CN111524133B - Window statistical vertex extraction method and system for honeycomb regularity detection - Google Patents

Window statistical vertex extraction method and system for honeycomb regularity detection Download PDF

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CN111524133B
CN111524133B CN202010388384.4A CN202010388384A CN111524133B CN 111524133 B CN111524133 B CN 111524133B CN 202010388384 A CN202010388384 A CN 202010388384A CN 111524133 B CN111524133 B CN 111524133B
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value
image
point
honeycomb
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CN111524133A (en
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王中钢
周伟
孙成名
姚松
袁可
孙博
谢素超
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Central South University
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Abstract

The invention discloses a window statistical vertex extraction method and a system for detecting cellular regularity, wherein the method comprises the steps of obtaining an image, processing the image, extracting the vertex and analyzing the morphology; the step of binarization is arranged between the step of image processing and the step of vertex extraction; the step of binarization is to set the pixel value of the background in the image as 0 and the pixel value of the honeycomb skeleton in the image as 1 to obtain a binarized image; the step of morphological analysis is based on binarized images, and the geometric deviation degree of the cells is calculated after vertex extraction. The system comprises a detection table, a digital camera and a computer; the digital camera is electrically connected with the computer; the digital camera is at least one, the resolution is not lower than 1080P, a telecentric lens is arranged, and the installation mode is fixed or/and movable. The method and the system have the advantages of being scientific, reasonable, simple and feasible, high in detection precision, high in working efficiency and the like.

Description

Window statistical vertex extraction method and system for honeycomb regularity detection
Technical Field
The invention relates to the fields of design, manufacture, application and the like of light structural products of equipment such as traffic, machinery, aerospace, ships and the like, in particular to a window statistical vertex extraction method and a system for detecting cellular regularity.
Background
Lightweight cellular products are widely used for their excellent load carrying capacity and good energy absorbing properties, for example, high speed trains are also widely used. However, the honeycomb products are prone to cell deformation during production, transportation and use, and the deformation can have an important effect on the performance of the honeycomb products. It is therefore necessary to evaluate the cell deformation, i.e. the geometric regularity, of the cellular product in order to make a decision on the quality of the cellular product.
Chinese patent application number 201910503383.7, filing date 2019, 9 and 13, discloses a method for identifying honeycomb edges from honeycomb core surface measurement data. The method comprises the following steps: carrying out two-dimensional coordinate transformation on the acquired three-dimensional data of the surface of the honeycomb core, and identifying angular points in a two-dimensional plane projection image of the honeycomb core through an angular point detection algorithm, wherein the angular points comprise humanoid vertexes, Y-shaped vertexes and pseudo-vertexes and also comprise unidentified missing angular points; based on the proposed corner type judging algorithm, sequentially identifying two endpoints of the honeycomb side, wherein one endpoint is determined when the adjacent side is identified, and the other endpoint is determined through local analysis of the identified vertex; in the realization process of the honeycomb edge, the false vertexes can be effectively eliminated, meanwhile, missing vertexes are supplemented, and the stable and high-precision recognition of the honeycomb edge is realized. After identifying the honeycomb edges, an evaluation can be made of the geometric regularity of the honeycomb product, and a determination can be made of the quality of the honeycomb product accordingly. The method has the advantages of high precision and good robustness, but the method needs to scan the surface of the honeycomb core point by point, and is time-consuming, long and complicated in steps.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a window statistical vertex extraction method and a system for detecting cellular regularity.
The aim of the invention is realized by the following technical scheme:
the method comprises the following steps:
a window statistical vertex extraction method for detecting the regularity of a honeycomb comprises the steps of identifying an image of a specified honeycomb product, and analyzing and judging the quality level of the honeycomb product; the method sequentially comprises the following steps: acquiring an image, processing the image, extracting vertexes and analyzing morphology; the step of acquiring an image comprises shooting the image and reading the image by a computer;
the step of binarization is arranged between the step of image processing and the step of vertex extraction; the step of binarization is to set the pixel value of the background in the image as 0 and the pixel value of the honeycomb skeleton in the image as 1 to obtain a binarized image;
the step of morphological analysis is based on binarized images, and the geometric deviation degree of the cells is calculated after vertex extraction.
