CN110288540B - Carbon fiber wire X-ray image online imaging standardization method - Google Patents

Carbon fiber wire X-ray image online imaging standardization method Download PDF

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CN110288540B
CN110288540B CN201910484022.2A CN201910484022A CN110288540B CN 110288540 B CN110288540 B CN 110288540B CN 201910484022 A CN201910484022 A CN 201910484022A CN 110288540 B CN110288540 B CN 110288540B
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carbon fiber
image
value
fiber composite
composite core
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CN110288540A (en
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胡轶宁
王征
陈大兵
李成钢
张建国
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Southeast University
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20032—Median filtering

Abstract

The invention provides a carbon fiber wire X-ray image online imaging standardization method, and belongs to the technical field of combination of computer vision and image processing. The technical scheme is as follows: a carbon fiber lead X-ray image online imaging standardization method comprises the following steps of S1: carrying out logarithm processing on the carbon fiber composite core image; s2: straightening the carbon fiber composite core image: s3: cutting the carbon fiber composite core X-ray imaging graph according to the boundary; s4: selecting a target graph D required by histogram specification; s5: the preprocessed image is histogram normalized. The invention has the beneficial effects that: according to the method, the carbon fiber composite core pictures with consistent bending degree are obtained according to the image straightening algorithm of the boundary position, so that the carbon fiber composite core is horizontally stretched; the histogram is specified to obtain an image with overall consistent brightness, and the overall brightness distribution of the image is ensured to be consistent; and obtaining an accurate carbon fiber composite core image by a cutting algorithm to obtain an effective carbon fiber composite core.

Description

Carbon fiber wire X-ray image online imaging standardization method
Technical Field
The invention relates to the technical field of combination of computer vision and image processing, in particular to an online imaging standardization method for an X-ray image of a carbon fiber lead.
Background
The carbon fiber wire is widely applied at present, the power consumption of various industries is increased geometrically, and the load borne by the carbon fiber composite core is increased more and more. Because the environment of the carbon fiber composite core is complex and various, no matter in construction or use, the carbon fiber composite core part is easy to be damaged, normal power supply is directly influenced, and the life of residents is greatly influenced, so that the method for detecting the damaged position of the carbon fiber composite core is very important. The X-ray imaging effect is most effective in a plurality of flaw detection modes, so that X-ray imaging is selected to detect the damaged position of the carbon fiber composite core, but the difficult processing conditions that the position of the carbon fiber composite core in an X-ray imaging picture is uncertain, the imaging of the carbon fiber composite core is bent and the imaging X-ray dosage is different exist, the image needs to be preprocessed by using standardized operation, and the subsequent image processing is convenient.
Disclosure of Invention
The invention aims to provide a carbon fiber lead X-ray online imaging standardization method, which can solve the problems of different image brightness and uncertain positions of carbon fiber composite core images caused by the fact that the carbon fiber composite cores are bent in imaging and different in X doses of images, ensure that the obtained imaging images are consistent in the positions, pixel distribution and bending degree of the carbon fiber composite cores, and achieve the purpose of standardization.
