CN115272256A - Sub-pixel level sensing optical fiber path Gaussian extraction method and system - Google Patents
Sub-pixel level sensing optical fiber path Gaussian extraction method and system Download PDFInfo
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Abstract
The embodiment of the specification provides a sub-pixel level sensing optical fiber path Gaussian extraction method and system, wherein the method is used for detecting the path of a sensing optical fiber laid on the surface of a silicon wafer and comprises the following steps: determining a visual lighting scheme according to the material characteristics, and acquiring an image based on the visual lighting scheme; denoising the image through bilateral filtering, and extracting edge information of the distributed sensing optical fiber by adopting a sub-pixel edge detection technology based on a Canny algorithm; and closing the edge pairs based on the edge information, and extracting skeleton information by using a Gaussian line detection method to obtain the path of the distributed sensing optical fiber.
Description
Technical Field
The document relates to the technical field of computers, in particular to a method and a system for Gaussian extraction of a sub-pixel level sensing optical fiber path.
Background
The distributed sensing optical fiber has the characteristics of controllable ultrahigh spatial resolution, small mass and diameter, corrosion resistance, electric insulation, high sensitivity and the like. In addition, based on the characteristic of relatively soft and tough texture, the distributed sensing optical fiber has relatively good adaptability to the shape of the surface of the structure, and is often used for monitoring strain, stress deformation, temperature change and the like. In a wafer detection system based on a distributed sensing optical fiber, in order to enable measuring point data and a wafer surface position to correspond to each other and provide a data basis for exploring causes of adverse conditions such as warping deformation, accurate data reconstruction needs to be carried out on a wafer surface sensing optical fiber path.
The distributed sensing optical fiber is usually used in large and long-working-distance occasions such as tunnels, slopes, mines and the like, the spatial resolution is usually 0.5m or 1m, and each measuring point is not required to be accurately positioned, but the distributed sensing optical fiber is obviously not suitable for the fields of precision measurement and the like in the development of modern semiconductor science and technology, and a method is required for accurately acquiring the spatial position of the optical fiber measuring point when the distributed sensing optical fiber is applied to the precision measurement. In recent years, machine vision technology is becoming more mature, and as a typical non-contact detection technology, because of having the advantages of high precision, high intelligence and the like, the technology is often applied to various occasions requiring precise monitoring, such as: the method has good effects in the fields of defect detection, image restoration, medical images and the like. In the prior art, one mode uses a Forstner feature extraction operator to extract optical fiber feature points of a part on an image respectively, splices adjacent images to obtain a component panoramic image, and extracts required area images respectively by adopting a maximum inter-class variance method. And finally, removing texture noise through fast Fourier transform, and obtaining the complete path of the optical fiber by using a line extraction operator. The prior art also provides an improved SIFT algorithm, provides a setting method and an empirical formula of related parameters, and compares and verifies the strong robustness and anti-interference capability of the SIFT algorithm and the correctness and feasibility of the setting method of the related parameters through the detection of actual surface defects. The SIFT algorithm is obviously superior in the detection rate of the defects of the pits and the spots, and particularly has greater advantage in the false detection rate of the cracks under the condition of noise image interference; in addition, in the prior art, the problem of low accuracy is solved by a package surface defect detection method based on an image processing technology. Collecting a surface image of the packaging box by using a CCD camera, distinguishing a background area from a target image by using a binarization processing technology, processing a background and a foreground by using a shading correction difference, detecting an image edge by using an image redrawing algorithm, eliminating noise to obtain obvious image edge characteristics, defining various defects in the image by using a threshold value to finish defect characteristic extraction, designing a defect classifier according to the extracted defect characteristics, and finishing packaging surface defect detection by dividing and identifying defect contents; in the prior art, a seed growth method can be adopted to extract the edge of the solar cell electrode; the OTSU method can also be used to detect the image surface defects, but the method is mostly used in the case of uniform global gray scale and relatively less interference information.
In summary, in the prior art, the optical fiber path is extracted by using the line extraction operator in the machine vision, but the detection object is the optical fiber path in the interlayer of the composite material, so that the problem of identifying and then splicing multiple image paths is mainly solved, the detection of the optical fiber path on the high-reflectivity mirror surface is not involved, and the identification precision information of the algorithm is not given; in the prior art, an SIFT algorithm is used for detecting the surface defects of the product, and although the algorithm can realize high-precision category identification on a noisy and complex image, the algorithm is not focused on high-precision measurement, so that the method cannot be competent for the reconstruction of optical fiber path data; in the prior art, the defect classifier is used for realizing the product packaging defect detection, the requirement on the edge detection precision of the image is not high, and the requirement on the reconstruction of the optical fiber position data cannot be met; in the prior art, a seed growth method is adopted to extract the edges of the solar cell electrodes, and the three methods are all used for detecting specific types of defects and are not suitable for continuous optical fiber path extraction. In the prior art, an OTSU method is adopted to detect the surface defects of the image, but the method is not suitable for occasions with uneven global gray scale and more interference information.
Disclosure of Invention
The invention aims to provide a method and a system for extracting Gaussian of a sub-pixel level sensing optical fiber path, and aims to solve the problems in the prior art.
