CN112907526B - LBF-based satellite telescope lens surface defect detection method - Google Patents

LBF-based satellite telescope lens surface defect detection method Download PDF

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CN112907526B
CN112907526B CN202110174979.4A CN202110174979A CN112907526B CN 112907526 B CN112907526 B CN 112907526B CN 202110174979 A CN202110174979 A CN 202110174979A CN 112907526 B CN112907526 B CN 112907526B
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邱根
陈薇
殷春
程玉华
王胤泽
陈凯
冯怡婷
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a satellite telescope lens surface defect detection method based on LBF, which comprises the steps of firstly, adding a weight term and a convergence term to a clustering target function to optimize the clustering target function, and classifying optical defect images by adopting an iterative method, so that the defect outline is enhanced while noise is removed; secondly, dividing the images subjected to classification into defect images by adopting an LBF active contour model through level set function evolution and taking a zero level set; and finally, quantizing the extracted defect image, and marking the defect outline of the defect image by adopting a binary chain code technology, thereby realizing the quantization of the area, the gravity center, the long and short diameters and the perimeter of the defect region. According to the method, the optimization clustering objective function is iterated, and the LBF active contour model is combined with defect image segmentation and defect quantification to enhance the damage characteristics of the defects on the surface of the lens of the satellite telescope, display the contour characteristic information of the lens, improve the detection precision and complete quantitative analysis of the defects.