The step of vertex extraction is based on a binarized image, and comprises the following steps of: pixel point numbering, pixel statistics, pixel assignment, determining cell side length, determining vertex threshold and determining vertex;
in the step "pixel number", if the image has m pixels in height and n pixels in width, the pixel number i=1 to k, namely: i has a starting point of P (1, 1) and an ending point of P (m, n);
the step of 'pixel statistics', starting from the pixel number i=1 to the pixel number i=k, when the pixel value of the pixel=1, taking the pixel as a center point, taking l=3 pixels as an initial side length as a square window, if part of the area of the window overflows an image, filling the pixel of the overflow area with pixels with the pixel value=0, then calculating the pixel numbers of the pixel value=0 and the pixel value=1 in the window and storing, if the sum of the pixel numbers of the pixel value=0 in the window is zero, clearing all records, starting from the pixel number i=1 to the pixel number i=k, when the pixel value of the pixel=1, taking the pixel as the center point, taking l=l+2 pixels as the side length as a square window, repeating the steps until the pixel number of the pixel value=0 in all the windows taking the pixel value=1 as the center is larger than zero;
the step of pixel assignment is to sequentially find a square assignment window with a side length L by taking a pixel point with a pixel value=1 as a center point on the basis of the step of pixel statistics, and calculate the sum of the pixel values in the assignment window to obtain pixel assignmentAssigning pixel values to the central point of the window, and creating matrix D mn ；
The step of determining the side length of the honeycomb cells is performed in a matrix D mn Based on the method, a point with the pixel assignment as the maximum value is found, a square area with the initial side length E=L is established by taking the point as the center, the absolute value Z of the difference value between the assignment of each pixel point on four boundaries of the area and the assignment of the center point is calculated, and the minimum Z value and the coordinate thereof are recorded; then, a new square area is made, the side length E=E+2 pixels are formed, the process is repeated to obtain a new minimum Z value until the minimum Z value has a remarkable reverse increasing trend, and the side length A of the honeycomb cell is solved by obtaining the coordinates of the boundary point of the minimum Z value and the coordinates of the window center point at the moment;
the step "determine vertex threshold" is represented by matrix D mn On the basis of the method, a pixel point with a pixel value of maximum value is found as a center, a square area with a side length of A is divided, absolute values of differences between values of elements on four boundaries of the area and values of elements of a center point are obtained, boundary points with the minimum value are extracted, the point value and the value of the center point are averaged to obtain a value, and the value is determined to be a vertex threshold value;
the step "determines vertices" in matrix D mn On the basis, a pixel point with the maximum value of pixel assignment is found, the pixel point is determined to be a vertex, the pixel number and the coordinates thereof are recorded, then a square area with the side length of A is divided by taking the point as the center, the pixel values of all the pixel points in the area are set to 0, on the basis, the pixel point with the maximum value of pixel assignment is found again, the process is repeated until the maximum value of the residual pixel assignment is smaller than the set vertex threshold value, and vertex extraction is completed.
The step of morphological analysis includes reconstructing cells and evaluating regularity; the step of reconstructing the cell is to connect the extracted vertexes according to the mapping relation between the cell and the vertexes so as to complete honeycomb reconstruction; the step of "regularity evaluation" is to calculate the cell regularity according to the "cellular reconstruction".
The step of binarizing can also adopt an Otsu method to determine a segmentation threshold value T, the value of a pixel with a pixel value smaller than or equal to T in an image is set to 0, and the value of a pixel with a pixel value larger than T in the image is set to 1, so that a binarized image is formed.
The steps of noise reduction filtering and smoothing filtering are respectively arranged before and after the step of binarization; the noise reduction filtering adopts a median filtering method; the smooth filtering adopts a morphological filtering method.
The system comprises:
a system for a window statistics vertex extraction method for cellular regularity detection, the system comprising a detection station, a digital camera, and a computer; the digital camera is electrically connected with the computer (3);
the digital camera is at least one, the resolution is not lower than 1080P, a telecentric lens is arranged, and the installation mode is fixed or/and movable.
The detection platform is a movable working platform and comprises a storage platform, a lifting device and a clamp, wherein the lifting device is arranged at the bottom of the storage platform; placing the honeycomb piece to be tested on a placing table; the lifting device comprises a vertical guide rail, an electric push rod or an electro-hydraulic push rod, and can push the object placing table to move up and down along the vertical guide rail, and the height of the tested honeycomb piece is adjusted to ensure that the upper end face of the tested honeycomb piece is level with the upper end face of the clamp; the control part of the lifting device is electrically connected with the computer;
the fixture consists of four flat plates and a driving device, can be closed towards the honeycomb piece to be tested under the action of the driving device, is locked after being abutted against the honeycomb piece to be tested, and is used for positioning and fixing the honeycomb piece to be tested.