The invention provides a sub-pixel level sensing optical fiber path Gaussian extraction method, which is used for carrying out path detection on a small-diameter sensing optical fiber laid on the surface of a silicon wafer, and comprises the following steps:
determining a visual lighting scheme according to the material characteristics, and acquiring an image based on the visual lighting scheme;
denoising the image through bilateral filtering, and extracting edge information of the distributed sensing optical fiber by adopting a sub-pixel edge detection technology based on a Canny algorithm;
and closing the edge pairs based on the edge information, and extracting skeleton information by using a Gaussian line detection method to obtain the path of the distributed sensing optical fiber.
The invention provides a sub-pixel level sensing optical fiber path Gaussian extraction system, which is used for the method and comprises the following steps:
the industrial camera is connected with the computer and used for acquiring an image of the detected silicon wafer and transmitting the image to the computer;
the computer is used for determining a visual lighting scheme according to material characteristics, acquiring the image, reducing noise of the image through bilateral filtering, extracting edge information of the distributed sensing optical fiber by adopting a sub-pixel edge detection technology based on a Canny algorithm, closing an edge pair based on the edge information, and extracting skeleton information by using a Gaussian line detection method to obtain a path of the distributed sensing optical fiber;
the workbench is used for fixing the industrial camera above a silicon wafer to be detected;
and the square shadowless light source is used for carrying out visual illumination on the silicon wafer to be detected arranged in the center of the square shadowless light source based on the determined visual illumination scheme.
By adopting the embodiment of the invention, the distributed sensing optical fiber path can be accurately detected and the optical fiber measuring point can be extracted, and the method has high robustness and high precision, and can meet the requirements of accurately extracting the coordinates of the optical fiber measuring point, high anti-interference performance and the like.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments described in the present specification, and that other drawings may be obtained by those skilled in the art without inventive labor.
FIG. 1 is a flow chart of a sub-pixel level sensing fiber path Gaussian extraction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system hardware side view of an embodiment of the invention;
FIG. 3 is a schematic diagram of a system hardware top view of an embodiment of the present invention;
fig. 4 is a preferred processing flow diagram of a sub-pixel level sensing fiber path gaussian extraction method according to an embodiment of the present invention.
Detailed Description
In order to solve the above problems in the prior art, embodiments of the present invention provide a method and an apparatus for extracting a coordinate sequence of measurement points in a length direction of a sensing optical fiber based on gaussian line detection, so as to implement strain detection data reconstruction of a distributed sensing optical fiber on a wafer surface. According to the optical characteristics and the surface height difference of a silicon wafer and an optical fiber, a square shadowless light source is used for lighting in a dark field environment to improve the distinguishing degree of a sensing optical fiber on the surface of the wafer, a preprocessed image is obtained after image noise is reduced through bilateral filtering, a sub-pixel edge detection technology based on a Canny algorithm is adopted to carry out characteristic screening and eliminate an interference edge, a distributed sensing optical fiber target area is obtained, finally an optical fiber path is extracted through a Gaussian line detection method, and the path can be segmented and coordinate values of optical fiber measuring points can be extracted according to the number of actual sensing optical fiber measuring points.
In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.
Method embodiment
According to an embodiment of the present invention, a method for gaussian extraction of a sub-pixel level sensing optical fiber path is provided, which is characterized in that the method is used for detecting a path of a small-diameter sensing optical fiber laid on a surface of a silicon wafer, fig. 1 is a flowchart of the gaussian extraction method of a sub-pixel level sensing optical fiber path according to the embodiment of the present invention, and as shown in fig. 1, the gaussian extraction method of a sub-pixel level sensing optical fiber path according to the embodiment of the present invention specifically includes:
step 101, determining a visual lighting scheme according to material characteristics, and acquiring an image based on the visual lighting scheme;
102, denoising the image through bilateral filtering, and extracting edge information of the distributed sensing optical fiber by adopting a sub-pixel edge detection technology based on a Canny algorithm;
and 103, closing the edge pair based on the edge information, and extracting skeleton information by using a Gaussian line detection method to obtain a path of the distributed sensing optical fiber.
Step 101 specifically includes: and in a dark field environment, a square shadowless light source is used for carrying out side illumination from the periphery of the wafer, and an image which is free of reflection and has large discrimination between the optical fiber and the surface of the silicon wafer is obtained by a camera with enough resolution.
Step 102 specifically includes:
firstly, opening the acquired image in an original size without stretching, converting the colored image into a gray image, setting a region to be processed in the gray image, drawing a rectangular ROI (region of interest) region to be processed, marking the rectangular ROI region as region1, drawing a plurality of regions of other useless optical fiber segments, merging the drawn regions, marking the merged region as region2, acquiring an image processing region, and acquiring the region to be subjected to optical fiber path identification on the gray image by using the regions 1-2 to obtain a preprocessed image;
according to formula 1 and formula 2, bilateral filtering performs noise on the image by bilateral filtering:
wherein the parameter σ d And σ r For smoothing the parameters, I (I, j) and I (k, l) are the gray levels of the pixels (I, j) and (k, l), respectively, and after the weights are calculated, they are normalized, so that I D The gray level of the pixel point (i, j) after noise reduction is obtained;
convolution with the image using a Gaussian filter to smooth the image, the generation equation of the Gaussian filter kernel of size (2 + 1) × (2 + 1) is as in equation 3:
according to equation 4, the gradient image in the x and y directions is obtained by using a sobel filter, and then the gradient strength G and the gradient direction θ are obtained:
wherein, G x And G y Respectively sobel operator S x And S y Convolution of a 3 × 3 window a in the image;
based on equation 5, the edge points are linked into edges by an algorithm that calculates non-maximum suppression and hysteresis threshold operations, and point I of the pre-image is detected (x,y) Amplitude A ofGreater than omega max Is immediately accepted as an edge point and the gray value of the output image point O is set to 255 with an amplitude smaller than ω min The other points are also accepted as edges if they are connected to an accepted edge point;
obtaining the sub-pixel edge coordinate by fitting a quadratic polynomial shown in formula 6 to finally obtain O (i,j) ；
Wherein x and y are the horizontal and vertical coordinates of the edge point of the current integer coordinate, G L And G R The gradient value of the edge point is a left-right gradient value of the edge point, G is a gradient value of the edge point, and w is a distance from an adjacent pixel to the edge point;
regions are selected according to the area characteristics, and for each region that is input, the area characteristics are calculated and if the calculated characteristics of each region are within the limit (6000, 2e + 006), that region will be output.
