CN108335285A - A kind of diamond abrasive grain wear rate assay method based on image procossing - Google Patents

A kind of diamond abrasive grain wear rate assay method based on image procossing Download PDF

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CN108335285A
CN108335285A CN201810040362.1A CN201810040362A CN108335285A CN 108335285 A CN108335285 A CN 108335285A CN 201810040362 A CN201810040362 A CN 201810040362A CN 108335285 A CN108335285 A CN 108335285A
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image
abrasive grain
area
segmentation
wear rate
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CN108335285B (en
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方从富
林燕芬
胡中伟
徐西鹏
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Huaqiao University
Xiamen Institute of Technology
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Xiamen Institute of Technology
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G06T5/30—Erosion or dilatation, e.g. thinning
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/77—Retouching; Inpainting; Scratch removal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
    • 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
    • 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
    • G06T2207/20036—Morphological image processing
    • 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
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Abstract

The present invention provides the diamond abrasive grain wear rate assay methods based on image procossing, first, medium filtering and gaussian filtering are carried out to diamond abrasive grain image, obtain the enhancement metal hard rock Debris Image being polymerized by medium filtering image and gaussian filtering image, abrasive grain region segmentation is carried out to enhancement metal hard rock Debris Image using edge detection and morphological method, and extracts the abrasive grain area image of segmentation；Secondly, medium filtering pretreatment is carried out to the abrasive grain area image of extraction, continues with edge detection and morphological method and eroded area segmentation is carried out to abrasive grain, and extracts the eroded area image of segmentation；Finally, the method counted using area pixel calculates separately abrasive grain region area, eroded area area and abrasive wear rate, completes diamond abrasive grain wear rate and measures.The present invention overcomes the deficiencies of previous artificial qualitatively observation diamond abrasive grain abrasion, realize the degree of wear of quantitative measurment diamond abrasive grain, the accuracy and efficiency of measurement all gets a promotion.

