CN104848808B - A kind of Surface Roughness Detecting Method and equipment - Google Patents
A kind of Surface Roughness Detecting Method and equipment Download PDFInfo
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Abstract
The invention discloses a kind of Surface Roughness Detecting Method, methods described includes：Obtain the definition of the corresponding relation, the wherein virtual image that definition is formed by object of reference in body surface between definition and rough object surfaces degree；Object of reference is obtained in the corresponding image of the virtual image that the surface of thing to be detected is formed；Calculate the definition values of image；And according to corresponding relation, calculate the corresponding surface roughness of definition of image, be used as the surface roughness of determinand.The invention also discloses corresponding surface roughness detection device.
Description
Technical field
The invention belongs to roughness measurement field, more particularly to a kind of Surface Roughness Detecting Method and equipment.
Background technology
The traditional detection method of workpiece surface roughness is the tracer method measurement of contact, in tracer method measurement, Buddha's warrior attendant
Stone contact pilotage has scuffing to workpiece surface, and it detects that sampling is linear sampling, it is impossible to represent the feature of whole surface profile.It is based on
Contactless optical measurement because equipment is expensive, by production on-site environment influenceed greatly, inconvenient operation, the low reason of operating efficiency
Limit its application.Therefore the roughness measurement method based on machine vision is increasingly subject to the attention of researcher.But current surface
The machine vision detection method of roughness is generally while using CCD camera and microscope, because microscopic fields of view is small, can measure
Workpiece scope it is smaller, do not meet the larger measurement request of abrading article surface texture randomness.In addition, machine vision metrology surface
Roughness carries out statistical analysis mainly for the gray value information of undetected object surface image, and gray level image is unfavorable for human vision
The judge directly perceived of system (HVS).
The content of the invention
Therefore, the present invention provides a kind of new Surface Roughness Detecting Method and equipment, to try hard to solve or at least delay
The problem of solution exists above.
According to an aspect of the present invention there is provided a kind of Surface Roughness Detecting Method, methods described includes：Obtain clear
Spend rough object surfaces degree between corresponding relation, the virtual image that wherein definition is formed by object of reference in body surface it is clear
Clear degree；Object of reference is obtained in the corresponding image of the virtual image that the surface of thing to be detected is formed；Calculate the definition values of image；And
According to corresponding relation, the corresponding surface roughness of definition of image is calculated, the surface roughness of determinand is used as.
Alternatively, in the method according to the invention, the corresponding relation between definition and rough object surfaces degree is obtained,
Including：Choose one group of sample block with different surface roughnesses；Obtain the void that object of reference is formed on various kinds block surface respectively
As corresponding one group of image；Calculate the definition values of each image in one group of image；According to the surface roughness of various kinds block and
The image clarity values calculated, the corresponding relation set up between definition and surface roughness.
Alternatively, in the method according to the invention, set up using least square method between definition and surface roughness
Corresponding relation.
Alternatively, in the method according to the invention, wherein object of reference is colored object of reference.
Alternatively, in the method according to the invention, wherein object of reference is red green block.
Alternatively, in the method according to the invention, wherein calculating image clarity values F (I) according to equation below：
In formula
Wherein, m, n represent the length pixel count and width pixel count of image respectively；
Rxy、Gxy、BxyThe red, green, blue component value of pixel in image is represented respectively；
It is brightness step function；
δxyFor aberration evaluating；
For non-linear amplification coefficient；
ΓijkFor color space, Γi(x, y) representation in components image is in the brightness of color space, Γi(x, y)=(Rxy+Gxy+
Bxy)/3。
According to an aspect of the present invention there is provided a kind of surface roughness detection device, including：Storage device, suitable for depositing
Store up the corresponding relation between definition and rough object surfaces degree, the void that wherein definition is formed by object of reference in body surface
The definition of picture；Image acquiring device, suitable for obtaining object of reference in the corresponding image of the virtual image that the surface of determinand is formed；Clearly
Clear degree computing device, the definition values suitable for calculating image；And roughness computing device, suitable for according to corresponding relation, calculating
The corresponding surface roughness of definition of image, is used as the roughness on determinand surface.