The invention is realized by the following measures: a carbon fiber wire X-ray image online imaging standardization method comprises the following steps:
s1: extracting carbon fiber composite core images in an effective imaging circle of the carbon fiber lead X-ray image, and carrying out logarithmic processing on the carbon fiber composite core images;
s2: straightening the carbon fiber composite core image:
the step S2 specifically includes the following steps:
s2.1: acquiring pixel points where the carbon fiber composite core boundaries on each row of pixels are located according to the pixel threshold or the gradient, and solving the average value of the positions of the upper boundary and the lower boundary as the middle point of the effective part of each row of carbon fiber composite cores;
traversing all pixel values of the picture, wherein the maximum value of the pixel values is the maximum gray value, and the maximum gray value multiplied by K is a threshold t; for the jth column, the topmost pixel P (0) is first selectedJ) begin a downward traversal, finding the first pixel whose value is greater than the threshold t, and the point P (i)1J) as the column boundary point; then, the upward traversal is started from the next pixel point, and the found pixel P (i) with the first pixel value larger than the threshold value t2And j) as the lower boundary point of the column, the midpoint of the effective portion of the carbon fiber composite core is marked as
Figure GDA0002961693160000011
S2.2: calculating the average value of the midpoints M (i) and the midpoint sequence of the effective parts of the carbon fiber composite cores of the pixels in each row
Figure GDA0002961693160000012
The difference value of (a) is used as the offset of each column of pixels;
s2.3: performing median filtering and multiple times of Gaussian filtering on the offset sequence;
s2.4: each column of pixels is longitudinally translated by an interpolation algorithm according to an offset and stored in a new graph P ', and for the j' th column of pixels, the integer part of the offset is calculated
Figure GDA0002961693160000026
Direct translation, for fractional part
Figure GDA0002961693160000027
Rounding down, and obtaining a final pixel point value P' (i, j) by using a selective interpolation algorithm between two pixel points P (i + O (j) -1, j) and P (i + O (j), j);
Figure GDA0002961693160000028
s3: cutting the carbon fiber composite core X-ray imaging graph according to the upper boundary and the lower boundary, and reserving the carbon fiber composite core part: obtaining all column boundary points P' (i) according to said step S2.11J) and a lower boundary point P' (i)2J)) and find the average value of the two line coordinates
Figure GDA0002961693160000021
And
Figure GDA0002961693160000022
the image is cut as an upper boundary and a lower boundary, the left cutting and the right cutting are the same as the upper cutting and the lower cutting, and the final cutting result is recorded as S;
s4: selecting a target graph D required by histogram specification: finding a graph from the graphs processed by the methods of the step S2 and the step S3, and counting the histogram distribution situation of the graph by using a histogram statistical method;
s5: histogram specification of the preprocessed image: all the images S obtained in the steps S2 and S3 are subjected to histogram defining operation with the target image D selected in the step S4 as a standard.
As a further optimized solution of the carbon fiber lead X-ray online imaging standardization method, in step S1, the pixels of all points of the carbon fiber composite core image in the effective imaging circle of the carbon fiber lead X-ray image are logarithmized, and the processing formula is as follows:
P(i,j)＝-lg(Po(i,j)/65535)*65535*α
wherein, PoThe (i, j) is the gray value before the point (i, j) is subjected to the logarithmic processing, the P (i, j) is the gray value after the point (i, j) is subjected to the logarithmic processing, and the value coefficient alpha is 0.3-0.5.
As a further optimized solution of the carbon fiber lead X-ray online imaging standardization method of the present invention, in step S1, in the X-ray imaging, the X-ray imaging radiation is circular, and the image in the circle is extracted.
As a further optimization scheme of the carbon fiber wire X-ray online imaging standardization method, in step S2.2, the average value of the midpoints of the effective parts of each row of pixels is obtained
Figure GDA0002961693160000023
Extracting the maximum value | M (i) of the absolute value | M (i) of the offset amount |, M |)maxThe middle points M (i) of the effective parts of the carbon fiber composite cores of the pixels in each row are averaged with the middle point sequence
Figure GDA0002961693160000024
Is added with | M (i) & gtdoes not countmaxDefining an offset as the offset of the column of pixels
Figure GDA0002961693160000025
Where R denotes the number of rows of the image matrix.
As a further optimization of the carbon fiber wire X-ray online imaging standardization method of the present invention, in step S2.3, a one-dimensional gaussian filter kernel with a size of 1 × 5 is used (0.1151, 0.2295, 0.3108, 0.2295, 0.1151).
As a further optimization scheme of the carbon fiber lead X-ray online imaging standardization method, in step S4, an image in which the histogram mean is in the range of 36000 ± 4000 is selected as an effective target map, which is a target map D obtained by histogram specification, and this step is performed only once.
As a further optimization scheme of the carbon fiber wire X-ray online imaging standardization method, the step S5 is specifically to calculate the histogram distribution condition of the input preprocessed image by using the histogram statistical method in the step S4, and match the histogram distribution of the two preprocessed images according to the fact that the histogram distribution of the two preprocessed images after histogram equalization is the same, so as to obtain an energy level mapping relationship; the histogram equalization is to calculate the probability value of the value according to the obtained histogram distribution result, make the probability of each value behind the value the sum of the probabilities of all the values in front of the value to obtain a cumulative histogram, and obtain the energy level probability after matching according to the cumulative histogram distribution.