Description

LBF-based satellite telescope lens surface defect detection method
Technology neighborhood
The invention belongs to the field of surface defect detection technology, and particularly relates to a satellite telescope lens surface defect detection method based on LBF.
Background
Ultra-precise optical elements are an important component of many high-precision instruments and equipment systems. In the aerospace neighborhood, a large number of optical components are used for 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. For a traditional precision system for detecting surface defects, although the detection precision is high, the equipment assembly is complex and high in cost, the position relation between parts, the motion condition and the like have strict requirements, and if a specific posture is changed, the directional indication is correspondingly changed, so that an operator is required to have a certain optical neighborhood 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 LBF-based satellite telescope lens surface defect detection method which is used for shooting a detected satellite telescope lens to obtain an optical defect image during in-situ non-contact defect detection of a large-size satellite telescope lens so as to improve the detection precision.
In order to achieve the above object, the present invention provides a method for detecting defects on the surface of a lens of a satellite telescope based on LBF, which is characterized by comprising:
(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 BDA0002940376990000021
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 BDA0002940376990000022
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 BDA0002940376990000023
Nxyset of neighborhood pixels, p, of pixel (x, y)rSet N of neighborhood pixelsxyPixel value of the middle r-th pixel point, IxyIs 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; p is a radical ofrPixel value, N, of a neighborhood point r representing a pixel point (x, y)xyA neighborhood set of pixels that is pixel (x, y);
wherein, betaxy＝αmin{||||pxy-cs||2||,s∈{1,2,...,G}},α∈[0,1] (4)
2.3) in
Figure BDA0002940376990000031
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 BDA0002940376990000032
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 BDA0002940376990000033
2.6) calculating the calculated clustering center c according to the formula (5) 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, i.e., | JFCM(k)-JFCM(k-1) if | | | is less than or equal to epsilon, finishing clustering, otherwise, skipping to the step 2.5);
2.8) according to the maximum membership criterion and according to 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 contour information*；
(3) Edge profile detection
For image I*Adopting a Local Binary Fitting (LBF) model, calculating the gradient descending flow of the LBF model, and carrying out level set function evolution and taking a zero level set to the image I*Dividing the defect image into defects and dividing the defectsReserving the image with the rest pixel values set as 0 to obtain the edge contour information image
Figure BDA0002940376990000034
Meanwhile, the segmented closed curve is used as a defect contour line;
(4) quantification of defects
Image of edge contour information
Figure BDA0002940376990000041
Represented in a three-dimensional image, comprising an XOY plane representing the size of the image, and a depth Z representing the defect in the lens, the depth Z being determined from the pixel values;
for a defect image, marking a defect contour line by adopting a binary chain code technology;
the areas of the defects are: edge profile information image
Figure BDA0002940376990000042
The total number of pixel points in the contour line of the medium defect;
center of gravity of defect
Figure BDA0002940376990000043
Comprises the following steps:
Figure BDA0002940376990000044
Figure BDA0002940376990000045
wherein H 'and L' are respectively the height and width of adjacent rectangles outside the defect outline;
perimeter of defect:
Figure BDA0002940376990000046
wherein n represents the total number of defective contour pixels, CjAnd the chain code direction number of the jth pixel point of the defect contour line is represented.
The invention aims to realize the following steps:
aiming at in-situ non-contact defect detection of a satellite telescope lens, the invention provides a LBF-based satellite telescope lens surface defect detection method. The method comprises the steps of adding a weight term and a convergence term to optimize a clustering objective function, and performing pixel point classification processing on acquired faulty images by using the optimized clustering objective function through an iteration method to inhibit the influence of illumination intensity nonuniformity on the acquired image intensity, ensure the faulty detection precision, improve the convergence speed of the algorithm and reduce the operation time of the algorithm; then, an LBF active contour model is adopted to segment the defect contour of the image representing the contour information after classification processing, and a target segmentation effect is obtained through level set function evolution and a zero level set, so that the active contour model is also called as a level set method, the model uses the zero level set of a high one-dimensional embedding function to represent a curve, and a gradient descending flow form of curve evolution is obtained through solving an Euler-Lagrange equation; and finally, quantifying the defects extracted from the active contour model, and marking the defects by adopting a binary chain code technology to the defect images, thereby realizing the quantification of the areas, the centers of gravity, the long and short diameters and the circumferences of the defect regions. Therefore, the LBF-based satellite telescope lens surface defect detection method combines the classification of the optimized clustering objective function, the detection of the geodesic active contour model and the defect quantification to enhance the damage characteristics of the defects on the surface of the satellite telescope lens, display the contour characteristic information of the defects, improve the detection precision and complete the quantitative analysis of the defects.
The related advantages and innovations of the invention are as follows:
1. book (I)The invention considers the illumination intensity information G in the clustering objective functionxy,iTo suppress the influence of the uneven intensity on the segmentation;
2. originally, each pixel point is isolated, but in the invention, the neighborhood condition of the pixel point is considered, and the membership function value u of the pixel point is calculatedxy,iThe spatial information of the optical defect is increased;
3. adding a weight term W in the objective function based on the information of optical image defects and membership degreesxy,iTo enhance the intensity information characteristic of the optical defect image;
4. and based on the membership degree information, a convergence item is added into the objective function to improve the convergence speed of the algorithm and reduce the running time of the algorithm.
Drawings
FIG. 1 is a schematic structural diagram of an embodiment of a system for detecting defects on the surface of a lens of a satellite telescope based on LBF, which is applied in the invention;
FIG. 2 is a specific flow chart of the LBF-based detection of defects on the surface 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 defects on the surface of the lens of the satellite telescope based on LBF;
FIG. 4 is a neighborhood information graph in the present invention;
FIG. 5 is a schematic diagram of chain codes and chain code encoding;
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 contour image segmented by the active contour model in an exemplary embodiment;
FIG. 9 is a three-dimensional image representation of a defect profile image in an exemplary embodiment;
fig. 10 is a scratch defect image in the specific example.
Detailed Description
The following description of the present invention will be provided in conjunction with the accompanying drawings for a better understanding of the present invention by those skilled in the art. 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 a system for detecting defects on the surface of a lens of a satellite telescope based on LBF, which is applied in the invention.
In this embodiment, as shown in fig. 1, the system for detecting defects on the lens surface of a satellite telescope based on LBF of the present invention is characterized by comprising: 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 specific working flow chart of the LBF-based satellite telescope lens surface defect detection system 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 a method for detecting defects on the surface of a lens of a satellite telescope based on LBF.
In this embodiment, as shown in fig. 3, the method for detecting defects on the surface of a lens of a satellite telescope based on LBF 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 BDA0002940376990000071
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 BDA0002940376990000072
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 BDA0002940376990000081
Nxyset of neighborhood pixels, p, of pixel (x, y)rSet N of neighborhood pixelsxyPixel value of the middle r-th pixel point, IxyIs 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 class i, x, y being images respectivelyHorizontal and vertical coordinates of the prime point; 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; p is a radical ofrPixel value, N, of a neighborhood point r representing a pixel point (x, y)xyA neighborhood set of pixels that is pixel (x, y);
in formula (1), the second term is:
Figure BDA0002940376990000082
for the convergence term, u is greater than or equal to 0xy,i1 or less, therefore
Figure BDA0002940376990000083
Is constantly equal to or greater than 0 and is in u xy,i0 or uxy，iWhen the value is 1, the minimum value is 0, so that the membership degree of the pixel is as close to 1 or 0 as possible, and the convergence rate of the algorithm is improved.
Wherein, betaxy＝αmin{||||pxy-cs||2||,s∈{1,2,...,G}},α∈[0,1] (4)。
As shown in FIG. 4, psPixel value, N, representing a subordinate neighborhood point of neighborhood point rrAnd a neighborhood pixel point set representing a pixel point neighborhood point r.
Step S2.3: in that
Figure BDA0002940376990000084
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 BDA0002940376990000085
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: calculating membership uxy,i：
Figure BDA0002940376990000091
Step S2.6: calculating the calculated clustering center c according to the formula (5) 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 pixel (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*。
Step S3: edge profile detection
For image I*Adopting a Local Binary Fitting (LBF) model, calculating the gradient descending flow of the LBF model, and carrying out level set function evolution and taking a zero level set to the image I*Dividing the defect image, reserving the divided defect image, setting the other pixel values as 0, and obtaining an edge contour information image
Figure BDA0002940376990000092
And simultaneously taking the segmented closed curve as a defect contour line.
Step S4: quantification of defects
Image of edge contour information
Figure BDA0002940376990000093
Represented in a three-dimensional image, including an XOY plane representing the size of the imageAnd a depth Z representing the defect in the lens, the depth Z being determined from the pixel values.
For a defect image, a binary chain code technology is adopted to mark a defect outline thereof, as shown in fig. 5.
The areas of the defects are: edge profile information image
Figure BDA0002940376990000094
The total number of pixel points in the contour line of the medium defect;
center of gravity of defect
Figure BDA0002940376990000095
Comprises the following steps:
Figure BDA0002940376990000101
Figure BDA0002940376990000102
wherein H 'and L' are respectively the height and width of adjacent rectangles outside the defect outline;
perimeter of defect:
Figure BDA0002940376990000103
wherein n represents the total number of defective contour pixels, CjThe serial number of the chain code direction of the j-th pixel point of the defect contour line is represented as 3202070764543 clockwise and 7010234346467 counterclockwise as shown in fig. 5.
Examples of the invention
In this embodiment, there is a scratch defect on the surface of the lens of the satellite telescope, and there is a scratch at the upper left edge position in fig. 6, and the particles formed by gas solidification are attached to the lens and distributed randomly. Using the device shown in FIG. 1 to collect the images of the defects on the surface of the lens of the satellite telescope, opening the vertical calibrator to makeThe laser point irradiates the geometric center of the measured lens which is horizontally placed, and the ruler slide rail is ensured to be vertical to the measured lens. 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. Classifying defects in the acquired images by using an optimized clustering objective function to obtain an image I with the characteristic contour information shown in FIG. 7*It can be seen that the clustering objective function is adopted for segmentation, and background information and defect outline information in the optical defect image are distinguished. And (3) extracting the outline edges of the defects by applying an active outline model to the classified images to obtain the images shown in the figure 8, and observing that scratch damage and gas solidified particles are fitted by an active outline algorithm, so that the integrity of outline information is ensured. Finally, the defects are quantitatively analyzed, the edge contour information image is represented as a three-dimensional image as shown in fig. 9, wherein the defects marked with gas solidified particles are quantified, and as the particles are circular defects, the major and minor diameters M and N are 3.75mm, the areas S and the circumferences a and 13 (pixel points), and the defects are attached to the surface of the lens, and the depth is 0. For scratch defects, the circumference a of the scratch defect shown in fig. 10 is 405 (pixel), and the depth in the lens is 77.32. The defect characteristics extracted by the invention are clear, and the quantification, namely high-precision detection, of the defects is realized.
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 without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matter which comes within the scope of the inventive concept is protected.