Alternatively, in the device in accordance with the invention, storage device is stored definition and rough object surfaces degree it
Between corresponding relation obtain as follows：Choose one group of sample block with different surface roughnesses；Obtain object of reference point
The not other corresponding one group of image of the virtual image that is formed on various kinds block surface；Calculate the definition values of each image in one group of image；Root
According to the surface roughness and the image clarity values that are calculated of various kinds block, pair set up between definition and surface roughness
It should be related to.
Alternatively, in the device in accordance with the invention, wherein, definition and surface roughness are set up using least square method
Between corresponding relation.
Alternatively, in the device in accordance with the invention, wherein object of reference is colored object of reference.
Alternatively, in the device in accordance with the invention, wherein object of reference is red green block.
Alternatively, in the device in accordance with the invention, wherein sharpness computation device is clear according to equation below calculating image
Clear degree F (I)：
In formula
Wherein, m, n represent the length pixel count and width pixel count of image respectively；
Rxy、Gxy、BxyThe red, green, blue component value of pixel in image is represented respectively；
It is brightness step function；
δxyFor aberration evaluating；
For non-linear amplification coefficient；
ΓijkFor color space, Γi(x, y) representation in components image is in the brightness of color space, Γi(x, y)=(Rxy+Gxy+
Bxy)/3。
Alternatively, in the device in accordance with the invention, wherein image acquiring device is charge coupled cell imaging sensor.
Alternatively, in the device in accordance with the invention, the wherein surface of the optical axis direction of image acquiring device and object of reference
It is parallel, and with the surface of determinand and sample block in 45 ° of inclinations angle
The image on thing surface to be detected is directly obtained with prior art, and is carried out on the basis of the gray level image of surface coarse
Degree assessment is compared, according to the surface roughness detection scheme of the present invention, obtains the void that object of reference is formed on thing surface to be detected
As corresponding image, the image can be coloured image, non-gray level image, thus in the absence of the process of image deterioration so that figure
As the accuracy of processing is improved, so that the accuracy of roughness measurement result is higher.
Brief description of the drawings
In order to realize above-mentioned and related purpose, some illustrative sides are described herein in conjunction with following description and accompanying drawing
Face, these aspects indicate the various modes of principles disclosed herein that can put into practice, and all aspects and its equivalent aspect
It is intended to fall under in the range of theme claimed.The following detailed description by being read in conjunction with the figure, the disclosure it is above-mentioned
And other purposes, feature and advantage will be apparent.Throughout the disclosure, identical reference generally refers to identical
Part or element.
Fig. 1 shows mirror image schematic diagram；
Fig. 2 shows article surface vein direction；
Fig. 3 shows the flow chart of Surface Roughness Detecting Method 300 according to an illustrative embodiment of the invention；
Fig. 4 shows the schematic diagram of surface roughness detection device 400 according to an illustrative embodiment of the invention；With
And
Fig. 5 shows the schematic diagram of detection object surface roughness.
Embodiment
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although showing the disclosure in accompanying drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
Limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
Complete conveys to those skilled in the art.
The realization principle of the surface roughness detection scheme of the embodiment of the present invention is, utilizes colored readily identified spy
Point, is estimated by the objective value of definition in conjunction with the subjective assessment of human visual system (HVS) to surface roughness.
Fig. 1 shows a mirror image schematic diagram.
The realization principle of surface roughness detection scheme make use of reflection law, as shown in figure 1, illumination is mapped to object
On, it is reflected on minute surface, minute surface is reflected light in the eyes of people again, we therefore see that void of the object in minute surface
Picture.
Similarly, by body surface as minute surface, camera takes an object of reference, then object of reference just can be in thing as human eye
Body surface face forms the virtual image.Because body surface compared to level crossing wants coarse many, therefore shaggy object, in the unit interval
In reflex in camera and carry object of the luminous flux certainly less than any surface finish of object of reference information.Therefore, material is to light
Reflectivity is relevant with the roughness of material surface, meanwhile, article surface vein direction its reflection direction of difference is also different.