The XLD contour is closed and then filled in as a region, noted region3.
And performing opening operation processing on the region3, and extracting the edge of the optical fiber by subtracting the corroded region3 from the preprocessed image.
Step 103 specifically comprises:
forming an edge pair rear closing forming area through sub-pixel level edges, reducing image interference through threshold selection and image opening operation, namely corrosion treatment, and finally extracting a skeleton path by using a Gaussian line detection method; the Gaussian line detection method specifically comprises the following steps:
determining Taylor quadratic polynomial parameters of each pixel point in the image in the x and y directions through a partial derivative of convolution of the image and a Gaussian mask, and calculating the line direction of each pixel point;
at right angles to the linesIn the second-order partial derivative Y of the direction, pixels showing local maximum values are marked as skeleton points, hysteresis threshold operation is carried out, and the condition that the second-order derivative is larger than Y is accepted max Rejecting second derivative less than Y min If all other line points are adjacent to the accepted points, the other line points are marked as skeleton points, and finally the found line points are connected into skeleton lines;
based on equation 7, the respective gray value contrasts P from the lines to be extracted max And P min Calculating the parameter Y from the selected sigma value max And Y min ：
The parameter sigma determines the smooth quantity to be executed by the Gaussian mask, the smooth quantity is in direct proportion to the smoothness of the image and in inverse proportion to the positioning accuracy of the lines, the path of the extracted result of the skeleton line is divided into a point set by the extracted result of the skeleton line, and coordinate points are provided for data reconstruction;
and extracting the skeleton in Gaussian detection, simultaneously extracting the line width of an XLD contour line, selecting a parabolic mode by adopting LineMode, combining sub-pixel level skeleton lines into a continuous skeleton line, and finally performing smoothing treatment.
After step 103 is performed, the following processing is also performed:
and obtaining sub-pixel level skeleton coordinates extracted through Gaussian detection through a get _ contourr _ xld operator, and screening out coordinates of the interval d on the skeleton according to the interval d of optical fiber data acquisition determined by the spatial resolution of an actual system to realize one-to-one correspondence of optical fiber detection data and the actual position of a silicon wafer.
The above technical solutions of the embodiments of the present invention are described in detail below with reference to the accompanying drawings.
The detection object of the embodiment of the invention is a sensing optical fiber with the surface laid on the surface of a silicon wafer.
As shown in fig. 2-3, the system architecture of the method of the embodiment of the present invention is composed of a computer (not shown), an industrial camera 1, a 12mm focal length lens 2, a workbench 3, a shadowless light source 4, a detection object sensing fiber 5 and a silicon wafer 6. The industrial camera 1 is mounted on a workbench and can move in x, y and z directions to adjust the photographing area and distance. The detection object is a sensing optical fiber 5 laid on a silicon wafer 6, which is located below the lens. A plurality of lamp beads are distributed around the shadowless light source 4 to polish the periphery of the silicon wafer 6. Meanwhile, the whole system is in a dark field environment, so that the problems of wafer reflection and the like caused by surrounding light sources are avoided. The reason for this is: the sensing optical fiber 5 is made of a high-transparency silicon dioxide material, the diameter of the sensing optical fiber is extremely small, the surface of a silicon wafer is smooth, and the specular reflection is serious, so that the side surface polishing can fully utilize the height difference of the sensing optical fiber 5 laid on the silicon wafer 6, the side surface polishing is only reflected to a lens through the sensing optical fiber 5, and almost no light is reflected to the lens in an area where the sensing optical fiber 5 is not laid due to the smooth surface of the silicon wafer 6.
As shown in fig. 4, the method of the embodiment of the present invention specifically includes the following processing:
acquiring an image: the computer acquires an original image with a large resolution of the sensing fiber 5 and the silicon wafer 6 from the industrial lens through the gigabit network cable.
Picture preprocessing:
1. the image 1 is first opened in its original size without stretching for subsequent image processing.
2. Converting the color image 1 into a gray scale image to obtain an image 2
3. A region to be processed is set in the image 2. First, a rectangular ROI area to be processed is drawn and is marked as region1. In addition, since only some segments of the optical fiber in the region1 need to be subjected to path identification for data reconstruction, a plurality of region renderings of other unnecessary optical fiber segments are required, and then the rendered plurality of regions are merged, and the merged region is referred to as region2.
4. An image processing region is acquired. On image 2, a color image is formed by: region1-region2 can obtain the region that needs to be identified for the fiber path, resulting in image 3.