In the step S2.1, the value obtained by multiplying the maximum gray value by K0.05 is a threshold t, and the value range of K is 0.05-0.1.
In order to better achieve the above object of the invention, specifically, a carbon fiber wire X-ray online imaging standardization method includes the following steps:
a carbon fiber wire X-ray image online imaging standardization method comprises the following steps:
s1: extracting carbon fiber composite core images in an effective imaging circle of the carbon fiber lead X-ray image, and carrying out logarithmic processing on the carbon fiber composite core images;
because in the X-ray imaging process, the X-ray imaging radiation is circular, so only the image imaging effect in the effective imaging circle of the carbon fiber lead X-ray image is good, the image in the circle needs to be extracted, specifically, the radiation circle is obtained by using the closed operation in the vertical direction as a mask, after the carbon fiber composite core is extracted according to the mask, the pixel of all points of the carbon fiber composite core image in the effective imaging circle of the carbon fiber lead X-ray image is logarithmized, and the processing formula is as follows:
P(i,j)＝-lg(Po(i,j)/65535)*65535*α
wherein, Po(i, j) is the gray value before point (i, j) is processed by logarithm, P (i, j) is the gray value after point (i, j) is processed by logarithm, the value taking coefficient prevents the pixel value after logarithm from being too high, and the value taking range of alpha is [0.3-0.5 ]]；
S2: straightening the carbon fiber composite core image:
the step S2 specifically includes the following steps:
s2.1: acquiring pixel points where the carbon fiber composite core boundaries on each row of pixels are located according to the pixel threshold or the gradient, and solving the average value of the positions of the upper boundary and the lower boundary as the middle point of the effective part of each row of carbon fiber composite cores;
traversing all pixel values of the picture, wherein the maximum value of the pixel values is the maximum gray value, and the maximum gray value multiplied by K is a threshold t; for the jth column, the uppermost pixel point P (0, j) is traversed downwards to find the pixel with the first pixel value larger than the threshold value t, and the point P (i) is1J) as the column boundary point; then, the upward traversal is started from the next pixel point, and the found pixel P (i) with the first pixel value larger than the threshold value t2And j) as the lower boundary point of the column, the midpoint of the effective portion of the carbon fiber composite core is marked as
Figure GDA0002961693160000041
S2.2: calculating the average value of the midpoints M (i) and the midpoint sequence of the effective parts of the carbon fiber composite cores of the pixels in each row
Figure GDA0002961693160000042
The difference value of (a) is used as the offset of each column of pixels;
calculating the average value of the middle points of the effective parts of the pixels of each column
Figure GDA0002961693160000043
Extracting the maximum value | M (i) of the absolute value | M (i) of the offset amount |, M |)maxThe middle points M (i) of the effective parts of the carbon fiber composite cores of the pixels in each row are averaged with the middle point sequence
Figure GDA0002961693160000044
Is added with | M (i) & gtdoes not countmaxDefining an offset as the offset of the column of pixels
Figure GDA0002961693160000045
Wherein R represents the number of rows of the image matrix;
s2.3: performing median filtering and multiple times of Gaussian filtering on the offset sequence; using a one-dimensional gaussian filter kernel of size 1 x 5 (0.1151, 0.2295, 0.3108, 0.2295, 0.1151);
s2.4: each column of pixels is longitudinally translated by an interpolation algorithm according to an offset and stored in a new graph P ', and for the j' th column of pixels, the integer part of the offset is calculated
Figure GDA0002961693160000046
Direct translation, for fractional part
Figure GDA0002961693160000047
Rounding down, and obtaining a final pixel point value P' (i, j) by using a selective interpolation algorithm between two pixel points P (i + O (j) -1, j) and P (i + O (j), j);
Figure GDA00029616931600000410
s3: cutting the carbon fiber composite core X-ray imaging graph according to the upper boundary and the lower boundary, and reserving the carbon fiber composite core part:obtaining all column boundary points P' (i) according to said step S2.11J) and a lower boundary point P' (i)2J)) and find the average value of the two line coordinates
Figure GDA0002961693160000048
And
Figure GDA0002961693160000049
the image is cut as an upper boundary and a lower boundary, the left cutting and the right cutting are the same as the upper cutting and the lower cutting, and the final cutting result is recorded as S;
s4: selecting a target graph D required by histogram specification: finding a graph from the graphs processed by the methods of the step S2 and the step S3, and counting the histogram distribution situation of the graph by using a histogram statistical method;
selecting an image of which the histogram mean value is in the range of 36000 +/-4000 as an effective target image, wherein the effective target image is a target image obtained by stipulating the histogram, and the step is only executed once;
s5: histogram specification of the preprocessed image: performing histogram normalization on all the images S obtained in the steps S2 and S3, with the target image D selected in the step S4 as a standard;
specifically, the histogram statistical method in step S4 is performed on the input preprocessed image to calculate the histogram distribution, and the histogram distribution of the two preprocessed images is matched according to the fact that the histogram distribution of the two histograms after equalization is the same, so as to obtain an energy level mapping relationship; the histogram equalization is to calculate the probability value of the value according to the obtained histogram distribution result, make the probability of each value behind the value the sum of the probabilities of all the values in front of the value to obtain a cumulative histogram, and obtain the energy level probability after matching according to the cumulative histogram distribution.