Description

A kind of diamond abrasive grain wear rate assay method based on image procossing
Technical field
The present invention relates to diamond tool Debris Image detection fields, more particularly to a kind of Buddha's warrior attendant based on image procossing Stone mill grain wear rate assay method.
Background technology
Diamond tool is widely used in the hard brittleness difficulty processing material such as ceramics, optical crystal, semiconductor and hard alloy The processing of material.In process, diamond abrasive grain and workpiece and chip occur it is complicated cut, plough pears, scratching, impact etc. it is dry Relate to effect.With the progress of process, diamond abrasive grain will be caused different degrees of abrasion occur.Diamond abrasive grain is in tool The abrasion condition on surface has important influence to machined surface quality, processing efficiency and life tools etc..In order to keep away as possible Exempt from diamond abrasive grain too early or heel and toe wear, raising processing quality and processing efficiency, extension diamond tool are processed the service life, needed The abrasion condition of diamond abrasive grain is measured, process parameter is optimized according to measurement result and improve Buddha's warrior attendant lapicide Has preparation process.
Currently, the method for commonly measuring diamond abrasive grain abrasion condition is artificial microscopic inspection, artificial microexamination Method is mainly using microscopic system come the abrasion condition of artificial sampling observation Abrasive Grain in Diamond Tools, in conjunction with what is pre-established Abrasive wear degree Category criteria, the abrasion classification of artificial observation assessment diamond tool surface sampling abrasive grain and its corresponding mill Abrasive grain quantity is damaged, diamond abrasive grain abrasion condition is completed and measures.Due to the artificial observation of artificial microscopic inspection needs dependence and in advance Abrasion Category criteria is first formulated, not only determination efficiency is low for this method, due also to the subjectivity of manual evaluation is strong and causes to measure knot The accuracy of fruit is not high, it is difficult to accurate evaluation diamond tool effectiveness.
In diamond abrasive grain detection method based on image procossing, Gong Junfeng etc. is proposed based on depth form focus Debris Image Snake model split plot designs, this method first passes through depth form focus mode and generates the cohesive image of diamond abrasive grain and height matrix, Diamond abrasive grain image is split using Snake models again.This method is only extracted diamond abrasive grain profile, and adopts The efficiency that diamond abrasive grain image is generated with depth form focus mode is low.Wu Wenyi etc. proposes the abrasive grain figure based on secondary histogram As thresholding method, this method has also only divided diamond abrasive grain region, and the image of segmentation still contains certain ambient noise, The conspicuousness of segmentation is not high, and this method is to be suitable for grey level histogram to have the Debris Image point of apparent peak valley feature It cuts.In addition, Miao Jingjing etc. proposes the method based on particle swarm optimization algorithm extraction diamond abrasive grain edge, this method is also only real The extraction at diamond abrasive grain edge is showed.Since there are more complicated background texture, abrasive wear lines for diamond abrasive grain image Reason and noise etc., existing diamond abrasive grain detection method still concentrate on the extraction segmentation at abrasive grain edge, also fail to effectively Evaluating abrasive particle abrasion condition.
Invention content
The technical problem to be solved in the present invention is, for the efficiency of artificial microscopy evaluation diamond abrasive grain state of wear It is low, subjectivity is big, and the diamond abrasive grain detection method based on image procossing is also failed to enough effectively to measure abrasive wear etc. and be asked Topic provides a kind of determination efficiency and the high diamond abrasive grain wear rate assay method based on image procossing of accuracy.
In order to achieve the object, the present invention adopts the following technical scheme that：
A kind of diamond abrasive grain wear rate assay method based on image procossing, by abrasive grain region segmentation, eroded area point It cuts and measures three step compositions with abrasive wear rate；
The abrasive grain region segmentation is by the processing of enhancement metal hard rock Debris Image, edge detection and Morphological scale-space group At, the specific steps are：
1) medium filtering is carried out respectively to the noisy diamond abrasive grain image I (x, y) of gray processing and gaussian filtering pre-processes, Obtain image I after medium filteringmImage I after (x, y) and gaussian filteringg(x, y) forms enhanced diamond abrasive grain by polymerization Image Ie(x,y)；
The enhancement metal hard rock Debris Image is defined as follows：
2) to enhancement metal hard rock Debris Image Ie(x, y) carries out edge detection, chooses detection threshold value λ, obtains target mill The contour edge bianry image I of grain imagegb(x,y)；
The edge detection is First-order Gradient amplitude detection, and gradient amplitude M (x, y) is defined as：
The detection threshold value λ is adaptive threshold, and threshold value lambda definition is：
In formula (3)：M and N is respectively the ranks pixel number of image.
3) the profile side of opening operation that morphological dilations and erosion operator are constituted and closed operation to target Debris Image is utilized Edge bianry image Igb(x, y) carries out morphologic filtering, removes unrelated background noise and texture edge；
4) holes filling and image cropping function is utilized to detach target abrasive grain, the abrasive grain area image divided；
The eroded area segmentation is made of medium filtering, gradient detection and Morphological scale-space, the specific steps are：
1) medium filtering pretreatment is carried out to the Debris Image of extraction, removal abrasive particle surface draws because of environment such as chip, dusts The noises such as the random grey white point risen；
2) edge detection is carried out to the pretreated Debris Image of medium filtering, edge detection method and threshold value choose the formula of pressing (2) it is calculated with formula (3), obtains the contour edge bianry image I of target abrasion imagewb(x,y)；
3) utilize the opening operation and closed operation that morphological dilations are constituted with erosion operator to bianry image Iwb(x, y) carries out shape State filters, and removes unrelated noise；
4) holes filling and image cropping function separation target is utilized to wear, the eroded area image divided；
The abrasive wear measurement is made of calculating abrasive grain region area, eroded area area and abrasive wear rate, The specific steps are：
1) the abrasive grain area image based on segmentation, the method counted using area pixel calculate abrasive grain region area Sg；Institute The abrasive grain region area S statedgIt is defined as：