Filtering and denoising:
in a vision processing system, particles such as dust easily exist on the surface of an actual object, noise may be generated in the processes of image acquisition, transmission and the like, the quality of an image is affected, and extraction of target information is interfered, especially in edge detection, because the gray value distribution characteristic of random noise is usually identified as an edge by a detection algorithm, preprocessing of an acquired original image is a very necessary process, the display quality of the image can be enhanced in the modes of noise reduction, image contrast adjustment and the like, so that subsequent processing steps such as feature extraction and the like are facilitated, and common traditional noise reduction methods include mean filtering, median filtering, gaussian filtering and the like. The noise reduction method used by the method is bilateral filtering, which is a typical nonlinear filtering method and can eliminate random noise and cause low edge blurring effect, and the effect is realized by smoothing pixels in a homogeneous region and reserving edge pixels with high contrast, and the definition is shown in formula 1 and formula 2.
Parameter σ in the formula d And σ r For smoothing the parameters, I (I, j) and I (k, l) are the gray levels of the pixels (I, j) and (k, l), respectively, and after calculating the weights, normalizing them, I D And (5) reducing the gray level of the pixel point (i, j) after noise reduction.
Edge extraction:
because the data reading of the sensing optical fiber corresponds to the optical fiber length information, in order to ensure the consistency of data reconstruction, the path extraction in the length direction is carried out on the distributed sensing optical fiber on the surface of the silicon wafer, and because the requirement on the precision of the detection result is extremely high, the edge detection is carried out on the optical fiber by adopting the sub-pixel edge detection technology based on the Canny algorithm, and then after the area is formed by closing the edge pair, the skeleton information is extracted by using the Gaussian line detection method, so that the sensing optical fiber path is obtained.
Sub-pixel edge detection based on the Canny algorithm: the edge detection has very important research significance as one of the technologies with high practical application value in the field of machine vision. The accuracy achieved by the traditional edge detection algorithm is pixel level, along with the progress of times and the development of the semiconductor industry, the accuracy required by industrial detection is rapidly improved, the traditional pixel level edge detection cannot meet the detection accuracy requirement of the sensing optical fiber 5, and therefore the method selects a sub-pixel level detection technology based on the Canny algorithm for dividing pixels again so as to improve the image resolution.
1. The image 3 is subjected to sub-pixel contour extraction using the edges _ sub _ pix operator. The filter is Canny.
The specific principle is as follows: first convolved with the image using a Gaussian filter to smooth the image, the equation for generating the Gaussian filter kernel of size (2 + 1) × (2 + 1) is shown in equation 3.
Then using sobel filter to obtain gradient image in x and y directions, and further obtaining gradient strength G and gradient direction theta, as formula 4, where G x And G y Respectively sobel operator S x And S y Convolution of a 3 x 3 window a in the image.
Edge points are linked into edges using algorithms that compute non-maximum suppression and operate similar to hysteresis thresholds. Point I of the Pre-detection image (x,y) If the amplitude A of (a) is larger than omega max Is immediately accepted as an edge point and the gray value of the output image point O is set to 255 with an amplitude smaller than ω min Is rejected, while other points are accepted as edges if they are connected to an accepted edge point. Expressed as in equation 5.
Finally, obtaining the edge coordinates of the sub-pixels by fitting through a quadratic polynomial formula 6, wherein x and y are the horizontal and vertical coordinates of the edge points of the current integer coordinates, and G L And G R Is the gradient value of the edge point left and right, G is the gradient value of the edge point, and w is the distance from the adjacent pixel to the edge point, and finally O is obtained (i,j) 。
2. Regions are selected based on area characteristics, and for each region input, area (area) characteristics are calculated. If the calculated characteristic of each region is within the limit of (6000, 2e + 006), that region will be output.
3. The XLD contour is first closed and then filled in as a region, denoted region3.
4. And performing opening operation processing on the region3, and subtracting the region3 after corrosion from the image 3 to realize the extraction of the optical fiber edge.
And (3) extracting a skeleton by Gaussian detection:
an edge pair is formed by the sub-pixel level edges, then an area is formed by closing, then the image interference is further reduced by threshold selection and image opening operation (namely corrosion processing), and finally a skeleton path is extracted by using a Gaussian line detection method.
The detection method is characterized in that Taylor quadratic polynomial parameters of each pixel point in an image in x and y directions are determined through a partial derivative of convolution of the image and a Gaussian mask, and therefore the line direction of each pixel point is calculated. In the second partial derivative Y, which is perpendicular to the line direction, pixels that exhibit local maxima are marked as skeleton points. Similar to the hysteresis threshold operation in equation 5, accepting the second derivative to be greater than Y max Rejecting second derivative less than Y min If all other line points are adjacent to the accepted points, they are also marked as skeleton points, and finally the found line points are connected as skeleton lines. Parameter Y max And Y min The respective gray value contrasts P that can be extracted from the lines to be extracted max And P min Calculated from the selected σ value, as shown in equation 7, where the parameter σ determines the amount of smoothing to be performed by the gaussian mask, it is proportional to the smoothness of the image, but inversely proportional to the positioning accuracy of the lines, which may cause a mis-positioning of the extracted lines. The path of the skeleton line can be split into a point set according to the extraction result of the skeleton line, and coordinate point positions are provided for data reconstruction.
Specifically, the gaussian detection extracts the skeleton and the line width of the XLD contour line, and the LineMode selects the parabolic mode because the edge of the picture is relatively sharp. And finally, combining the sub-pixel level skeleton lines into a continuous skeleton line, and finally performing smoothing treatment.