In the step S2.1, the value obtained by multiplying the maximum gray value by K0.05 is a threshold t, and the value range of K is 0.05-0.1.
The invention has the beneficial effects that: according to the method, the carbon fiber composite core pictures with consistent bending degree are obtained according to the image straightening algorithm of the boundary position, so that the carbon fiber composite core is ensured to be horizontally stretched; the histogram is specified to obtain an image with overall consistent brightness, and the overall brightness distribution of the image is ensured to be consistent; the cutting algorithm obtains an accurate carbon fiber composite core image, an effective carbon fiber composite core is ensured to be obtained, unnecessary non-reflection black areas are reduced, the calculated amount is reduced, the image standard can be effectively unified through the flow operation, and the calculation complexity and the situation complexity which possibly occur in subsequent images are reduced; obtaining a carbon fiber composite core image with consistent brightness and position through an image straightening algorithm, a normalization algorithm and a cutting algorithm of the boundary position; the method effectively solves the problems of different image brightness and uncertain positions of the carbon fiber composite core image caused by the bending of the carbon fiber composite core image and the different X doses of the image, ensures that the obtained imaging image keeps consistent in the positions of the carbon fiber composite core, the pixel distribution and the bending degree of the carbon fiber composite core, and achieves the aim of standardization.
Drawings
FIG. 1 is a general flow chart of an embodiment of the present invention;
FIG. 2 is a flow chart of a straightening algorithm for an X-ray image of a carbon fiber composite core in an embodiment of the invention;
FIG. 3 is a process diagram of an example of the on-line imaging normalization of an X-ray image of a carbon fiber wire according to the present invention.
Detailed Description
In order to clearly illustrate the technical features of the present solution, the present solution is explained below by way of specific embodiments.
Referring to fig. 1-3, the present invention is: a carbon fiber wire X-ray image online imaging standardization method comprises the following steps:
s1: extracting carbon fiber composite core images in an effective imaging circle of the carbon fiber lead X-ray image, and carrying out logarithmic processing on the carbon fiber composite core images;
because in the X-ray imaging process, the X-ray imaging radiation is circular, so only the image imaging effect in the effective imaging circle of the carbon fiber lead X-ray image is good, the image in the circle needs to be extracted, specifically, the radiation circle is obtained by using the closed operation in the vertical direction as a mask, after the carbon fiber composite core is extracted according to the mask, the pixel of all points of the carbon fiber composite core image in the effective imaging circle of the carbon fiber lead X-ray image is logarithmized, and the processing formula is as follows:
P(i,j)＝-lg(Po(i,j)/65535)*65535*α
wherein, Po(i, j) is a gray value before point (i, j) is subjected to logarithmic processing, P (i, j) is a gray value after point (i, j) is subjected to logarithmic processing, a coefficient is taken to prevent a pixel value after logarithmic processing from being too high, and alpha is taken to be 0.42;
by pixel point p0(1100,1500) and p1(1370,2680) for example, the former pixel value is 28550, and after logarithmic processing, the value is 11824, and the pixel point appears as a point in a dark stripe in the image; the latter pixel value is 7000, and after the logarithmic processing is 26736, it appears as a point of normal brightness in the image.