When the installation mode of the digital camera is mobile, the system is additionally provided with a walking portal frame, a sliding rail and a mobile device;
the digital camera is arranged on a beam of the walking type portal frame and can transversely move along the beam under the drive of the moving device;
the walking type portal frame can longitudinally move along the sliding rail under the drive of the moving device, and the movement of the digital camera and the movement of the walking type portal frame are controlled by a computer.
Compared with the prior art, the method and the system thereof have the advantages of being scientific, reasonable, simple and feasible, high in precision, high in efficiency and the like.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of the method step "vertex extraction" of the present invention;
FIG. 3 is a diagram showing a binarized honeycomb product image and its vertex extraction results according to an embodiment of the present invention; wherein (a) is a binarized honeycomb product image, and (b) is an image obtained by extracting vertexes from (a);
FIG. 4 shows a cell reconstruction result according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a device configuration of the system of the present invention;
fig. 6 is a top view of fig. 5.
In the figure: the device comprises a 1-detection platform, a 2-digital camera, a 3-computer, a 4-object placing platform, a 5-lifting device, a 6-clamp, a 7-walking portal frame, an 8-sliding rail and a 9-moving device.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
the method refers to the accompanying figures 1-4:
a window statistical vertex extraction method for detecting the regularity of a honeycomb comprises the steps of identifying an image of a specified honeycomb product, and analyzing and judging the quality level of the honeycomb product; the method sequentially comprises the following steps: acquiring an image, processing the image, extracting vertexes and analyzing morphology; the step of acquiring an image comprises shooting the image and reading the image by a computer;
the step of binarization is arranged between the step of image processing and the step of vertex extraction; the step of binarization is to set the pixel value of the background in the image as 0 and the pixel value of the honeycomb skeleton in the image as 1 to obtain a binarized image;
the step of morphological analysis is based on binarized images, and the geometric deviation degree of the cells is calculated after vertex extraction.
The step of vertex extraction is based on a binarized image, and comprises the following steps of: pixel point numbering, pixel statistics, pixel assignment, determining cell side length, determining vertex threshold and determining vertex;
in the step "pixel number", if the image has m pixels in height and n pixels in width, the pixel number i=1 to k, namely: i has a starting point of P (1, 1) and an ending point of P (m, n);
the step of 'pixel statistics', starting from the pixel number i=1 to the pixel number i=k, when the pixel value of the pixel=1, taking the pixel as a center point, taking l=3 pixels as an initial side length as a square window, if part of the area of the window overflows an image, filling the pixel of the overflow area with pixels with the pixel value=0, then calculating the pixel numbers of the pixel value=0 and the pixel value=1 in the window and storing, if the sum of the pixel numbers of the pixel value=0 in the window is zero, clearing all records, starting from the pixel number i=1 to the pixel number i=k, when the pixel value of the pixel=1, taking the pixel as the center point, taking l=l+2 pixels as the side length as a square window, repeating the steps until the pixel number of the pixel value=0 in all the windows taking the pixel value=1 as the center is larger than zero;
the step of pixel assignment is that on the basis of the step of pixel statistics, a square assignment window with a side length L is sequentially searched for pixel points with a pixel value of 1 as a central point, the sum of the pixel values in the assignment window is calculated to obtain pixel assignment, then the pixel assignment is assigned to the central point of the assignment window, and a matrix D is established based on the pixel assignment mn ；
The step of determining the side length of the honeycomb cells is performed in a matrix D mn Based on the method, a point with the pixel assignment as the maximum value is found, a square area with the initial side length E=L is established by taking the point as the center, the absolute value Z of the difference value between the assignment of each pixel point on four boundaries of the area and the assignment of the center point is calculated, and the minimum Z value and the coordinate thereof are recorded; and then do one againThe new square area, its side length E=E+2 pixels, repeat the above-mentioned process, get the new minimum Z value, until the minimum Z value has apparent trend of increasing reversely, solve the honeycomb cell side length A with the boundary point coordinate and window central point coordinate of the minimum Z value of acquisition at this moment;
the step "determine vertex threshold" is represented by matrix D mn On the basis of the method, a pixel point with a pixel value of maximum value is found as a center, a square area with a side length of A is divided, absolute values of differences between values of elements on four boundaries of the area and values of elements of a center point are obtained, boundary points with the minimum value are extracted, the point value and the value of the center point are averaged to obtain a value, and the value is determined to be a vertex threshold value;
the step "determines vertices" in matrix D mn On the basis, a pixel point with the maximum value of pixel assignment is found, the pixel point is determined to be a vertex, the pixel number and the coordinates thereof are recorded, then a square area with the side length of A is divided by taking the point as the center, the pixel values of all the pixel points in the area are set to 0, on the basis, the pixel point with the maximum value of pixel assignment is found again, the process is repeated until the maximum value of the residual pixel assignment is smaller than the set vertex threshold value, and vertex extraction is completed.
