CN111060540A - Automatic identification method for inclusion particles in automatic inclusion identification system - Google Patents
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- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
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Abstract
The invention discloses an automatic identification method of inclusion particles in an automatic inclusion identification system, which comprises the following steps: adjusting working parameters, and shooting a back scattering diagram of the sample to be detected in the current view field range; calculating a gray level histogram of the backscatter image, counting waveform change characteristics of the gray level histogram, and merging adjacent waveform intervals meeting requirements; calculating the integral of the number of pixels in each waveform interval by adopting an integral operation method, and extracting a background gray scale range according to the waveform interval with a larger integral value; and carrying out binarization processing on the back scattering image according to the background gray scale range to extract impurity particles. The invention solves the problem that the inclusion particles cannot be identified due to unreasonable parameter setting of the electronic scanning mirror, and shows automatic identification of the inclusion particles.
Description
Technical Field
The invention relates to the technical field of smelting, in particular to an automatic identification method of inclusion particles in an automatic inclusion identification system.
Background
The automatic inclusion classification system continuously and automatically acquires information of a plurality of back scattering patterns (BSE) and X-Ray (X-Ray) of a steel sample, and the types of inclusions in the steel sample are obtained after analysis. The primary purpose of obtaining a backscatter map is to identify inclusion particles in the sample; and after the inclusion particles are identified, obtaining X-Ray information of the inclusion particles, and identifying the type of the inclusion according to the spectral peak characteristics of each element in the X-Ray information of the inclusion.
The background gray value of a steel sample is preset by the existing inclusion particle identification algorithm based on a backscattering image, a binarization method is used according to the background gray range, namely, pixels belonging to the background gray are set as the background, and pixels not in the background gray range are identified as inclusion particles. Because the working condition of the electronic scanning mirror is easily influenced by the environment, when a plurality of backscatter images are continuously shot, the gray value, the brightness value and the contrast information of each backscatter image change, so that the position of the inclusion particles identified and extracted by using the fixed background gray value is inaccurate, and the accuracy of the subsequent X-Ray information acquisition is directly influenced.
Disclosure of Invention
In order to solve the technical problems, the invention provides an automatic identification method of inclusion particles in an automatic inclusion identification system.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an automatic identification method for inclusion particles in an automatic inclusion identification system comprises the following steps:
s1, adjusting working parameters, shooting a back scattering diagram of the sample to be detected in the current view range, and calculating a gray level histogram of the back scattering diagram;
s2: counting waveform change characteristics of the gray level histogram of the backscatter image, and merging adjacent waveform intervals meeting requirements;
s3, calculating the integral of the number of pixels in each waveform interval by adopting an integral operation method, and extracting a background gray scale range according to the waveform interval with a larger integral value;
s4: and carrying out binarization processing on the back scattering image according to the background gray scale range to extract impurity particles.
Preferably, the step S1 is preceded by the following steps:
s5: preparing a sample to be measured, and calculating the vision field number of the sample to be measured according to the actual size of the sample to be measured.
Preferably, the step S4 is followed by the following steps:
s6: and repeating the steps S1 to S4 until the sample to be detected in all the vision field ranges is measured, so that all the inclusion particles of the sample to be detected can be identified.
Preferably, the merging of the adjacent waveform intervals meeting the requirement is to count peaks in each waveform interval, and when the peaks in the adjacent waveform intervals are the same and the distance between the peaks is within 10 gray-scale values, the adjacent waveform intervals are merged.
Preferably, the operating parameters include resolution, magnification, acceleration voltage and beam current.
Based on the technical scheme, the invention has the beneficial effects that: the method is adaptive to different gray values of each frame of steel sample back scattering image, the background gray range is extracted, the automatic identification of the inclusion particles is realized by using a binarization method, and the problem that the inclusion particles cannot be identified due to unreasonable parameter setting of an electronic scanning mirror is solved.