S2: straightening the carbon fiber composite core image:
the step S2 specifically includes the following steps:
s2.1: acquiring pixel points where the carbon fiber composite core boundaries on each row of pixels are located according to the pixel threshold or the gradient, and solving the average value of the positions of the upper boundary and the lower boundary as the middle point of the effective part of each row of carbon fiber composite cores;
traversing all pixel values of the picture, wherein the maximum value of the pixel values is the maximum gray value, and the threshold t is obtained by multiplying the maximum gray value by 0.05; wherein the maximum pixel value is 64840, the maximum gray-scale value multiplied by 0.05 is the threshold t, that is, t equals 3242, for the jth column, the first pixel point P (0, j) starts to traverse downwards, and the pixel with the first pixel value greater than the threshold t is found, and the point P (i) is1J) as the column boundary point; then, the upward traversal is started from the next pixel point, and the found pixel P (i) with the first pixel value larger than the threshold value t2And j) as the lower boundary point of the column, the midpoint of the effective portion of the carbon fiber composite core is marked as
Figure GDA0002961693160000061
For the example of image 434, the ordinate of the upper boundary is 1229, the ordinate of the lower boundary is 1581, and the corresponding midpoint value is 1405.
S2.2: find eachThe midpoint M (i) of the effective part of the carbon fiber composite core of the row pixels and the average value of the midpoint sequence
Figure GDA0002961693160000062
The difference value of (a) is used as the offset of each column of pixels;
calculating the average value of the middle points of the effective parts of the pixels of each column
Figure GDA0002961693160000063
Extracting the maximum value | M (i) of the absolute value | M (i) of the offset amount |, M |)maxThe middle points M (i) of the effective parts of the carbon fiber composite cores of the pixels in each row are averaged with the middle point sequence
Figure GDA0002961693160000064
Is added with | M (i) & gtdoes not countmaxDefining an offset as the offset of the column of pixels
Figure GDA0002961693160000065
Wherein R represents the number of rows of the image matrix;
the average of the pixel values in the test sample is calculated to be 1371.5932, column 1300 is taken as an example, the midpoint position in the column is 1383, the maximum offset value is 82, and the offset value is 89.4068.
S2.3: performing median filtering and multiple times of Gaussian filtering on the offset sequence; using a one-dimensional gaussian filter kernel of size 1 x 5 (0.1151, 0.2295, 0.3108, 0.2295, 0.1151);
s2.4: each column of pixels is longitudinally translated by an interpolation algorithm according to an offset and stored in a new graph P ', and for the j' th column of pixels, the integer part of the offset is calculated
Figure GDA0002961693160000066
Direct translation, for fractional part
Figure GDA0002961693160000067
Rounding down, and obtaining a final pixel point value P' (i, j) by using a selective interpolation algorithm between two pixel points P (i + O (j) -1, j) and P (i + O (j), j);
Figure GDA0002961693160000068
according to the example of step S2.2, the pixel values of the pixel point (1380, 1300) and the pixel point (1381,1300), the previous pixel value is 28765, and the next pixel value is 28515, so as to obtain the interpolated pixel value 28663.3 of the pixel point (1291,1300), and the rounded value is 28663.
S3: cutting the carbon fiber composite core X-ray imaging graph according to the upper boundary and the lower boundary, and reserving the carbon fiber composite core part: obtaining all column boundary points P' (i) according to said step S2.11J) and a lower boundary point P' (i)2J)) and find the average value i of the two line coordinates1And i2The image is cut as an upper boundary and a lower boundary, the left cutting and the right cutting are the same as the upper cutting and the lower cutting, and the final cutting result is recorded as S;
and (3) according to the average values 1216 and 1532 of the upper and lower coordinates in the step S2.1, performing clipping according to the average values of the upper and lower boundaries, wherein the average abscissa of the left and right boundaries is 623 and 2652, and clipping the left and right boundaries to finally obtain a clipping graph with the size of 2029x 316.