The step of morphological analysis includes reconstructing cells and evaluating regularity; the step of reconstructing the cell is to connect the extracted vertexes according to the mapping relation between the cell and the vertexes so as to complete honeycomb reconstruction; the step of "regularity evaluation" is to calculate the cell regularity according to the "cellular reconstruction".
The step of binarizing can also adopt an Otsu method to determine a segmentation threshold value T, the value of a pixel with a pixel value smaller than or equal to T in an image is set to 0, and the value of a pixel with a pixel value larger than T in the image is set to 1, so that a binarized image is formed.
The steps of noise reduction filtering and smoothing filtering are respectively arranged before and after the step of binarization; the noise reduction filtering adopts a median filtering method; the smooth filtering adopts a morphological filtering method.
The system, with reference to fig. 5,6:
a system for a window statistics vertex extraction method for cellular regularity detection, the system comprising a detection stand 1, a digital camera 2 and a computer 3; the digital camera 2 is electrically connected with the computer 3;
the digital camera 2 is at least one and has a resolution of not lower than 1080P, and is provided with a telecentric lens, and the installation mode is fixed or/and movable.
The detection table 1 is a movable working platform and comprises a storage table 4, a lifting device 5 and a clamp 6, wherein the lifting device 5 is arranged at the bottom of the storage table 4; the honeycomb piece to be tested is placed on the object placing table 4; the lifting device 5 comprises a vertical guide rail, an electric push rod or an electro-hydraulic push rod, and can push the object placing table 4 to move up and down along the vertical guide rail, and the height of the tested honeycomb piece is adjusted so as to ensure that the upper end face of the tested honeycomb piece is level with the upper end face of the clamp 6; the control part of the lifting device 5 is electrically connected with the computer 3;
the clamp 6 consists of four flat plates and a driving device, can be closed towards the honeycomb piece to be tested under the action of the driving device, is locked after being abutted against the honeycomb piece to be tested, and is used for positioning and fixing the honeycomb piece to be tested.
When the installation mode of the digital camera 2 is mobile, the system is additionally provided with a walking portal frame 7, a sliding rail 8 and a mobile device 9;
the digital camera 2 is arranged on a beam of the walking portal frame 7 and can move transversely along the beam under the drive of the moving device 9;
the walking portal frame 7 can longitudinally move along the slide rail 8 under the drive of the moving device 9, and the movement of the digital camera 2 and the walking portal frame 7 are controlled by the computer 3.
Method example 1:
a window statistical vertex extraction method for detecting the regularity of a honeycomb comprises the steps of identifying an image of a specified honeycomb product, and analyzing and judging the quality level of the honeycomb product; the method sequentially comprises the following steps: acquiring an image, processing the image, extracting vertexes and analyzing morphology; the step of acquiring an image comprises shooting the image and reading the image by a computer;
the step of binarization is arranged between the step of image processing and the step of vertex extraction; the step of binarization is to set the pixel value of the background in the image as 0 and the pixel value of the honeycomb skeleton in the image as 1 to obtain a binarized image;
the step of morphological analysis is based on binarized images, and the geometric deviation degree of the cells is calculated after vertex extraction.