Outputting coordinates:
since the data reading of the sensing fiber 5 corresponds to the fiber length information, the corresponding coordinates can be found on the extracted skeleton line according to the measuring point position (actual measuring position) determined by the actual system spatial resolution of the sensing fiber 5, and then output. So far, the one-to-one correspondence between the measurement data of the sensing optical fiber 5 and the specific position of the silicon wafer 6 can be realized.
Specifically, the sub-pixel level skeleton coordinates extracted through Gaussian detection can be obtained through a get _ constraint _ xld operator, then the coordinates of the interval d on the skeleton are screened out according to the interval d of the measuring points determined by the system space resolution of the actual optical fiber, and finally input is carried out.
In conclusion, the embodiment of the invention realizes sub-pixel high-precision path detection and optical fiber measuring point coordinate extraction of the sensing optical fiber laid on the silicon wafer. The extracted sensing optical fiber is subjected to line width analysis, the unilateral line width is uniformly distributed, and the system stability is high. The accuracy of the detection method can reach 10 microns by calculation according to the actual diameter of the sensing optical fiber, and the high stability and the high accuracy of the Gaussian line skeleton path detection method are also demonstrated. The technical scheme of the embodiment of the invention has high robustness, and experiments prove that although the actually laid sensing optical fiber 5 is not well attached and similar edge information interference exists around the sensing optical fiber, the method can still accurately identify the skeleton path of the actually laid sensing optical fiber 5 and is not influenced by defects and accidental interference.
System embodiment
According to an embodiment of the present invention, there is provided a sub-pixel level sensing optical fiber path gaussian extraction system, which is used in the method described in the above method embodiment, and the sub-pixel level sensing optical fiber path gaussian extraction system according to the embodiment of the present invention specifically includes:
the industrial camera is connected with the computer and used for acquiring an image of the detected silicon wafer and transmitting the image to the computer; the industrial camera is a five million-pixel camera with the resolution of 3856 multiplied by 2764, and the focal length of a lens is 12mm.
The computer is used for determining a visual lighting scheme according to material characteristics, acquiring the image, reducing noise of the image through bilateral filtering, extracting edge information of the distributed sensing optical fiber by adopting a sub-pixel edge detection technology based on a Canny algorithm, closing an edge pair based on the edge information, and extracting skeleton information by using a Gaussian line detection method to obtain a path of the distributed sensing optical fiber;
the workbench is used for fixing the industrial camera above the silicon wafer to be detected;
and the square shadowless light source is used for carrying out visual illumination on the silicon wafer to be detected arranged in the center of the square shadowless light source based on the determined visual illumination scheme. The visual lighting scheme specifically comprises: and in a dark field environment, a square shadowless light source is used for carrying out side illumination from the periphery of the wafer, and an image which is free of reflection and has large discrimination between the optical fiber and the surface of the silicon wafer is obtained by a camera with enough resolution.
The computer is specifically configured to:
firstly, opening the acquired image in an original size without stretching, converting the colored image into a gray image, setting a region to be processed in the gray image, drawing a rectangular ROI (region of interest) region to be processed, marking the rectangular ROI region as region1, drawing a plurality of regions of other useless optical fiber segments, merging the drawn regions, marking the merged region as region2, acquiring an image processing region, and acquiring the region to be subjected to optical fiber path identification on the gray image by using the regions 1-2 to obtain a preprocessed image;
according to formula 1 and formula 2, bilateral filtering denoises the image by bilateral filtering:
wherein the parameter σ d And σ r For smoothing the parameters, I (I, j) and I (k, l) are the gray levels of the pixels (I, j) and (k, l), respectively, and after calculating the weights, normalizing them, I D The gray level of the pixel point (i, j) after noise reduction;
convolution with the image using a Gaussian filter to smooth the image, the equation for generating the Gaussian filter kernel of size (2 + 1) × (2 + 1) is as in equation 3:
according to formula 4, a sobel filter is used to obtain gradient images in the x and y directions, and then gradient strength G and gradient direction theta are obtained:
wherein G is x And G y Respectively sobel operator S x And S y Convolution of a 3 × 3 window a in the image;
based on equation 5, the edge points are linked into edges by an algorithm that calculates non-maximum suppression and hysteresis threshold operations, and point I of the pre-image is detected (x,y) If the amplitude A of (a) is larger than omega max Is immediately accepted as an edge point and the gray value of the output image point O is set to 255 with an amplitude smaller than ω min The other points are also accepted as edges if they are connected to an accepted edge point;
obtaining sub-pixel edge coordinates by fitting a quadratic polynomial shown in formula 6 to finally obtain O (i,j) ；
Wherein x and y are the horizontal and vertical coordinates of the edge point of the current integer coordinate, G L And G R The gradient value of the edge point is left and right, G is the gradient value of the edge point, and w is the distance from the adjacent pixel to the edge point;
regions are selected according to the area characteristics, and for each region that is input, the area characteristics are calculated and if the calculated characteristics of each region are within the limit (6000, 2e + 006), that region will be output.
The XLD contour is closed and then filled in as a region, denoted region3.