S4: selecting a target graph D required by histogram specification: finding a graph from the graphs processed by the methods of the step S2 and the step S3, and counting the histogram distribution situation of the graph by using a histogram statistical method; specifically, a 16-bit image is counted into an [0,1023] interval according to a pixel value divided by 64, the number of each value from 0 to 1023 is accumulated to obtain a histogram, an image of which the histogram mean value is in the range of 36000 +/-4000 is selected as an effective target image which is a target image obtained by stipulating the histogram, and the step is only executed once;
s5: histogram specification of the preprocessed image: performing histogram normalization on all the images S obtained in the steps S2 and S3, with the target image D selected in the step S4 as a standard;
specifically, the histogram statistical method in step S4 is performed on the input preprocessed image to calculate the histogram distribution, and the histogram distribution of the two preprocessed images is matched according to the fact that the histogram distribution of the two histograms after equalization is the same, so as to obtain an energy level mapping relationship; the histogram equalization is to calculate the probability value of the value according to the obtained histogram distribution result, make the probability of each value behind the value the sum of the probabilities of all the values in front of the value to obtain a cumulative histogram, and obtain the energy level probability after matching according to the cumulative histogram distribution.
The invention has the beneficial effects that: according to the method, the carbon fiber composite core pictures with consistent bending degree are obtained according to the image straightening algorithm of the boundary position, so that the carbon fiber composite core is ensured to be horizontally stretched; the histogram is specified to obtain an image with overall consistent brightness, and the overall brightness distribution of the image is ensured to be consistent; the cutting algorithm obtains an accurate carbon fiber composite core image, an effective carbon fiber composite core is ensured to be obtained, unnecessary non-reflection black areas are reduced, the calculated amount is reduced, the image standard can be effectively unified through the flow operation, and the calculation complexity and the situation complexity which possibly occur in subsequent images are reduced; obtaining a carbon fiber composite core image with consistent brightness and position through an image straightening algorithm, a normalization algorithm and a cutting algorithm of the boundary position; the method effectively solves the problems of different image brightness and uncertain positions of the carbon fiber composite core image caused by the bending of the carbon fiber composite core image and the different X doses of the image, ensures that the obtained imaging image keeps consistent in the positions of the carbon fiber composite core, the pixel distribution and the bending degree of the carbon fiber composite core, and achieves the aim of standardization.
This embodiment is based on the Microsoft Visual Studio 2012 platform.
In a word, practice proves that the carbon fiber lead X-ray online imaging standardization method can obtain carbon fiber composite core images with consistent brightness and positions according to an image straightening algorithm, a normalization algorithm and a cutting algorithm of boundary positions; and the obtained carbon fiber lead X-ray imaging graph keeps consistent in the position of the carbon fiber composite core, the pixel distribution and the bending degree of the carbon fiber composite core, so that the aim of standardization is fulfilled.
The technical features of the present invention which are not described in the above embodiments may be implemented by or using the prior art, and are not described herein again, of course, the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and variations, modifications, additions or substitutions which may be made by those skilled in the art within the spirit and scope of the present invention should also fall within the protection scope of the present invention.