Method example 2:
substantially the same as "method example 1", except that: the step of vertex extraction is based on a binarized image, and comprises the following steps of: pixel point numbering, pixel statistics, pixel assignment, determining cell side length, determining vertex threshold and determining vertex;
in the step "pixel number", if the image has m pixels in height and n pixels in width, the pixel number i=1 to k, namely: i has a starting point of P (1, 1) and an ending point of P (m, n);
the step of 'pixel statistics', starting from the pixel number i=1 to the pixel number i=k, when the pixel value of the pixel=1, taking the pixel as a center point, taking l=3 pixels as an initial side length as a square window, if part of the area of the window overflows an image, filling the pixel of the overflow area with pixels with the pixel value=0, then calculating the pixel numbers of the pixel value=0 and the pixel value=1 in the window and storing, if the sum of the pixel numbers of the pixel value=0 in the window is zero, clearing all records, starting from the pixel number i=1 to the pixel number i=k, when the pixel value of the pixel=1, taking the pixel as the center point, taking l=l+2 pixels as the side length as a square window, repeating the steps until the pixel number of the pixel value=0 in all the windows taking the pixel value=1 as the center is larger than zero;
the step of pixel assignment is based on the step of pixel statistics, and a square assignment window with a side length L is sequentially found by taking a pixel point with a pixel value of = 1 as a center pointCalculating the sum of pixel values in the assignment window to obtain pixel assignment, assigning the pixel assignment to the center point of the assignment window, and creating matrix D mn ；
The step of determining the side length of the honeycomb cells is performed in a matrix D mn Based on the method, a point with the pixel assignment as the maximum value is found, a square area with the initial side length E=L is established by taking the point as the center, the absolute value Z of the difference value between the assignment of each pixel point on four boundaries of the area and the assignment of the center point is calculated, and the minimum Z value and the coordinate thereof are recorded; then, a new square area is made, the side length E=E+2 pixels are formed, the process is repeated to obtain a new minimum Z value until the minimum Z value has a remarkable reverse increasing trend, and the side length A of the honeycomb cell is solved by obtaining the coordinates of the boundary point of the minimum Z value and the coordinates of the window center point at the moment;
the step "determine vertex threshold" is represented by matrix D mn On the basis of the method, a pixel point with a pixel value of maximum value is found as a center, a square area with a side length of A is divided, absolute values of differences between values of elements on four boundaries of the area and values of elements of a center point are obtained, boundary points with the minimum value are extracted, the point value and the value of the center point are averaged to obtain a value, and the value is determined to be a vertex threshold value;
the step "determines vertices" in matrix D mn On the basis, a pixel point with the maximum value of pixel assignment is found, the pixel point is determined to be a vertex, the pixel number and the coordinates thereof are recorded, then a square area with the side length of A is divided by taking the point as the center, the pixel values of all the pixel points in the area are set to 0, on the basis, the pixel point with the maximum value of pixel assignment is found again, the process is repeated until the maximum value of the residual pixel assignment is smaller than the set vertex threshold value, and vertex extraction is completed.
Method examples 3,4:
substantially the same as "method examples 1,2", respectively, except that: the step of morphological analysis includes reconstructing cells and evaluating regularity; the step of reconstructing the cell is to connect the extracted vertexes according to the mapping relation between the cell and the vertexes so as to complete honeycomb reconstruction; the step of "regularity evaluation" is to calculate the cell regularity according to the "cellular reconstruction".
Method examples 5-8:
substantially the same as "method examples 1 to 4", respectively, except that: the step of binarizing can also adopt an Otsu method to determine a segmentation threshold value T, the value of a pixel with a pixel value smaller than or equal to T in an image is set to 0, and the value of a pixel with a pixel value larger than T in the image is set to 1, so that a binarized image is formed.
Method examples 9-18:
substantially the same as "method examples 1 to 8", respectively, except that: the steps of noise reduction filtering and smoothing filtering are respectively arranged before and after the step of binarization; the noise reduction filtering adopts a median filtering method; the smooth filtering adopts a morphological filtering method.
System example 1:
a system for a window statistics vertex extraction method for cellular regularity detection, the system comprising a detection stand 1, a digital camera 2 and a computer 3; the digital camera 2 is electrically connected with the computer 3;
the digital camera 2 is at least one and has a resolution of not lower than 1080P, and is provided with a telecentric lens, and the installation mode is fixed or/and movable.
Same example 2:
substantially the same as "system example 1", except that: the detection table 1 is a movable working platform and comprises a storage table 4, a lifting device 5 and a clamp 6, wherein the lifting device 5 is arranged at the bottom of the storage table 4; the honeycomb piece to be tested is placed on the object placing table 4; the lifting device 5 comprises a vertical guide rail, an electric push rod or an electro-hydraulic push rod, and can push the object placing table 4 to move up and down along the vertical guide rail, and the height of the tested honeycomb piece is adjusted so as to ensure that the upper end face of the tested honeycomb piece is level with the upper end face of the clamp 6; the control part of the lifting device 5 is electrically connected with the computer 3;
the clamp 6 consists of four flat plates and a driving device, can be closed towards the honeycomb piece to be tested under the action of the driving device, is locked after being abutted against the honeycomb piece to be tested, and is used for positioning and fixing the honeycomb piece to be tested.