Performing opening operation processing on the region3, and extracting the edge of the optical fiber by subtracting the region3 subjected to corrosion from the preprocessed image;
forming an edge pair rear closing forming area through sub-pixel level edges, reducing image interference through threshold selection and image opening operation, namely corrosion treatment, and finally extracting a skeleton path by using a Gaussian line detection method; the Gaussian line detection method specifically comprises the following steps:
determining Taylor quadratic polynomial parameters of each pixel point in the image in the x and y directions through a partial derivative of convolution of the image and a Gaussian mask, and calculating the line direction of each pixel point;
in the second partial derivative Y perpendicular to the line direction, willMarking the pixel showing local maximum as skeleton point, carrying out hysteresis threshold operation, and accepting the second derivative greater than Y max Line point of (3), rejecting second derivative less than Y min If all other line points are adjacent to the accepted points, the other line points are marked as skeleton points, and finally the found line points are connected into skeleton lines;
based on equation 7, the respective gray value contrasts P from the lines to be extracted max And P min Calculating the parameter Y from the selected sigma value max And Y min ：
The parameter sigma determines the smooth quantity to be executed by the Gaussian mask, the smooth quantity is in direct proportion to the smoothness of the image and in inverse proportion to the positioning accuracy of the lines, the path of the extracted result of the skeleton line is divided into a point set by the extracted result of the skeleton line, and coordinate points are provided for data reconstruction;
and extracting the skeleton in Gaussian detection, extracting the line width of an XLD contour line, selecting a parabolic mode by adopting LineMode, combining sub-pixel level skeleton lines into a continuous skeleton line, and finally performing smoothing treatment.
The computer is further configured to:
and obtaining sub-pixel level skeleton coordinates extracted through Gaussian detection through a get _ contourr _ xld operator, and screening out coordinates of an interval d on the skeleton according to the interval d of data acquisition of an actual optical fiber to realize one-to-one correspondence between optical fiber detection data and the actual position of a silicon wafer.
That is to say, the system hardware part mainly comprises a computer, an industrial camera, a workbench and a square shadowless light source. The industrial camera is a five million pixel camera with a resolution of 3856 x 2764, a lens focal length of 12mm, and is connected to the computer through a gigabit network cable. The silicon wafer to be detected is placed in the center of the square shadowless light source, and the industrial camera is installed above the silicon wafer through the workbench and takes a picture downwards. In order to improve the discrimination of the distributed sensing optical fiber path on the surface of the silicon wafer, a proper visual illumination scheme needs to be designed according to the material characteristics of the silicon wafer and the optical fiber, so that the image preprocessing process of graying and noise reduction is performed after an image is acquired.
The optical fiber is usually made of a high-transparency silica material and has a very small volume, so that when the optical fiber is laid on the surface of a silicon wafer, the optical fiber has a phenomenon of low visual distinction degree, and the surface of the silicon wafer is very smooth and has an obvious mirror reflection phenomenon, so that the optical fiber is easily interfered by ambient light, and the requirement on the type selection of a light source is also increased. In order to prevent the camera from being unable to acquire an image with a sufficient amount of information, a suitable illumination scheme needs to be designed.
The illumination solution of the embodiment of the invention is as follows: a square shadowless light source is used to side illuminate the wafer from all sides in a dark field environment. The problem of ambient light interference is solved through arranging dark field environment, and the sensing optical fiber is ingeniously utilized again and is laid the difference in height that forms on the wafer surface, has solved the problem that optic fibre divides the degree at the wafer surface low through the mode of side illumination, has avoided the produced specular reflection effect of wafer when the light source is from the front illumination simultaneously, finally obtains waiting to detect the original image.
As shown in fig. 2-3, the system architecture of the method of the embodiment of the present invention is composed of a computer (not shown), an industrial camera 1, a 12mm focal length lens 2, a workbench 3, a shadowless light source 4, a detection object sensing fiber 5 and a silicon wafer 6. The industrial camera 1 is mounted on a workbench and can move in x, y and z directions to adjust the photographing area and distance. The detection object is a sensing optical fiber 5 laid on a silicon wafer 6, which is located below the lens. A plurality of lamp beads are distributed around the shadowless light source 4 and polish the periphery of the silicon wafer 6. Meanwhile, the whole system is in a dark field environment, so that the problems of wafer reflection and the like caused by surrounding light sources are avoided. The reason for this is: the sensing optical fiber 5 is made of a high-transparency silicon dioxide material, the diameter of the sensing optical fiber is extremely small, the surface of a silicon wafer is smooth, and the specular reflection is serious, so that the side surface polishing can fully utilize the height difference of the sensing optical fiber 5 laid on the silicon wafer 6, the side surface polishing is only reflected to a lens through the sensing optical fiber 5, and almost no light is reflected to the lens in an area where the sensing optical fiber 5 is not laid due to the smooth surface of the silicon wafer 6.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A sub-pixel level sensing optical fiber path Gaussian extraction method is used for carrying out path detection on a sensing optical fiber laid on the surface of a silicon wafer, and comprises the following steps:
determining a visual lighting scheme according to the material characteristics, and acquiring an image based on the visual lighting scheme;
denoising the image through bilateral filtering, and extracting edge information of the distributed sensing optical fiber by adopting a sub-pixel edge detection technology based on a Canny algorithm;
and closing the edge pairs based on the edge information, and extracting skeleton information by using a Gaussian line detection method to obtain the path of the distributed sensing optical fiber.
2. The method according to claim 1, wherein a visual illumination scheme is determined from the material properties, the acquiring of the image based on the visual illumination scheme comprising in particular:
and in a dark field environment, a square shadowless light source is used for carrying out side illumination from the periphery of the wafer, and an image which is free of reflection and has large discrimination between the optical fiber and the surface of the silicon wafer is obtained by a camera with enough resolution.