Sg=a × SUMpgFormula (4)
In formula (4)：A is the correction factor of real area representated by unit pixel, SUMpgFor total picture contained by abrasive grain region Element value；If a takes 1, show directly using total pixel value contained by abrasive grain region；
2) the eroded area image based on segmentation, the method counted using area pixel calculate eroded area area Sw；Institute The eroded area area S statedwIt is defined as：
Sw=a × SUMpwFormula (5)
In formula (5)：SUMpwFor total pixel value contained by eroded area；If a takes 1, show directly to use attrition region institute The total pixel value contained；
3) it is based on abrasive grain region area SgWith eroded area area Sw, calculate abrasive wear rate coefficient K；The abrasive grain mill Loss rate COEFFICIENT K is defined as：
Compared to the prior art, technical scheme of the present invention has following advantageous effect：
1. the present invention measures three big image procossing steps using abrasive grain region segmentation, eroded area segmentation and abrasive wear rate Suddenly, diamond abrasive grain wear rate more can be rapidly and accurately measured, the deficiency of artificial microscopy evaluation abrasion method is overcome；
2. the present invention combines the advantages of medium filtering and gaussian filtering, by the diamond abrasive grain image procossing of acquisition at enhancing Type diamond abrasive grain image, it is suppressed that the influence to abrasive grain region segmentation such as complex background texture reduces segmentation difficulty；
3. the present invention carries out deeper eroded area segmentation to the Debris Image of segmentation, abrasive wear region is improved The accuracy of segmentation.
Description of the drawings
Fig. 1 is flow chart of the present invention.
Fig. 2 is the diamond abrasive grain image of acquisition.
Fig. 3 is the enhancement metal hard rock Debris Image handled based on Fig. 2.
Fig. 4 is the abrasive grain area image of segmentation.
Fig. 5 is the abrasive wear area image of segmentation.
Fig. 6 is the abrasive wear rate result of image segmentation.
Specific implementation method
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
Fig. 1 is that the present invention wears assay method flow chart for a kind of diamond abrasive grain based on image segmentation, specifically Cutting procedure has been described in detail in invention content, no longer describes herein.
A kind of diamond abrasive grain wear rate assay method based on image procossing, by abrasive grain region segmentation, eroded area point It cuts and measures three step compositions with abrasive wear rate；
The abrasive grain region segmentation is by the processing of enhancement metal hard rock Debris Image, edge detection and Morphological scale-space group At, the specific steps are：
1) medium filtering is carried out respectively to the noisy diamond abrasive grain image I (x, y) of gray processing and gaussian filtering pre-processes, Obtain image I after medium filteringmImage I after (x, y) and gaussian filteringg(x, y) forms enhanced diamond abrasive grain by polymerization Image Ie(x,y)；
The enhancement metal hard rock Debris Image is defined as follows：
2) to enhancement metal hard rock Debris Image Ie(x, y) carries out edge detection, chooses detection threshold value λ, obtains target mill The contour edge bianry image I of grain imagegb(x,y)；
The edge detection is First-order Gradient amplitude detection, and gradient amplitude M (x, y) is defined as：
The detection threshold value λ is adaptive threshold, and threshold value lambda definition is：
In formula (3)：M and N is respectively the ranks pixel number of image.
3) the profile side of opening operation that morphological dilations and erosion operator are constituted and closed operation to target Debris Image is utilized Edge bianry image Igb(x, y) carries out morphologic filtering, removes unrelated background noise and texture edge；
4) holes filling and image cropping function is utilized to detach target abrasive grain, the abrasive grain area image divided；
The eroded area segmentation is made of medium filtering, gradient detection and Morphological scale-space, the specific steps are：
1) medium filtering pretreatment is carried out to the Debris Image of extraction, removal abrasive particle surface draws because of environment such as chip, dusts The noises such as the random grey white point risen；
2) edge detection is carried out to the pretreated Debris Image of medium filtering, edge detection method and threshold value choose the formula of pressing (2) it is calculated with formula (3), obtains the contour edge bianry image I of target abrasion imagewb(x,y)；
3) utilize the opening operation and closed operation that morphological dilations are constituted with erosion operator to bianry image Iwb(x, y) carries out shape State filters, and removes unrelated noise；
4) holes filling and image cropping function separation target is utilized to wear, the eroded area image divided；
The abrasive wear measurement is made of calculating abrasive grain region area, eroded area area and abrasive wear rate, The specific steps are：
1) the abrasive grain area image based on segmentation, the method counted using area pixel calculate abrasive grain region area Sg；Institute The abrasive grain region area S statedgIt is defined as：
Sg=a × SUMpgFormula (4)
In formula (4)：A is the correction factor of real area representated by unit pixel, SUMpgFor total picture contained by abrasive grain region Element value；If a takes 1, show directly using total pixel value contained by abrasive grain region；
2) the eroded area image based on segmentation, the method counted using area pixel calculate eroded area area Sw；Institute The eroded area area S statedwIt is defined as：
Sw=a × SUMpwFormula (5)
In formula (5)：SUMpwFor total pixel value contained by eroded area；If a takes 1, show directly to use attrition region institute The total pixel value contained；
3) it is based on abrasive grain region area SgWith eroded area area Sw, calculate abrasive wear rate coefficient K；The abrasive grain mill Loss rate COEFFICIENT K is defined as：
In order to illustrate the effect of the present invention, below by taking the diamond image of acquisition as an example, diamond abrasive grain figure is carried out respectively Image intensifying, abrasive grain region segmentation, eroded area segmentation and abrasive wear rate determination experiment.
It can be seen that from Fig. 2-4：The background of diamond image is more noisy before segmentation, but after the present invention is divided, The abrasive grain region of segmentation and the profile of eroded area are more complete, based on segmentation as a result, diamond abrasive grain can be measured quickly The degree of wear, reach measure diamond abrasive grain wear rate measurement purpose.
The foregoing is merely present pre-ferred embodiments, therefore cannot limit the technical scope of the present invention according to this, therefore Fan Yiben Equivalent changes and modifications made by the technical spirit and description of invention, in the range of should all belonging to technical solution of the present invention.