System examples 3,4:
substantially the same as "system examples 1,2", respectively, except that: when the installation mode of the digital camera 2 is mobile, the system is additionally provided with a walking portal frame 7, a sliding rail 8 and a mobile device 9;
the digital camera 2 is arranged on a beam of the walking portal frame 7 and can move transversely along the beam under the drive of the moving device 9;
the walking portal frame 7 can longitudinally move along the slide rail 8 under the drive of the moving device 9, and the movement of the digital camera 2 and the walking portal frame 7 are controlled by the computer 3.
Fig. 2 and 3 are graphs of vertex extraction and cell reconstruction results according to an embodiment of the present invention, wherein the maximum value of the internal angle deviation is 18.02, the average value of the internal angle deviation is 3.3, the standard deviation of the internal angle deviation is 2.97, both of which are within the set value range, the vertex extraction and cell reconstruction effects are good, and the qualification of the product quality can be fully determined.

Claims (11)

1. A window statistical vertex extraction method for detecting the regularity of a honeycomb comprises the steps of identifying an image of a specified honeycomb product, and analyzing and judging the quality level of the honeycomb product; the method sequentially comprises the following steps: acquiring an image, processing the image, extracting vertexes and analyzing morphology; the step of acquiring an image comprises shooting the image and reading the image by a computer; the method is characterized in that:
the step of binarization is arranged between the step of image processing and the step of vertex extraction; the step of binarization is to set the pixel value of the background in the image as 0 and the pixel value of the honeycomb skeleton in the image as 1 to obtain a binarized image;
the step of morphological analysis is based on a binarized image, and the geometric deviation degree of the cell is calculated after vertex extraction;
the step of vertex extraction is based on a binarized image, and comprises the following steps of: pixel point numbering, pixel statistics, pixel assignment, determining cell side length, determining vertex threshold and determining vertex;
in the step "pixel number", if the image has m pixels in height and n pixels in width, the pixel number i=1 to k, namely: i has a starting point of P (1, 1) and an ending point of P (m, n);
the step of 'pixel statistics', starting from the pixel number i=1 to the pixel number i=k, when the pixel value of the pixel=1, taking the pixel as a center point, taking l=3 pixels as an initial side length as a square window, if part of the area of the window overflows an image, filling the pixel of the overflow area with pixels with the pixel value=0, then calculating the pixel numbers of the pixel value=0 and the pixel value=1 in the window and storing, if the sum of the pixel numbers of the pixel value=0 in the window is zero, clearing all records, starting from the pixel number i=1 to the pixel number i=k, when the pixel value of the pixel=1, taking the pixel as the center point, taking l=l+2 pixels as the side length as a square window, repeating the steps until the pixel number of the pixel value=0 in all the windows taking the pixel value=1 as the center is larger than zero;
the step of pixel assignment is that on the basis of the step of pixel statistics, a square assignment window with a side length L is sequentially searched for pixel points with a pixel value of 1 as a central point, the sum of the pixel values in the assignment window is calculated to obtain pixel assignment, then the pixel assignment is assigned to the central point of the assignment window, and a matrix D is established based on the pixel assignment mn ；
The step of determining the side length of the honeycomb cells is performed in a matrix D mn Based on the method, a point with the pixel assignment as the maximum value is found, a square area with the initial side length E=L is established by taking the point as the center, the absolute value Z of the difference value between the assignment of each pixel point on four boundaries of the area and the assignment of the center point is calculated, and the minimum Z value and the coordinate thereof are recorded; then making a new square region with side length E=E+2 pixels, repeating the above-mentioned process to obtain new minimum Z value until the minimum Z value isSolving the side length A of the honeycomb cell by using the coordinates of the boundary point of the minimum Z value and the coordinates of the central point of the window;
the step "determine vertex threshold" is represented by matrix D mn On the basis of the method, a pixel point with a pixel value of the maximum value is found as a center, a square area with a side length of A is divided, absolute values of differences between values of pixels on four boundaries of the area and values of pixels of the center point are obtained, boundary points with the minimum value of the absolute values are extracted, and values obtained by averaging the values of the pixels of the four boundaries and the values of the pixels of the center point are determined to be a vertex threshold value;
the step "determines vertices" in matrix D mn On the basis, a pixel point with the maximum value of pixel assignment is found, the pixel point is determined to be a vertex, the pixel number and the coordinates thereof are recorded, then a square area with the side length of A is divided by taking the point as the center, the pixel values of all the pixel points in the area are set to 0, on the basis, the pixel point with the maximum value of pixel assignment is found again, the process is repeated until the maximum value of the residual pixel assignment is smaller than the set vertex threshold value, and vertex extraction is completed.