Claims (1)

1. A satellite telescope lens surface defect detection method based on LBF 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 FDA0003530407270000011
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 FDA0003530407270000012
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 FDA0003530407270000013
Nxyset of neighborhood pixels, p, of pixel (x, y)rSet N of neighborhood pixelsxyPixel value of the middle r-th pixel point, IxyIs 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 alsoSo that 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; p is a radical ofrPixel value, N, of a neighborhood point r representing a pixel point (x, y)xyA neighborhood set of pixels that is pixel (x, y);
wherein, betaxy＝αmin{||||pxy-cs||2||,s∈{1,2,...,G}},α∈[0,1] (4)
2.3) in
Figure FDA0003530407270000014
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 FDA0003530407270000021
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 FDA0003530407270000022
2.6) calculating the calculated clustering center c according to the formula (5) 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, i.e. | | JFCM(k)-JFCM(k-1) if | | | is less than or equal to epsilon, finishing clustering, otherwise, skipping to the step 2.5);
2.8) according to the maximum membership criterion and according to 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 to obtain an image I representing the contour information*；
(3) Edge profile detection
For image I*Adopting a Local Binary Fitting (LBF) model, calculating the gradient descending flow of the LBF model, and carrying out level set function evolution and taking a zero level set to the image I*Dividing the defect image, reserving the divided defect image, setting the other pixel values as 0, and obtaining an edge contour information image
Figure FDA0003530407270000023
Meanwhile, the segmented closed curve is used as a defect contour line;
(4) quantification of defects
Image of edge contour information
Figure FDA0003530407270000024
Represented in a three-dimensional image, comprising an XOY plane representing the size of the image, and a depth Z representing the defect in the lens, the depth Z being determined from the pixel values;
for a defect image, marking a defect contour line by adopting a binary chain code technology;
the areas of the defects are: edge profile information image
Figure FDA0003530407270000031
The total number of pixel points in the contour line of the medium defect;
center of gravity of defect
Figure FDA0003530407270000032
Comprises the following steps:
Figure FDA0003530407270000033
Figure FDA0003530407270000034
wherein H 'and L' are respectively the height and width of adjacent rectangles outside the defect outline;
perimeter of defect:
Figure FDA0003530407270000035
wherein n represents the total number of defective contour pixels, CjAnd the chain code direction number of the jth pixel point of the defect contour line is represented.
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