Fig. 2A-B show article surface vein direction, as shown in Fig. 2A figures, and object of reference projects to the light of body surface
Dissipated after reflection to both sides, form the virtual image of both sides extension；As shown in Figure 2 B, object of reference projects to the light warp of body surface
Dissipated up and down after reflection, the virtual image extended above and below formation.
Then, quality I (x, y) and illumination n (x, y), reflectivity r (x, y), the surface characteristic F (x, y) of the object of reference virtual image have
Close, wherein surface characteristic can include the characteristics such as material, grain direction and roughness.Formula is as follows：
I (x, y)=f (n (x, y), r (x, y), F (x, y))
Because the scope captured by camera is fixed, the luminous flux for carrying object of reference information is more into camera, in object table
The virtual image for the object of reference that face is formed is more clear.Therefore, surface roughness Ra (x, y) can set up following number with image definition
Learn model：
Ra (x, y)=f (I (x, y))
Because the information of object of reference can be manually set, therefore, the design of object of reference is for evaluating surface roughness
It is also vital.It is more than the more difficult formation clearly complete virtual images of more than 0.4um in view of roughness, object of reference is designed to letter
Single red green two kinds of colors can be conducive to the simple judgement of human visual system (HVS).
The mechanism of roughness is evaluated based on above definition, the embodiment of the present invention provides a kind of Surface Roughness Detecting Method
And equipment.
Fig. 3 shows the flow chart of the Surface Roughness Detecting Method 300 according to one exemplary embodiment of the present invention.
Step S310 is demarcating steps, in step S310, obtains corresponding between definition and rough object surfaces degree
Relation (i.e. calibration function), wherein the definition for the virtual image that the definition is formed by object of reference in body surface.
According to a kind of embodiment, the corresponding relation obtained between definition and rough object surfaces degree includes：Choose one
Sample block of the group with different surface roughnesses, wherein, the sample block of different roughness should be identical material, such as steel, sample block
The grain direction on surface should be same direction, such as all for laterally or vertically, the detection environment such as light source residing for sample block is strong
Degree should be identical.
The corresponding one group of image of the virtual image that object of reference is formed on various kinds block surface respectively is obtained, one group of image is calculated
In each image definition values, according to the surface roughness of various kinds block and the image clarity values calculated, set up clear
Corresponding relation between clear degree and surface roughness.
Wherein, object of reference can be colored object of reference, for example, red green block.Set up definition and surface roughness it
Between corresponding relation when, least square method can be used, it being understood, however, that all can be thick with surface for setting up definition
The algorithm or model of corresponding relation between rugosity are all within protection scope of the present invention.
In step s 320, object of reference is obtained in the corresponding image of the virtual image that the surface of thing to be detected is formed.
When obtaining the virtual image that surface to be detected is formed, determinand should be with the sample block in step S310 identical material,
Identical grain direction, the detection environment residing for determinand etc. should be identical.Object of reference be and reference same in step S310
Thing.
Then, in step S330, calculate the object of reference obtained in step s 320 and formed on the surface of thing to be detected
Image definition.
The definition values of image can utilize the aberration between pixel tristimulus values (i.e. the R of color value, G, B component) related
The color of sex determination pixel, sets up the definition aberration factor, and with the gradient gain system of nonlinear function raising pixel
Number, makes the evaluation function judge more sensitive to the definition of image.Can also be by calculating the normal orientation at a certain edge of image
Grey scale change situation evaluated, i.e., grey scale change is more violent, and edge is more clear, image is also more clear.It should but manage
Solution, it is all can for calculate image definition values algorithm all within protection scope of the present invention.