3. The method according to claim 1, wherein the noise is applied to the image through bilateral filtering, and the extracting of the edge information of the distributed sensing optical fiber by using a sub-pixel edge detection technology based on a Canny algorithm specifically comprises:
firstly, opening the acquired image in an original size without stretching, converting the colored image into a gray image, setting a region to be processed in the gray image, drawing a rectangular ROI (region of interest) region to be processed, marking the rectangular ROI region as region, drawing a plurality of regions of other useless optical fiber line segments, merging the plurality of drawn regions, marking the merged region as region2, acquiring an image processing region, and acquiring the region to be subjected to optical fiber path identification on the gray image by using the regions 1-2 to obtain a preprocessed image;
according to formula 1 and formula 2, bilateral filtering performs noise on the image by bilateral filtering:
wherein the parameter σ d And σ r For smoothing the parameters, I (I, j) and I (k, l) are the gray levels of the pixels (I, j) and (k, l), respectively, and after the weights are calculated, they are normalized, so that I D The gray level of the pixel point (i, j) after noise reduction;
convolution of the image with a Gaussian filter to smooth the image, the generation equation of the Gaussian filter kernel of size (2k + 1) × (2k + 1) is as in equation 3:
according to equation 4, the gradient image in the x and y directions is obtained by using a sobel filter, and then the gradient strength G and the gradient direction θ are obtained:
wherein, G x And G y Respectively sobel operator S x And S y Convolution of a 3 × 3 window a in the image;
based on formula 5, the edge points are linked into edges by using an algorithm for calculating non-maximum suppression and hysteresis threshold operation, and a point I of the previous image is detected (x，y) If the amplitude A of (a) is larger than omega max Is immediately accepted as an edge point and the gray value of the output image point O is set to 255 with an amplitude smaller than ω min The other points are rejected, and if connected with the accepted edge points, the other points are also accepted as edges;
obtaining sub-pixel edge coordinates by fitting a quadratic polynomial shown in formula 6 to finally obtain O (i，j) ；
Wherein x and y are the horizontal and vertical coordinates of the edge point of the current integer coordinate, G L And G R The gradient value of the edge point is left and right, G is the gradient value of the edge point, and w is the distance from the adjacent pixel to the edge point;
regions are selected according to the area characteristics, and for each region input, the area characteristics are calculated, and if the calculation characteristics of each region are within the limit (6000,2e + 006), the region is output.
The XLD contour is closed and then filled in as a region, denoted region3.
The region3 is subjected to an opening operation process, and the fiber edge is extracted by subtracting the region3 after the etching from the preprocessed image.
4. The method of claim 1, wherein closing edge pairs based on the edge information and extracting skeleton information using a gaussian line detection method to obtain paths of the distributed sensing fiber specifically comprises:
forming an edge pair rear closing forming area through sub-pixel level edges, reducing image interference through threshold selection and image opening operation, namely corrosion treatment, and finally extracting a skeleton path by using a Gaussian line detection method; the Gaussian line detection method specifically comprises the following steps:
determining Taylor quadratic polynomial parameters of each pixel point in the image in the x and y directions through a partial derivative of convolution of the image and a Gaussian mask, and calculating the line direction of each pixel point;
marking pixels showing local maximum values as skeleton points in a second-order partial derivative Y vertical to the line direction, performing hysteresis threshold operation, and receiving the condition that the second-order derivative is larger than Y max Rejecting second derivative less than Y min If all other line points are adjacent to the accepted points, the other line points are marked as skeleton points, and finally the found line points are connected into skeleton lines;
based on equation 7, the respective gray value contrasts P from the lines to be extracted max And P min Calculating the parameter Y from the selected sigma value max And Y min ：
The parameter sigma determines the smoothing quantity to be executed by the Gaussian mask, is in direct proportion to the smoothness of the image, but is in inverse proportion to the positioning accuracy of the lines, and the path is divided into point sets by the extraction result of the skeleton line, so that coordinate points are provided for data reconstruction;
and extracting the skeleton in Gaussian detection, simultaneously extracting the line width of an XLD contour line, selecting a parabolic mode by adopting LineMode, combining sub-pixel level skeleton lines into a continuous skeleton line, and finally performing smoothing treatment.
5. The method of claim 1, further comprising:
and obtaining sub-pixel level skeleton coordinates extracted through Gaussian detection through a get _ contourr _ xld operator, and screening out the coordinates of the interval d on the skeleton according to the interval d of optical fiber data acquisition determined by the spatial resolution of an actual optical fiber demodulator, so as to realize the one-to-one correspondence of the optical fiber detection data and the actual position of the silicon wafer.
6. A sub-pixel level sensing fiber path gaussian extraction system for use in the method of any of the preceding claims 1 to 5, the system comprising:
the industrial camera is connected with the computer and used for acquiring an image of the detected silicon wafer and transmitting the image to the computer;
the computer is used for determining a visual lighting scheme according to material characteristics, obtaining the image, reducing noise of the image through bilateral filtering, extracting edge information of the distributed sensing optical fiber by adopting a sub-pixel edge detection technology based on a Canny algorithm, closing an edge pair based on the edge information, and extracting skeleton information by using a Gaussian line detection method to obtain a path of the distributed sensing optical fiber;
the workbench is used for fixing the industrial camera above a silicon wafer to be detected;
and the square shadowless light source is used for visually illuminating the silicon wafer to be detected arranged in the center of the square shadowless light source based on the determined visual illumination scheme.
7. The system of claim 6, wherein the industrial camera is a five million pixel camera with a resolution of 3856 x 2764 and a lens focal length of 12mm.