Claims (1)

1. a kind of diamond abrasive grain wear rate assay method based on image procossing, it is characterised in that by abrasive grain region segmentation, mill It damages region segmentation and abrasive wear rate measures three step compositions；
The abrasive grain region segmentation is handled by enhancement metal hard rock Debris Image, edge detection and Morphological scale-space form, The specific steps are：
1) medium filtering is carried out respectively to the noisy diamond abrasive grain image I (x, y) of gray processing and gaussian filtering pre-processes, obtained Image I after medium filteringmImage I after (x, y) and gaussian filteringg(x, y) forms enhancement metal hard rock Debris Image by polymerization Ie(x,y)；The enhancement metal hard rock Debris Image is defined as follows：
2) to enhancement metal hard rock Debris Image Ie(x, y) carries out edge detection, chooses detection threshold value λ, obtains target Debris Image Contour edge bianry image Igb(x,y)；
The edge detection is First-order Gradient amplitude detection, and gradient amplitude M (x, y) is defined as：
The detection threshold value λ is adaptive threshold, and threshold value lambda definition is：
In formula (3)：M and N is respectively the ranks pixel number of image.
3) contour edge two of opening operation that morphological dilations and erosion operator are constituted and closed operation to target Debris Image is utilized It is worth image Igb(x, y) carries out morphologic filtering, removes unrelated background noise and texture edge；
4) holes filling and image cropping function is utilized to detach target abrasive grain, the abrasive grain area image divided；
The eroded area segmentation is made of medium filtering, gradient detection and Morphological scale-space, the specific steps are：
1) medium filtering pretreatment is carried out to the Debris Image of extraction, removal abrasive particle surface is caused by the environment such as chip, dust The noises such as random ash white point；
2) edge detection is carried out to the pretreated Debris Image of medium filtering, edge detection method and threshold value, which are chosen, presses formula (2) It is calculated with formula (3), obtains the contour edge bianry image I of target abrasion imagewb(x,y)；
3) utilize the opening operation and closed operation that morphological dilations are constituted with erosion operator to bianry image Iwb(x, y) carries out morphology Filtering, removes unrelated noise；
4) holes filling and image cropping function separation target is utilized to wear, the eroded area image divided；
The abrasive wear measurement is made of calculating abrasive grain region area, eroded area area and abrasive wear rate, specifically Step is：
1) the abrasive grain area image based on segmentation, the method counted using area pixel calculate abrasive grain region area Sg；Described Abrasive grain region area SgIt is defined as：
Sg=a × SUMpgFormula (4)
In formula (4)：A is the correction factor of real area representated by unit pixel, SUMpgFor total pixel value contained by abrasive grain region； If a takes 1, show directly using total pixel value contained by abrasive grain region；
2) the eroded area image based on segmentation, the method counted using area pixel calculate eroded area area Sw；Described Eroded area area SwIt is defined as：
Sw=a × SUMpwFormula (5)
In formula (5)：SUMpwFor total pixel value contained by eroded area；If a takes 1, show directly to use contained by attrition region Total pixel value；
3) it is based on abrasive grain region area SgWith eroded area area Sw, calculate abrasive wear rate coefficient K；The abrasive wear rate COEFFICIENT K is defined as：
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CN113916800A (en) * 2021-10-08 2022-01-11 南京航空航天大学 Detection method for visually judging abrasion of high polymer plastic abrasive
CN114491836A (en) * 2021-12-30 2022-05-13 华侨大学 Virtual diamond tool generation method based on image processing and data driving
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Publication number Priority date Publication date Assignee Title
CN113063705A (en) * 2021-03-22 2021-07-02 陕西科技大学 Diamond wire surface diamond grain quality detection method based on machine vision
CN113063705B (en) * 2021-03-22 2022-09-27 陕西科技大学 Diamond wire surface carborundum particle quality detection method based on machine vision
CN113916800A (en) * 2021-10-08 2022-01-11 南京航空航天大学 Detection method for visually judging abrasion of high polymer plastic abrasive
CN113916800B (en) * 2021-10-08 2022-09-27 南京航空航天大学 Detection method for visually judging abrasion of high polymer plastic abrasive
CN114491836A (en) * 2021-12-30 2022-05-13 华侨大学 Virtual diamond tool generation method based on image processing and data driving
CN116645732A (en) * 2023-07-19 2023-08-25 厦门工学院 Site dangerous activity early warning method and system based on computer vision
CN116645732B (en) * 2023-07-19 2023-10-10 厦门工学院 Site dangerous activity early warning method and system based on computer vision

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