Drawings
FIG. 1: a flow chart of an automatic identification method of inclusion particles in an automatic inclusion identification system;
FIG. 2: in the first embodiment of the automatic identification method of the inclusion particles in the automatic inclusion identification system, the back scattering diagram of the steel sample is shown;
FIG. 3: in the first embodiment of the automatic identification method of the inclusion particles in the automatic identification system of the inclusions, the background gray scale range is obtained by calculating the steel sample;
FIG. 4: the invention discloses a steel sample inclusion particle recognition result in an embodiment I of an automatic inclusion particle recognition method in an automatic inclusion recognition system.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Example one
The invention provides an automatic identification method of inclusion particles in an automatic inclusion identification system, which takes a steel sample as an example and is described as follows:
setting a measuring area for a steel sample, dividing the measuring area into a plurality of measuring intervals according to the size of the measuring area and working parameters of an electronic scanning mirror, wherein the size of each measuring interval is consistent with the size of an actual object reflected by the shooting of the electronic scanning mirror (view range);
setting the working position of an electronic scanning mirror, shooting a back scattering diagram of a measurement interval, mainly collecting the time of a back scattering signal, and shooting the resolution of the obtained back scattering diagram;
the gray value range of one back scattering image is 0-255, the gray value reflects the average atomic weight of a tested substance, the gray value of a substance with large average atomic weight is high, and the gray value of a substance with small average atomic weight is low. The distribution of the inclusion particles can be determined by analyzing the gray distribution of the backscatter image, and therefore, a gray histogram of the backscatter image is calculated, that is, the number and value of pixels of each gray value are counted. The basic substance in the steel sample is iron, the average atomic weight of the iron is larger than that of various non-metallic inclusions, the iron is white in a back scattering diagram, and the gray scale range of the iron obtained in a large-value area can be observed;
the waveform change characteristic of the gray level histogram of the statistical backscatter image, namely the relationship between the rising edge and the falling edge of the curve of the statistical histogram, is that a peak range is the region between two adjacent falling edges, namely the last falling edge is the beginning of the interval and the adjacent falling edge is the end of the interval. In order to ensure that the rising edge and the falling edge appear in pairs, a zero is added before and after the histogram;
taking the small fluctuation as an area, counting the peak values in each waveform interval, and merging two adjacent waveform intervals when the peak values of the adjacent waveform intervals are the same and the distance between the adjacent peak values is smaller (10 gray values are set in the test);
calculating the pixel number integral of each waveform interval by adopting an integral operation method, reserving the first 5 intervals with larger integral values as background alternative intervals due to richer iron pixels of the steel sample, and using 150 as a threshold value in the areas with higher values due to high background pixel values of the iron sample to obtain the largest interval larger than 150 as a background area;
carrying out binarization operation on the backscatter image, setting a pixel to be 0 to represent a background when the pixel gray level is in a background area, and setting a pixel to be 1 to represent inclusion particles when the pixel gray level is not in the background area, and extracting the inclusion particles;
and repeating the steps until other measuring intervals are measured, and then extracting all the inclusion particles in the steel sample to finish the whole automatic identification process.
The above description is only a preferred embodiment of the method for automatically identifying inclusion particles in an automatic inclusion identification system disclosed in the present invention, and is not intended to limit the scope of the embodiments of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the embodiments of the present disclosure should be included in the protection scope of the embodiments of the present disclosure.
Claims (5)
1. An automatic identification method for inclusion particles in an automatic inclusion identification system is characterized by comprising the following steps:
s1: adjusting working parameters, and shooting a back scattering diagram of the sample to be detected in the current view field range;
s2: calculating a gray level histogram of the backscatter image, counting waveform change characteristics of the gray level histogram, and merging adjacent waveform intervals meeting requirements;
s3: calculating the integral of the number of pixels in each waveform interval by adopting an integral operation method, and extracting a background gray scale range according to the waveform interval with a larger integral value;
s4: and carrying out binarization processing on the back scattering image according to the background gray scale range to extract impurity particles.
2. The method for automatically identifying inclusion particles in an inclusion automatic identification system according to claim 1, wherein the step S1 is preceded by the steps of:
s5: preparing a sample to be measured, and calculating the vision field number of the sample to be measured according to the actual size of the sample to be measured.
3. The method for automatically identifying inclusion particles in an inclusion automatic identification system according to claim 2, wherein the step S4 is further followed by the steps of:
s6: and repeating the steps S1 to S4 until the sample to be detected in all the vision field ranges is measured, so that all the inclusion particles of the sample to be detected can be identified.
4. The method of claim 1, wherein the step of combining the adjacent waveform segments is to count the peak values in each waveform segment, and when the peak values in the adjacent waveform segments are the same and the distance between the peak values is within 10 gray-scale values, the adjacent waveform segments are combined.
5. The method of claim 1, wherein the operating parameters include resolution, magnification, acceleration voltage and electron beam current.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112730476A (en) * | 2020-12-18 | 2021-04-30 | 核工业北京地质研究院 | Mineral purity detection method |
| CN114252371A (en) * | 2021-12-24 | 2022-03-29 | 欧波同科技产业有限公司 | Method for simultaneously obtaining different gray scale particles in inclusion analysis system |
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| CN114252371A (en) * | 2021-12-24 | 2022-03-29 | 欧波同科技产业有限公司 | Method for simultaneously obtaining different gray scale particles in inclusion analysis system |
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