8. The system according to claim 6, characterized in that the visual lighting scheme is in particular: and in a dark field environment, a square shadowless light source is used for carrying out side illumination from the periphery of the wafer, and an image which is free of reflection and has large discrimination between the optical fiber and the surface of the silicon wafer is obtained by a camera with enough resolution.
9. The system of claim 6, wherein the computer is specifically configured to:
firstly, opening the acquired image in an original size without stretching, converting the colored image into a gray image, setting a region to be processed in the gray image, drawing a rectangular ROI (region of interest) region to be processed, marking the rectangular ROI region as region1, drawing a plurality of regions of other useless optical fiber line segments, merging the drawn plurality of regions, marking the merged region as region2, acquiring an image processing region, and acquiring the region to be subjected to optical fiber path identification on the gray image by using the region1-region2 to obtain a preprocessed image;
according to formula 1 and formula 2, bilateral filtering performs noise on the image by bilateral filtering:
wherein the parameter σ d And σ r For smoothing the parameters, I (I, j) and I (k, l) are the gray levels of the pixels (I, j) and (k, l), respectively, and after the weights are calculated, they are normalized, so that I D The gray level of the pixel point (i, j) after noise reduction;
convolving with the image using a Gaussian filter to smooth the image, the generation equation of the Gaussian filter kernel of size (2k + 1) × (2k + 1) is as in equation 3:
according to equation 4, the gradient image in the x and y directions is obtained by using a sobel filter, and then the gradient strength G and the gradient direction θ are obtained:
wherein, G x And G y Respectively sobel operator S x And S y Convolution of a 3 × 3 window a in the image;
based on equation 5, the edge points are linked into edges by an algorithm that calculates non-maximum suppression and hysteresis threshold operations, and point I of the pre-image is detected (x，y) If the amplitude A of (a) is larger than omega max Is immediately accepted as an edge point and the gray value of the output image point O is set to 255 with an amplitude smaller than ω min The other points are rejected, and if connected with the accepted edge points, the other points are also accepted as edges;
obtaining sub-pixel edge coordinates by fitting a quadratic polynomial shown in formula 6 to finally obtain O (i，j) ；
Wherein x and y are the horizontal and vertical coordinates of the edge point of the current integer coordinate, G L And G R The gradient value of the edge point is left and right, G is the gradient value of the edge point, and w is the distance from the adjacent pixel to the edge point;
regions are selected according to the area characteristics, and for each region that is input, the area characteristics are calculated and if the calculated characteristics of each region are within the limit (6000, 2e + 006), that region will be output.
The XLD contour is closed and then filled in as a region, noted region3.
Performing opening operation processing on the region3, and extracting the edge of the optical fiber by subtracting the region3 subjected to corrosion from the preprocessed image;
forming an edge pair rear closing forming area through sub-pixel level edges, reducing image interference through threshold selection and image opening operation, namely corrosion treatment, and finally extracting a skeleton path by using a Gaussian line detection method; the Gaussian line detection method specifically comprises the following steps:
determining Taylor quadratic polynomial parameters of each pixel point in the image in the x direction and the y direction through a partial derivative of convolution of the image and a Gaussian mask, and calculating the line direction of each pixel point;
marking pixels showing local maximum values as skeleton points in a second-order partial derivative Y vertical to the line direction, performing hysteresis threshold operation, and receiving the condition that the second-order derivative is larger than Y max Rejecting second derivative less than Y min If all other line points are adjacent to the accepted points, the other line points are marked as skeleton points, and finally the found line points are connected into skeleton lines;
based on equation 7, the respective gray value contrasts P from the lines to be extracted max And P min Calculating the parameter Y from the selected sigma value max And Y min ：
The parameter sigma determines the smooth quantity to be executed by the Gaussian mask, the smooth quantity is in direct proportion to the smoothness of the image and in inverse proportion to the positioning accuracy of the lines, the path of the extracted result of the skeleton line is divided into a point set by the extracted result of the skeleton line, and coordinate points are provided for data reconstruction;
and extracting the skeleton in Gaussian detection, simultaneously extracting the line width of an XLD contour line, selecting a parabolic mode by adopting LineMode, combining sub-pixel level skeleton lines into a continuous skeleton line, and finally performing smoothing treatment.
10. The system of claim 6, wherein the computer is further configured to:
and obtaining sub-pixel level skeleton coordinates extracted through Gaussian detection through a get _ contourr _ xld operator, and screening out the coordinates of the interval d on the skeleton according to the interval d of optical fiber data acquisition determined by the spatial resolution of the actual demodulator, so as to realize the one-to-one correspondence of the optical fiber detection data and the actual position of the silicon wafer.
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| CN116309510A (en) * | 2023-03-29 | 2023-06-23 | 清华大学 | Numerical control machining surface defect positioning method and device |
| CN116342613A (en) * | 2023-06-01 | 2023-06-27 | 嘉兴视联智能科技股份有限公司 | Self-adaptive anti-interference machine vision detection method and system for monocrystalline silicon rod crystal line |
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| CN116309510A (en) * | 2023-03-29 | 2023-06-23 | 清华大学 | Numerical control machining surface defect positioning method and device |
| CN116309510B (en) * | 2023-03-29 | 2024-03-22 | 清华大学 | Numerical control machining surface defect positioning method and device |
| CN116342613A (en) * | 2023-06-01 | 2023-06-27 | 嘉兴视联智能科技股份有限公司 | Self-adaptive anti-interference machine vision detection method and system for monocrystalline silicon rod crystal line |
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