According to a kind of embodiment, image clarity values F (I) is calculated according to equation below：
In formula
Wherein, m, n represent the length pixel count and width pixel count of image respectively；
Rxy、Gxy、BxyThe red, green, blue component value of pixel in image is represented respectively；
It is brightness step function；
δxyFor aberration evaluating；
For non-linear amplification coefficient；
ΓijkFor color space, Γi(x, y) representation in components image is in the brightness of color space, Γi(x, y)=(Rxy+Gxy+
Bxy)/3。
Then, in step S340, according between the definition and rough object surfaces degree obtained in step S310
Corresponding relation, calculates the corresponding surface roughness of definition of described image, is used as the surface roughness of determinand.
It should be noted that in the design of the present invention, to improve the accuracy of detection of roughness, adaptive algorithm, mould
The grain direction that the object and/or sample block of type should be identical material, object and/or sample block surface should identical, object and/or sample block
Should be identical with the environment residing for object of reference.That is, the thing to be detected of unlike material corresponds to different calibration functions.
In the design of the present invention, the image on thing surface to be detected is directly obtained with prior art, and in surface ash
Roughness assessment is carried out on the basis of degree image to compare, and according to the surface roughness detection scheme of the present invention, is obtained object of reference and is existed
The corresponding image of the virtual image that thing surface to be detected is formed, the image can be coloured image, non-gray level image, thus be not present
The process of image deterioration so that the accuracy of image procossing is improved, so that the accuracy of roughness measurement result is higher.Build
Corresponding relation between vertical definition and rough object surfaces degree is non-linear relation, than other non-contact detections technical side
The linear relationship that case is used is more accurate, so as to improve accuracy of detection.
Fig. 4 shows the schematic diagram of the surface roughness detection device 400 according to one exemplary embodiment of the present invention.
As shown in figure 4, surface roughness detection device 400 includes：Storage device 410, image acquiring device 420, sharpness computation dress
Put 430 and roughness computing device 440.
Storage device 410 is suitable to the corresponding relation between storage definition and rough object surfaces degree, and definition and thing
Corresponding relation between body surface surface roughness can be obtained as follows：Choosing one group has different surface roughnesses
Sample block；Obtain the corresponding one group of image of the virtual image that object of reference is formed on various kinds block surface respectively；Calculate each in one group of image
The definition values of image；According to the surface roughness of various kinds block and the image clarity values calculated, set up definition with
Corresponding relation between surface roughness.
Wherein, the definition for the virtual image that definition is formed by object of reference in body surface.Object of reference can be colored ginseng
According to thing, such as red green block.
Image acquiring device 420 obtains object of reference in the corresponding image of the virtual image that the surface of determinand is formed.Image is obtained
It can be charge coupled cell imaging sensor, such as CCD camera to take device 420.The optical axis of above-mentioned image acquiring device 420
Direction and the surface of determinand and/or sample block are in 45 ° of inclinations angle, and determinand and/or the sample block, object of reference are, for example, square
One edge strip of a line and object of reference of shape, wherein determinand and/or sample block be arranged in parallel, and object of reference is located at image acquiring device
In front of 420 optical axis directions, and object of reference surface is parallel with the optical axis direction of image acquiring device 420.
Sharpness computation device 430 obtains image at image acquiring device 420 and calculates the definition values of image.Clearly
Degree computing device 430 can be calculated and obtained by the definition algorithm based on an acutance, Measurement for Digital Image Definition, can also
Image definition F (I) is calculated according to the formula in embodiment of the method, it being understood, however, that all can be for calculating image
The algorithm of definition values is all within protection scope of the present invention.
Roughness computing device 440 calculates obtained definition values according to sharpness computation device 430, and is filled according to storage
The corresponding relation between the definition stored in 410 and rough object surfaces degree is put, the definition for calculating above-mentioned image is corresponding
Surface roughness, is used as the roughness on determinand surface.According to a kind of embodiment, using least square method set up definition with
Corresponding relation between surface roughness, it being understood, however, that all can be for setting up between definition and surface roughness
Corresponding relation algorithm or model all within protection scope of the present invention.
Fig. 5 shows the schematic diagram of rough object surfaces degree detection.
According to one embodiment, when being detected to rough object surfaces degree, as shown in figure 5, the equipment that can be selected
Including：5000000 pixel CCD cameras, connect the computer of CCD camera, LED white light source and controller, object of reference and workbench.