2. The method according to claim 1, characterized in that: the step of morphological analysis includes reconstructing cells and evaluating regularity; the step of reconstructing the cell is to connect the extracted vertexes according to the mapping relation between the cell and the vertexes so as to complete honeycomb reconstruction; the step of "regularity evaluation" is to calculate the cell regularity according to the "cellular reconstruction".
3. The method according to claim 1 or 2, characterized in that: the step of binarizing can also adopt an Otsu method to determine a segmentation threshold value T, the value of a pixel with a pixel value smaller than or equal to T in an image is set to 0, and the value of a pixel with a pixel value larger than T in the image is set to 1, so that a binarized image is formed.
4. A method according to claim 3, characterized in that: the step of binarizing can also adopt an Otsu method to determine a segmentation threshold value T, the value of a pixel with a pixel value smaller than or equal to T in an image is set to 0, and the value of a pixel with a pixel value larger than T in the image is set to 1, so that a binarized image is formed.
5. The method according to claim 1 or 2, characterized in that: the steps of noise reduction filtering and smoothing filtering are respectively arranged before and after the step of binarization; the noise reduction filtering adopts a median filtering method; the smooth filtering adopts a morphological filtering method.
6. The method according to claim 2, characterized in that: the steps of noise reduction filtering and smoothing filtering are respectively arranged before and after the step of binarization; the noise reduction filtering adopts a median filtering method; the smooth filtering adopts a morphological filtering method.
7. A method according to claim 3, characterized in that: the steps of noise reduction filtering and smoothing filtering are respectively arranged before and after the step of binarization; the noise reduction filtering adopts a median filtering method; the smooth filtering adopts a morphological filtering method.
8. The method according to claim 4, wherein: the steps of noise reduction filtering and smoothing filtering are respectively arranged before and after the step of binarization; the noise reduction filtering adopts a median filtering method; the smooth filtering adopts a morphological filtering method.
9. A system suitable for use in the method of any one of claims 1-8, the system comprising a detection station (1), a digital camera (2) and a computer (3); the digital camera (2) is electrically connected with the computer (3); the method is characterized in that:
the digital camera (2) is at least one, the resolution is not lower than 1080P, a telecentric lens is arranged, and the installation mode is fixed or/and movable.
10. The system according to claim 9, wherein: the detection table (1) is a movable working platform and comprises a storage table (4), a lifting device (5) and a clamp (6), wherein the lifting device (5) is arranged at the bottom of the storage table (4); the honeycomb piece to be tested is placed on the object placing table (4); the lifting device (5) comprises a vertical guide rail, an electric push rod or an electro-hydraulic push rod, and can push the object placing table (4) to move up and down along the vertical guide rail, and the height of the tested honeycomb piece is adjusted so as to ensure that the upper end face of the tested honeycomb piece is level with the upper end face of the clamp (6); the control part of the lifting device (5) is electrically connected with the computer (3);
the fixture (6) consists of four flat plates and a driving device, can be closed towards the honeycomb piece to be tested under the action of the driving device, is locked after being abutted against the honeycomb piece to be tested, and is used for positioning and fixing the honeycomb piece to be tested.
11. The system according to claim 9 or 10, characterized in that: when the installation mode of the digital camera (2) is mobile, the system is additionally provided with a walking type portal frame (7), a sliding rail (8) and a mobile device (9);
the digital camera (2) is arranged on a cross beam of the walking type portal frame (7) and can transversely move along the cross beam under the drive of the moving device (9);
the walking type portal frame (7) can longitudinally move along the sliding rail (8) under the drive of the moving device (9), and the movement of the digital camera (2) and the movement of the walking type portal frame (7) are controlled by the computer (3).
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