Claims (7)

1. A carbon fiber wire X-ray image online imaging standardization method is characterized by comprising the following steps:
s1: extracting carbon fiber composite core images in an effective imaging circle of the carbon fiber lead X-ray image, and carrying out logarithmic processing on the carbon fiber composite core images;
s2: straightening the carbon fiber composite core image:
the step S2 specifically includes the following steps,
s2.1: acquiring pixel points where the carbon fiber composite core boundaries on each row of pixels are located according to the pixel threshold or the gradient, and solving the average value of the positions of the upper boundary and the lower boundary as the middle point of the effective part of each row of carbon fiber composite cores;
traversing all pixel values of the picture, wherein the maximum value of the pixel values is the maximum gray value, and the maximum gray value multiplied by K is a threshold t; for the jth column, the uppermost pixel point P (0, j) is traversed downwards to find the pixel with the first pixel value larger than the threshold value t, and the point P (i) is1J) as the column boundary point; then, the upward traversal is started from the next pixel point, and the found pixel P (i) with the first pixel value larger than the threshold value t2And j) as the lower boundary point of the column, the midpoint of the effective portion of the carbon fiber composite core is marked as
Figure FDA0002961693150000011
Multiplying the maximum gray value by K in the step S2.1 to obtain a threshold value t, wherein the value range of K is 0.05-0.1;
s2.2: calculating the average value of the midpoints M (i) and the midpoint sequence of the effective parts of the carbon fiber composite cores of the pixels in each row
Figure FDA0002961693150000012
The difference value of (a) is used as the offset of each column of pixels;
s2.3: performing median filtering and multiple times of Gaussian filtering on the offset sequence;
s2.4: each column of pixels is longitudinally translated by an interpolation algorithm according to an offset and stored in a new graph P ', and for the j' th column of pixels, the integer part of the offset is calculated
Figure FDA0002961693150000013
Direct translation, for fractional part
Figure FDA0002961693150000014
Rounding down to two pixels
Figure FDA0002961693150000015
And
Figure FDA0002961693150000016
the final pixel point value is obtained as P' (i, j) by using a selective interpolation algorithm;
Figure FDA0002961693150000017
s3: cutting the carbon fiber composite core X-ray imaging graph according to the upper boundary and the lower boundary, and reserving the carbon fiber composite core part: all column boundary points P' (i) are obtained according to step S2.41J) and a lower boundary point P' (i)2J) and find the average value of the two line coordinates
Figure FDA0002961693150000018
And
Figure FDA0002961693150000019
the image is cut as an upper boundary and a lower boundary, the left cutting and the right cutting are the same as the upper cutting and the lower cutting, and the final cutting result is recorded as S;
s4: selecting a target graph D required by histogram specification: finding a graph from the graphs processed by the methods of the step S2 and the step S3, and counting the histogram distribution situation of the graph by using a histogram statistical method;
s5: histogram specification of the preprocessed image: all the images S obtained in the steps S2 and S3 are subjected to histogram defining operation with the target image D selected in the step S4 as a standard.
2. The method for standardizing the on-line imaging of the X-ray image of the carbon fiber lead as claimed in claim 1, wherein in the step S1, the pixels of all the points of the carbon fiber composite core image in the effective imaging circle of the X-ray image of the carbon fiber lead are processed by logarithmization, and the processing formula is as follows:
P(i,j)＝-lg(Po(i,j)/65535)*65535*α
wherein, PoThe (i, j) is the gray value before the point (i, j) is subjected to the logarithmic processing, the P (i, j) is the gray value after the point (i, j) is subjected to the logarithmic processing, and the value coefficient alpha is 0.3-0.5.
3. The method for standardizing X-ray images on-line imaging of carbon fiber leads as claimed in claim 1, wherein in step S1, the X-ray imaging radiation is circular in shape, and the image in the circle is extracted.
4. The method for standardizing X-ray images on-line imaging of carbon fiber leads as claimed in claim 1, wherein in step S2.2, the mean value of the midpoints of the effective parts of each row of pixels is obtained
Figure FDA0002961693150000021
Extracting the maximum value | M (i) of the absolute value | M (i) of the offset amount |, M |)maxThe middle points M (i) of the effective parts of the carbon fiber composite cores of the pixels in each row are averaged with the middle point sequence
Figure FDA0002961693150000022
Is added with | M (i) & gtdoes not countmaxDefining an offset as the offset of the column of pixels
Figure FDA0002961693150000023
Where R denotes the number of rows of the image matrix.
5. The method for on-line imaging normalization of an X-ray image of a carbon fiber wire according to claim 1, characterized in that in step S2.3, a one-dimensional Gaussian filter kernel with a size of 1X 5 is used (0.1151, 0.2295, 0.3108, 0.2295, 0.1151).
6. The method for standardizing the on-line imaging of the X-ray image of the carbon fiber wire as claimed in claim 1, wherein in the step S4, the image with the histogram mean value in the range of 36000 ± 4000 is selected as the effective target image, which is the target image D obtained by the histogram specification, and the step is performed only once.
7. The method for standardizing an X-ray image of a carbon fiber wire in an online imaging manner as claimed in any one of claims 1 to 6, wherein the step S5 is to calculate the histogram distribution of the input preprocessed image by the histogram statistical method in the step S4, and match the histogram distribution of the preprocessed image and the histogram distribution of the preprocessed image to obtain a level mapping relationship;
the histogram equalization is to calculate the probability value of the value according to the obtained histogram distribution result, make the probability of each value behind the value the sum of the probabilities of all the values in front of the value to obtain a cumulative histogram, and obtain the energy level probability after matching according to the cumulative histogram distribution.
