contour point (x) is compared with the gradient amplitudes of two adjacent pixel pointsj，yj) Gradient amplitude F (x)j，yj) If it is maximum, the contour point (x) is retainedj，yj) Otherwise, setting the pixel value of the contour point to 0, thus inhibiting the pixel points with insufficient gradient amplitude, only keeping the R contour points with maximum gradient amplitude, and obtaining the optimized edge contour information image
Figure FDA0003217689590000041
(4) Contour refinement fitting
Fitting R sub-pixel points by adopting a discrete Chebyshev polynomial according to the R contour points reserved in the step (3);
and performing curve fitting on the R contour points and the R sub-pixel points to obtain a contour which is refined and close to the defect image.
CN202011373483.1A 2020-11-30 2020-11-30 System and method for detecting surface defect contour of lens of satellite telescope Active CN112557400B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011373483.1A CN112557400B (en) 2020-11-30 2020-11-30 System and method for detecting surface defect contour of lens of satellite telescope

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011373483.1A CN112557400B (en) 2020-11-30 2020-11-30 System and method for detecting surface defect contour of lens of satellite telescope

Publications (2)

Publication Number Publication Date
CN112557400A CN112557400A (en) 2021-03-26
CN112557400B true CN112557400B (en) 2021-10-22

Family

ID=75045390

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011373483.1A Active CN112557400B (en) 2020-11-30 2020-11-30 System and method for detecting surface defect contour of lens of satellite telescope

Country Status (1)

Country Link
CN (1) CN112557400B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113470056B (en) * 2021-09-06 2021-11-16 成都新西旺自动化科技有限公司 Sub-pixel edge point detection method based on Gaussian model convolution

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101046624A (en) * 2006-03-31 2007-10-03 Hoya株式会社 Pattern defect detection device, pattern defect detection method and manufacturing method for photomask
CN101196680A (en) * 2006-12-05 2008-06-11 Hoya株式会社 Detecting device, detecting method, manufacturing method and pattern transferring method for photomask
TW201011370A (en) * 2008-06-05 2010-03-16 Coopervision Int Holding Co Lp Multi-imaging automated inspection methods and systems for wet ophthalmic lenses

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9047657B2 (en) * 2011-05-17 2015-06-02 Gii Acquisition, Lcc Method and system for optically inspecting outer peripheral surfaces of parts
CN102735695B (en) * 2012-06-04 2014-07-09 华中科技大学 Rapid lens flaw detection method and apparatus thereof
CN203117114U (en) * 2013-01-31 2013-08-07 长安大学 Precision-adjustable detecting device for notch grooves of cement pavement
CN103743761B (en) * 2013-12-31 2017-06-23 江苏大学附属医院 A kind of eyeglass watermark defect image detection device
CN104568380B (en) * 2014-12-02 2017-12-29 新昌县回山新农村建设有限公司 Camera lens positive and negative detection means and detection method
CN105067639B (en) * 2015-07-20 2018-03-27 丹阳市精通眼镜技术创新服务中心有限公司 The eyeglass defect automatic detection device and method of a kind of Grating Modulation
US10379049B2 (en) * 2015-10-15 2019-08-13 Lumos Diagnostics IP Pty Ltd Device for reading an IVD assay
CN106841237A (en) * 2017-04-18 2017-06-13 电子科技大学 A kind of electronic display glass cover plate surfaces defect detecting system and method
CN207779874U (en) * 2017-07-25 2018-08-28 杭州电子科技大学 A kind of stainless steel plate on-line measuring device based on machine vision towards the confined space
CN108918543B (en) * 2018-05-14 2021-03-23 上海工程技术大学 Dynamic detection device and method for mirror surface scratch
CN111024730A (en) * 2019-12-10 2020-04-17 福建工程学院 Glass scratch detection method and device

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101046624A (en) * 2006-03-31 2007-10-03 Hoya株式会社 Pattern defect detection device, pattern defect detection method and manufacturing method for photomask
CN101196680A (en) * 2006-12-05 2008-06-11 Hoya株式会社 Detecting device, detecting method, manufacturing method and pattern transferring method for photomask
TW201011370A (en) * 2008-06-05 2010-03-16 Coopervision Int Holding Co Lp Multi-imaging automated inspection methods and systems for wet ophthalmic lenses

Also Published As

Publication number Publication date
CN112557400A (en) 2021-03-26

Similar Documents

Publication Publication Date Title
CN112508903B (en) Method for detecting surface defect contour of lens of satellite telescope
CN107543828B (en) Workpiece surface defect detection method and system
TWI805857B (en) System and method for characterizing a specimen
GB2569751A (en) Static infrared thermal image processing-based underground pipe leakage detection method
CN111047568B (en) Method and system for detecting and identifying steam leakage defect
Tao et al. Weak scratch detection and defect classification methods for a large-aperture optical element
CN110910350B (en) Nut loosening detection method for wind power tower cylinder
US20210090230A1 (en) System and method for efficiently scoring probes in an image with a vision system
Oliveira Santos et al. Automatic mapping of cracking patterns on concrete surfaces with biological stains using hyper‐spectral images processing
CN112907526B (en) LBF-based satellite telescope lens surface defect detection method
Flesia et al. Sub-pixel straight lines detection for measuring through machine vision
CN110738644A (en) automobile coating surface defect detection method and system based on deep learning
CN115272256A (en) Sub-pixel level sensing optical fiber path Gaussian extraction method and system
CN112557400B (en) System and method for detecting surface defect contour of lens of satellite telescope
CN113012103B (en) Quantitative detection method for surface defects of large-caliber telescope lens
Heather et al. Multimodal image registration with applications to image fusion
Zhang et al. Research on the size of mechanical parts based on image recognition
CN115578315A (en) Bridge strain close-range photogrammetry method based on unmanned aerial vehicle image
Semenishchev et al. Algorithm for the automated determination of heating areas during metal processing with numerically controlled machines
CN114792327A (en) Image processing method and system
CN114299137A (en) Laser spot center positioning method and test system
Su et al. Dual-light inspection method for automatic pavement surveys
CN114663882B (en) Electric automobile chassis scratch three-dimensional detection method based on deep learning
CN114187269B (en) Rapid detection method for surface defect edge of small component
Loktev et al. Image Blur Simulation for the Estimation of the Behavior of Real Objects by Monitoring Systems.

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant