CN113063705A - Diamond wire surface diamond grain quality detection method based on machine vision - Google Patents
Diamond wire surface diamond grain quality detection method based on machine vision Download PDFInfo
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Abstract
The invention discloses a diamond wire surface diamond dust particle quality detection method based on machine vision, which comprises the steps of collecting a surface image of a diamond wire to be detected by using a CCD industrial camera; preprocessing the collected surface image by using methods such as ROI clipping, image filtering and the like; extracting the emery particle regions in the surface image by using methods such as connected domain detection, roundness detection, region area detection and the like for the preprocessed surface image, classifying the emery particle regions into two types of agglomeration and normal, and performing region type judgment and quantity statistics; performing three-dimensional reconstruction on the extracted surface image information to obtain a diamond wire surface stereo image; and performing data storage and front-end interface display on the acquired diamond wire surface diamond particle information and the three-dimensional image. The method can detect the number and the agglomeration condition of the diamond grains on the surface of the diamond wire and the distribution condition of the diamond grains on the surface of the three-dimensional recovery diamond wire, further detect the surface quality of the diamond wire and realize the automatic detection and data archiving of the surface quality in the production of the diamond wire.
Description
Technical Field
The invention relates to the technical field of diamond wire surface quality detection, in particular to a diamond wire surface diamond grain quality detection method based on machine vision.
Background
The diamond wire is widely used for cutting noble materials such as solar silicon wafers, semiconductors, LEDs, precious stones, optical glass and the like, has the remarkable advantages of high cutting efficiency and high precision, and has the characteristics of wear resistance, long service life, environmental protection and the like and wide market prospect compared with the traditional cutting sand wire. In cutting, the quality of the surface of the diamond wire directly influences the efficiency and the quality of cutting, and the quality of the diamond wire mainly depends on the quantity, the size, the agglomeration condition and the spatial distribution condition of diamond grains attached to the surface
At present, the existing diamond wire surface quality detection methods at home and abroad mainly comprise a manual observation method and a machine vision-based method. The existing method based on machine vision mainly identifies the number of diamond grains, has single function, and cannot acquire the agglomeration condition and the spatial distribution condition of the diamond grains on the surface of the diamond wire.
Disclosure of Invention
The invention aims to provide a diamond wire surface diamond grain quality detection method based on machine vision, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a diamond wire surface diamond dust particle quality detection method based on machine vision comprises the following steps:
s101, acquiring an image of the surface of the diamond wire;
s102, surface image preprocessing;
s103, extracting and counting the agglomerated areas of the emery particles in the surface image;
s104, extracting and counting the normal areas of the emery particles in the surface image;
s105, three-dimensional reconstruction of a surface image;
and S106, storing and displaying the surface quality data of the diamond wire.
Further, in the step S101, a CCD industrial camera is used for image acquisition of the diamond wire surface, the resolution of the image acquired by the CCD industrial camera is 656 × 488 pixels, 96dpi, and the bit depth is 8. By testing and comparing several CCD industrial cameras commonly available in the market, the parameter camera can meet the requirement of subsequent image processing and has lower cost.
Further, the step S102 is to pre-process the surface image of the diamond wire acquired by the CCD industrial camera in S101, and includes the following steps:
s1021, carrying out binarization on the original image by adopting a prior carborundum particle gray value interval;
s1022, performing expansion operation on the image by using the rectangular structural elements;
s1023, ROI clipping is carried out on the expanded target area in the image;
and S1024, performing contrast enhancement processing on the image.
Further, after the diamond wire surface image is preprocessed in step S103, the extraction and quantity statistics of the diamond sand particle agglomeration region in the image are required, which includes the following steps:
s1031, performing mean value filtering on the image by using rectangular structural elements;
s1032, performing dynamic threshold segmentation on the image by using a gray threshold;
s1033, performing regional corrosion operation on the image twice by using the circular structural elements;
s1034, performing region division on the image based on the connected domain;
s1035, selecting an area-based area for the image by using an area threshold;
s1036, performing filling operation on the selected area;
s1037, carrying out area selection based on roundness on the selected area by using a roundness threshold value;
s1038, obtaining the number of the agglomeration areas in the image based on the connected domain statistics.
Further, after the diamond wire surface image is preprocessed in step S104, extraction and quantity statistics of normal regions of diamond grit particles in the image are required, including the following steps:
s1041, performing band-pass filtering processing on the image obtained in the S1024 by using a 'lines' type filter;
s1042, performing binarization segmentation on the image by using a gray threshold;
s1043, performing filling operation on the image;
s1044, carrying out difference operation on the image and the agglomeration area obtained in S1037 to obtain a normal area image;
s1045, performing area division on the image based on the connected domain;
s1046, area-based area selection is carried out on the selected area by using an area threshold;
s1047, obtaining the number of normal areas in the image based on connected domain statistics.
Further, after the step S105 extracts the corundum particle aggregation region and the normal region in the diamond wire surface image, the three-dimensional reconstruction of the diamond wire surface image is required, which includes the following steps:
s1051, combining the carborundum particle agglomeration area obtained in the step S103 and the carborundum particle normal area obtained in the step S104 into a new particle distribution image;
s1052, constructing X-axis and Y-axis coordinates of a three-dimensional image by using horizontal and vertical pixels of a distributed image;
s1053, constructing a Z-axis coordinate of the three-dimensional image according to gray values of pixel points of the distributed image;
and S1054, rendering the diamond wire surface image three-dimensional restoration image by using three-dimensional coordinates.
Further, in the step S106, the surface quality data of the steel wire is stored and displayed, and the storage of the data, the display of the data modeling, and the display of the front end face are completed by controlling the data through the industrial personal computer.
Compared with the prior art, the invention has the beneficial effects that:
1. the method based on machine vision can realize the automatic detection of the surface quality of the diamond wire, greatly improve the detection efficiency and reduce the labor cost.
2. The method can extract two types of the emery agglomeration area and the normal area in the surface image of the diamond wire, and respectively carry out quantity statistics.
3. The invention carries out three-dimensional reconstruction on the distribution of the carborundum particles on the diamond wire and visually shows the three-dimensional distribution condition of the carborundum particles.
4. The invention also designs the storage of the diamond wire surface diamond grain quality data and the three-dimensional image and the display of the front end interface in the industrial personal computer, and realizes the automatic detection of the surface quality and the data archiving in the diamond wire production.
In conclusion, the method can detect the quantity and the agglomeration condition of the diamond grains on the surface of the diamond wire and the distribution condition of the diamond grains on the surface of the three-dimensional restoration diamond wire, further detect the surface quality of the diamond wire, store the quality data of the diamond grains on the surface of the diamond wire and three-dimensional images in an industrial personal computer and display a front-end interface, and realize the automatic detection and data archiving of the surface quality in the production of the diamond wire.
Drawings
FIG. 1 is a schematic diagram of the structure of the present invention;
FIG. 2 is an original image of the surface of a diamond wire acquired by a CCD industrial camera according to the present invention;
FIG. 3 is a schematic diagram illustrating the principle of diamond wire surface image preprocessing according to the present invention;
FIG. 4 is a pre-processed image obtained after pre-processing a diamond wire surface image in accordance with the present invention;
FIG. 5 is a schematic diagram of the diamond grain agglomeration region extraction and quantity statistics of the present invention;
FIG. 6 is an image of an area where emery particles agglomerate in a pre-processed image according to the present invention;
FIG. 7 is a schematic diagram of the extraction and quantity statistics of the normal region of emery particles according to the present invention;
FIG. 8 is an image of a normal region of emery particles from a pre-processed image according to the present invention;
FIG. 9 is a schematic diagram of the principle of three-dimensional reconstruction of a diamond wire surface image according to the present invention;
fig. 10 is a three-dimensional restoration image of a diamond wire surface image according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-10, the present invention provides a technical solution:
a diamond wire surface diamond dust particle quality detection method based on machine vision comprises the following steps:
s101, acquiring an image of the surface of the diamond wire;
s102, surface image preprocessing;
s103, extracting and counting the agglomerated areas of the emery particles in the surface image;
s104, extracting and counting the normal areas of the emery particles in the surface image;
s105, three-dimensional reconstruction of a surface image;
and S106, storing and displaying the surface quality data of the diamond wire.
In the invention, a CCD industrial camera is used for collecting the diamond wire surface image in the step S101, the resolution of the image collected by the CCD industrial camera is 656 × 488 pixels, 96dpi, and the bit depth is 8. By testing and comparing several CCD industrial cameras commonly available in the market, the parameter camera can meet the requirement of subsequent image processing and has lower cost.
In the present invention, the step S102 is to pre-process the surface image of the diamond wire acquired by the CCD industrial camera in S101, and includes the following steps:
s1021, carrying out binarization on the original image by adopting a prior carborundum particle gray value interval;
s1022, performing expansion operation on the image by using the rectangular structural elements;
s1023, ROI clipping is carried out on the expanded target area in the image;
and S1024, performing contrast enhancement processing on the image.
In the invention, after the diamond wire surface image is preprocessed in step S103, the extraction and quantity statistics of the diamond sand particle agglomeration region in the image are required, and the method comprises the following steps:
s1031, performing mean value filtering on the image by using rectangular structural elements;
s1032, performing dynamic threshold segmentation on the image by using a gray threshold;
s1033, performing regional corrosion operation on the image twice by using the circular structural elements;
s1034, performing region division on the image based on the connected domain;
s1035, selecting an area-based area for the image by using an area threshold;
s1036, performing filling operation on the selected area;
s1037, carrying out area selection based on roundness on the selected area by using a roundness threshold value;
s1038, obtaining the number of the agglomeration areas in the image based on the connected domain statistics.
In the present invention, after the diamond wire surface image is preprocessed in step S104, extraction and quantity statistics of normal regions of emery particles in the image are required, including the following steps:
s1041, performing band-pass filtering processing on the image obtained in the S1024 by using a 'lines' type filter;
s1042, performing binarization segmentation on the image by using a gray threshold;
s1043, performing filling operation on the image;
s1044, carrying out difference operation on the image and the agglomeration area obtained in S1037 to obtain a normal area image;
s1045, performing area division on the image based on the connected domain;
s1046, area-based area selection is carried out on the selected area by using an area threshold;
s1047, obtaining the number of normal areas in the image based on connected domain statistics.
In the present invention, after the step S105 of extracting the statistics of the emery particle aggregation region and the normal region in the image from the diamond wire surface image, three-dimensional reconstruction of the diamond wire surface image needs to be performed, the method includes the following steps:
s1051, combining the carborundum particle agglomeration area obtained in the step S103 and the carborundum particle normal area obtained in the step S104 into a new particle distribution image;
s1052, constructing X-axis and Y-axis coordinates of a three-dimensional image by using horizontal and vertical pixels of a distributed image;
s1053, constructing a Z-axis coordinate of the three-dimensional image according to gray values of pixel points of the distributed image;
and S1054, rendering the diamond wire surface image three-dimensional restoration image by using three-dimensional coordinates.
In the invention, the surface quality data of the steel wire in the step S106 is stored and displayed, and the storage of the data, the display of data modeling and the display of the front end face are finished by controlling through the industrial personal computer.
Examples of the following,
A diamond wire surface diamond dust particle quality detection method based on machine vision comprises the following steps:
s101, acquiring an image of the surface of the diamond wire;
as shown in fig. 2, for an original image of the surface of the diamond wire, a CCD industrial camera is used for acquisition, the resolution of the image acquired by the camera is 656 × 488 pixels, 96dpi, and the bit depth is 8.
S102, surface image preprocessing;
as shown in fig. 3, a schematic diagram of a principle of diamond wire surface image preprocessing specifically includes:
s1021, carrying out binarization on the original image by adopting a prior carborundum particle gray value interval;
s1022, performing expansion operation on the image by using 656 × 20 rectangular structural elements;
s1023, ROI clipping is carried out on the expanded target area in the image;
and S1024, performing contrast enhancement processing on the image.
The prior emery particle gray value interval is an average effect optimal value obtained through multiple experiments, the calculation speed of subsequent image processing can be greatly increased by cutting the target area ROI of the image, and the effect that the subsequent image processing flow can be optimized by carrying out binarization and contrast enhancement on the original image can be obtained through multiple experiments. As shown in fig. 4, the preprocessed image is obtained by preprocessing the surface image of one diamond wire.
S103, extracting and counting the agglomerated areas of the emery particles in the surface image;
as shown in fig. 5, a schematic diagram of the principles of diamond grit particle agglomeration region extraction and quantity statistics in the diamond wire surface image after preprocessing is performed on the diamond wire surface image, specifically includes:
s1031, performing mean filtering on the image by using 8-by-8 pixel rectangular structural elements;
s1032, performing dynamic threshold segmentation on the image by using a 2-gray threshold;
s1033, performing regional corrosion operation on the image twice by using 1-pixel circular structural elements;
s1034, performing region division on the image based on the connected domain;
s1035, performing area-based region selection on the image using an area threshold 50;
s1036, performing filling operation on the selected area;
s1037, carrying out roundness-based area selection on the selected area by using a roundness threshold value of 0.6;
s1038, obtaining the number of the agglomeration areas in the image based on the connected domain statistics.
The rectangular structure parameter, the gray level threshold value, the circular structure parameter, the area threshold value and the roundness threshold value are average optimal values determined after multiple experiments, in addition, the average filtering and dynamic threshold value segmentation are determined through multiple experiment comparisons, the best particle region dividing effect in the image can be achieved, the method is superior to the traditional watershed method, the fixed threshold value segmentation method and other methods, and in addition, the normal region and the agglomeration region of particles in the image can be effectively distinguished through region selection based on the roundness.
As shown in fig. 6, an image is extracted for the agglomerated region of the emery particles in the image, and the number of the agglomerated regions is automatically counted to be 5.
S104, extracting and counting the normal areas of the emery particles in the surface image;
as shown in fig. 7, after the diamond wire surface image is preprocessed, a schematic diagram of the extraction and quantity statistics principle of the normal region of the diamond sand particles in the image is obtained, which specifically includes:
s1041, performing band-pass filtering processing on the image obtained in the S1024 by using a 'lines' type filter;
s1042, performing binarization segmentation on the image by using a 253 gray threshold;
s1043, performing filling operation on the image;
s1044, carrying out difference operation on the image and the agglomeration area obtained in S1037 to obtain a normal area image;
s1045, performing area division on the image based on the connected domain;
s1046, area-based area selection is carried out on the selected area by using an area threshold 4;
s1047, obtaining the number of normal areas in the image based on connected domain statistics.
The filter type, the gray level threshold value and the area threshold value are average optimal values determined after multiple experiments, and in addition, through multiple experimental comparisons, the best effect can be obtained by using a 'lines' type filter for band-pass filtering when the image is subjected to filtering processing.
As shown in fig. 8, the image is extracted for the normal region of the emery particles in the image, and the number of the agglomerated regions is automatically counted to be 136.
S105, three-dimensional reconstruction of a surface image;
as shown in fig. 9, after extracting the emery particle aggregation region and the normal region in the image for the diamond wire surface image, a schematic diagram of the principle of three-dimensional reconstruction of the diamond wire surface image is performed, which specifically includes:
s1051, combining the carborundum particle agglomeration area obtained in S103 and the carborundum particle normal area obtained in S104 into a new particle distribution image;
s1052, constructing X-axis and Y-axis coordinates of a three-dimensional image by using horizontal and vertical pixels of a distributed image;
s1053, constructing a Z-axis coordinate of the three-dimensional image according to gray values of pixel points of the distributed image;
and S1054, rendering the diamond wire surface image three-dimensional restoration image by using three-dimensional coordinates.
As shown in fig. 10, the figure shows a three-dimensional restoration image of the surface image of the diamond wire.
And S106, storing and displaying the surface quality data of the diamond wire.
The diamond wire surface image is subjected to diamond particle area agglomeration, normal area extraction and quantity statistics, and after the diamond wire surface image is subjected to three-dimensional reconstruction, diamond wire surface diamond particle quality data and three-dimensional images need to be stored in an industrial personal computer and a front end interface needs to be displayed.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. A diamond wire surface diamond grain quality detection method based on machine vision is characterized by comprising the following steps:
s101, acquiring an image of the surface of the diamond wire;
s102, surface image preprocessing;
s103, extracting and counting the agglomerated areas of the emery particles in the surface image;
s104, extracting and counting the normal areas of the emery particles in the surface image;
s105, three-dimensional reconstruction of a surface image;
and S106, storing and displaying the surface quality data of the diamond wire.
2. The diamond wire surface diamond dust particle quality detection method based on machine vision according to claim 1, characterized in that: the step S102 includes the steps of:
s1021, carrying out binarization on the original image by adopting a prior carborundum particle gray value interval;
s1022, performing expansion operation on the image by using the rectangular structural elements;
s1023, ROI clipping is carried out on the expanded target area in the image;
and S1024, performing contrast enhancement processing on the image.
3. The diamond wire surface diamond dust particle quality detection method based on machine vision according to claim 1, characterized in that: the step S103 includes the steps of:
s1031, performing mean value filtering on the image by using rectangular structural elements;
s1032, performing dynamic threshold segmentation on the image by using a gray threshold;
s1033, performing regional corrosion operation on the image twice by using the circular structural elements;
s1034, performing region division on the image based on the connected domain;
s1035, selecting an area-based area for the image by using an area threshold;
s1036, performing filling operation on the selected area;
s1037, carrying out area selection based on roundness on the selected area by using a roundness threshold value;
s1038, obtaining the number of the agglomeration areas in the image based on the connected domain statistics.
4. The diamond wire surface diamond dust particle quality detection method based on machine vision according to claim 2, characterized in that: the step S104 includes the steps of:
s1041, performing band-pass filtering processing on the image obtained in the S1024 by using a 'lines' type filter;
s1042, performing binarization segmentation on the image by using a gray threshold;
s1043, performing filling operation on the image;
s1044, carrying out difference operation on the image and the agglomeration area obtained in S1037 to obtain a normal area image;
s1045, performing area division on the image based on the connected domain;
s1046, area-based area selection is carried out on the selected area by using an area threshold;
s1047, obtaining the number of normal areas in the image based on connected domain statistics.
5. The diamond wire surface diamond dust particle quality detection method based on machine vision according to claim 1, characterized in that: the step S105 includes the steps of:
s1051, combining the carborundum particle agglomeration area obtained in the step S103 and the carborundum particle normal area obtained in the step S104 into a new particle distribution image;
s1052, constructing X-axis and Y-axis coordinates of a three-dimensional image by using horizontal and vertical pixels of a distributed image;
s1053, constructing a Z-axis coordinate of the three-dimensional image according to gray values of pixel points of the distributed image;
and S1054, rendering the diamond wire surface image three-dimensional restoration image by using three-dimensional coordinates.
6. The diamond wire surface diamond dust particle quality detection method based on machine vision according to claim 1, characterized in that: and in the step S101, a CCD industrial camera is used for collecting the diamond wire surface image, the resolution of the image collected by the CCD industrial camera is 656 × 488 pixels, 96dpi, and the bit depth is 8.
7. The diamond wire surface diamond dust particle quality detection method based on machine vision according to claim 1, characterized in that: and S106, storing and displaying the surface quality data of the steel wire, and controlling by using an industrial personal computer to finish the storage of data, the display of data modeling and the display of the front end face.
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