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* Cited by examiner, † Cited by third party
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CN110807751A (en) * 2019-10-31 2020-02-18 栗智 Method for eliminating illumination influence in industrial environment imaging
CN115376167B (en) * 2022-10-26 2023-02-24 山东圣点世纪科技有限公司 Palm detection method and system under complex background

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102937599A (en) * 2012-10-25 2013-02-20 中国科学院自动化研究所 Non-destructive testing systems and method used for detecting a metal-containing object through X-ray detection
CN103033134A (en) * 2013-01-07 2013-04-10 上海理工大学 Detection system and detection method aiming at damaged optical fiber geometry parameters
CN105321147A (en) * 2014-06-25 2016-02-10 腾讯科技（深圳）有限公司 Image processing method and apparatus
CN106846298A (en) * 2016-12-22 2017-06-13 清华大学 The recognition methods of optical fiber winding displacement and device
CN107657605A (en) * 2017-09-11 2018-02-02 中南大学 A kind of Measurement of surface deepth method before sieve plate based on active profile and energy constraint
CN107730520A (en) * 2017-09-22 2018-02-23 智车优行科技（北京）有限公司 Method for detecting lane lines and system
CN108036746A (en) * 2017-12-26 2018-05-15 太原理工大学 A kind of Gabor transformation based on Spectrum Method realizes carbon fibre composite surface texture analysis method
CN108074235A (en) * 2017-12-26 2018-05-25 太原理工大学 Carbon fiber surface defect degree method of estimation based on region growing algorithm
CN108776957A (en) * 2018-05-25 2018-11-09 北京小米移动软件有限公司 Face image processing process and device

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8781177B2 (en) * 2011-07-08 2014-07-15 I Shou University Method and computer program product for extracting feature vectors from a palm image
CN103686194B (en) * 2012-09-05 2017-05-24 北京大学 Video denoising method and device based on non-local mean value
CN103218605B (en) * 2013-04-09 2016-01-13 东南大学 A kind of fast human-eye positioning method based on integral projection and rim detection
CN105550660B (en) * 2015-12-26 2018-11-23 河北工业大学 A kind of recognition methods of woven fabric structure structure species
CN108573269B (en) * 2017-10-24 2021-02-05 北京金山云网络技术有限公司 Image feature point matching method, matching device, electronic device and storage medium

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102937599A (en) * 2012-10-25 2013-02-20 中国科学院自动化研究所 Non-destructive testing systems and method used for detecting a metal-containing object through X-ray detection
CN103033134A (en) * 2013-01-07 2013-04-10 上海理工大学 Detection system and detection method aiming at damaged optical fiber geometry parameters
CN105321147A (en) * 2014-06-25 2016-02-10 腾讯科技（深圳）有限公司 Image processing method and apparatus
CN106846298A (en) * 2016-12-22 2017-06-13 清华大学 The recognition methods of optical fiber winding displacement and device
CN107657605A (en) * 2017-09-11 2018-02-02 中南大学 A kind of Measurement of surface deepth method before sieve plate based on active profile and energy constraint
CN107730520A (en) * 2017-09-22 2018-02-23 智车优行科技（北京）有限公司 Method for detecting lane lines and system
CN108036746A (en) * 2017-12-26 2018-05-15 太原理工大学 A kind of Gabor transformation based on Spectrum Method realizes carbon fibre composite surface texture analysis method
CN108074235A (en) * 2017-12-26 2018-05-25 太原理工大学 Carbon fiber surface defect degree method of estimation based on region growing algorithm
CN108776957A (en) * 2018-05-25 2018-11-09 北京小米移动软件有限公司 Face image processing process and device

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
X射线数字成像技术在电力电缆现场检测中的应用;李成钢 等;《无损检测》;20150228;第37卷(第2期);第74-77页 *
基于数字图像处理的经编布瑕疵检测系统;关向伟;《中国优秀硕士学位论文全文数据库信息科技辑》;20160915;第I138-546页 *

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