Sample block surface parallel to workbench, object of reference surface and workbench in 45 ° of inclinations angle, object of reference surface parallel to
The optical axis of CCD camera is put to and positioned at the front of CCD camera, CCD camera can capture a ginseng vertical with sample block surface
According to the image of the thing virtual image.In detection process, it is contemplated that fluorescent lamp has the phenomenon of stroboscopic, using the white strip sources of two LED,
Pass through light-source controller controls illumination.
One group of sample block with different surface roughnesses is chosen, for example, chooses the different sample block of 9 surface roughnesses, slightly
Rugosity is respectively：0.05um、0.1um、0.2um、0.3um、0.4um、0.5um、1.2um、1.6um、2.4um.Wherein, this group of sample
Block is identical material, same surface texture direction and detected positioned at identical in environment.
On the premise of ensureing that lighting environment is stable and camera, color lump position immobilize, object of reference is obtained in sample block
The virtual image on surface, the definition values of virtual images are calculated by coloured image definition evaluation algorithms F (I), virtual image definition
Computational algorithm has been described in detail above, does not do excessive narration herein.Further, passed through according to the image definition calculated
The relational model that least square method is set up between definition and surface roughness, and by algorithm and model storage in a computer.
Then, the roughness for surveying determinand surface is detected, that is, obtain object of reference on object under test surface into void
The image of picture, calculates the definition of virtual images, and the table of determinand is calculated according to image definition and above-mentioned relational model
Surface roughness.When detecting the roughness on object under test surface, object under test should be identical material, same surface texture with sample block
Direction and positioned at identical detection environment in.
The embodiment of the present invention evaluates the mechanism of roughness based on definition, and according to the coloured image based on color correlation
Definition evaluation algorithms, establish the relational model between surface roughness and image definition, and combine human visual system
(HVS) subjective assessment is detected to surface roughness, realizes the non-contact detecting of surface roughness, and accuracy of detection
It is high.
In the specification that this place is provided, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention
Example can be put into practice in the case of these no details.In some instances, known method, knot is not been shown in detail
Structure and technology, so as not to obscure the understanding of this description.
A11：Equipment according to claim A10, wherein the object of reference is red green block.A12：Will according to right
The equipment described in A7 or A8 is sought, wherein sharpness computation device calculates image definition F (I) according to equation below：
In formula
Wherein, m, n represent the length pixel count and width pixel count of image respectively；
Rxy、Gxy、BxyThe red, green, blue component value of pixel in image is represented respectively；
It is brightness step function；
δxyFor aberration evaluating；
For non-linear amplification coefficient；
ΓijkFor color space, Γi(x, y) representation in components image is in the brightness of color space, Γi(x, y)=(Rxy+Gxy+
Bxy)/3。
A13：Equipment according to claim A7, wherein described image acquisition device includes：Charge coupled cell figure
As sensor.A14：Equipment according to claim A7 or A8, the wherein optical axis direction of described image acquisition device and institute
The surface for stating object of reference is parallel, and with the surface of determinand and sample block in 45 ° of inclinations angle.
Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect, exist
Above in the description of the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：It is i.e. required to protect
The application claims of shield are than the feature more features that is expressly recited in each claim.More precisely, as following
As claims reflect, inventive aspect is all features less than single embodiment disclosed above.Therefore, abide by
Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself
It is used as the separate embodiments of the present invention.
Those skilled in the art should be understood the module or unit or group of the equipment in example disclosed herein
Part can be arranged in equipment as depicted in this embodiment, or alternatively can be positioned at and the equipment in the example
In different one or more equipment.Module in aforementioned exemplary can be combined as a module or be segmented into addition multiple
Submodule.
Those skilled in the art, which are appreciated that, to be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment
Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or
Sub-component.In addition at least some in such feature and/or process or unit exclude each other, it can use any
Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so to appoint
Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power
Profit is required, summary and accompanying drawing) disclosed in each feature can or similar purpose identical, equivalent by offer alternative features come generation
Replace.
Although in addition, it will be appreciated by those of skill in the art that some embodiments described herein include other embodiments
In included some features rather than further feature, but the combination of the feature of be the same as Example does not mean in of the invention
Within the scope of and form different embodiments.For example, in the following claims, times of embodiment claimed
One of meaning mode can be used in any combination.
In addition, be described as herein can be by the processor of computer system or by performing for some in the embodiment
Method or the combination of method element that other devices of the function are implemented.Therefore, with for implementing methods described or method
The processor of the necessary instruction of element forms the device for implementing this method or method element.In addition, device embodiment
Element described in this is the example of following device：The device is used to implement as in order to performed by implementing the element of the purpose of the invention
Function.
As used in this, unless specifically stated so, come using ordinal number " first ", " second ", " the 3rd " etc.
Description plain objects are merely representative of the different instances for being related to similar object, and are not intended to imply that the object being so described must
Must have the time it is upper, spatially, in terms of sequence or given order in any other manner.
Although describing the present invention according to the embodiment of limited quantity, above description, the art are benefited from
It is interior it is clear for the skilled person that in the scope of the present invention thus described, it can be envisaged that other embodiments.Additionally, it should be noted that
The language that is used in this specification primarily to readable and teaching purpose and select, rather than in order to explain or limit
Determine subject of the present invention and select.Therefore, in the case of without departing from the scope and spirit of the appended claims, for this
Many modifications and changes will be apparent from for the those of ordinary skill of technical field.For the scope of the present invention, to this
The done disclosure of invention is illustrative and not restrictive, and it is intended that the scope of the present invention be defined by the claims appended hereto.
Claims (14)
1. a kind of Surface Roughness Detecting Method, including：
The corresponding relation between definition and rough object surfaces degree is obtained, wherein the definition is object of reference in body surface
The definition of the virtual image formed；
Object of reference is obtained in the corresponding image of the virtual image that the surface of thing to be detected is formed；
Calculate the definition of described image；And
According to the corresponding relation, the corresponding surface roughness of definition of described image is calculated, it is thick as the surface of determinand
Rugosity.
2. according to the method described in claim 1, wherein, it is described obtain between definition and rough object surfaces degree corresponding close
System, including：
Choose one group of sample block with different surface roughnesses；
Obtain the corresponding one group of image of the virtual image that object of reference is formed on various kinds block surface respectively；
Calculate the definition values of each image in one group of image；
According to the surface roughness of various kinds block and the image clarity values calculated, set up definition and surface roughness it
Between corresponding relation.
3. method according to claim 2, wherein, set up using least square method between definition and surface roughness
Corresponding relation.
4. according to the method described in claim 1, wherein the object of reference is colored object of reference.
5. method according to claim 4, wherein the object of reference is red green block.
6. method according to claim 1 or 2, wherein calculating image clarity values F (I) according to equation below：
<mrow>
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Wherein, m, n represent the length pixel count and width pixel count of image respectively；
Rxy、Gxy、BxyThe red, green, blue component value of pixel in image is represented respectively；
It is brightness step function；
δxyFor aberration evaluating；
For non-linear amplification coefficient；
ΓijkFor color space, Γi(x, y) representation in components image is in the brightness of color space, Γi(x, y)=(Rxy+Gxy+Bxy)/
3。
7. a kind of surface roughness detection device, including：
Storage device, suitable for the corresponding relation between storage definition and rough object surfaces degree, wherein the definition is ginseng
The definition of the virtual image formed according to thing in body surface；
Image acquiring device, suitable for obtaining object of reference in the corresponding image of the virtual image that the surface of determinand is formed；
Sharpness computation device, the definition values suitable for calculating described image；And
Roughness computing device, suitable for according to the corresponding relation, calculating the corresponding surface roughness of definition of described image,
It is used as the roughness on determinand surface.
8. equipment according to claim 7, wherein, between definition and rough object surfaces degree that storage device is stored
Corresponding relation obtain as follows：
Choose one group of sample block with different surface roughnesses；
Obtain the corresponding one group of image of the virtual image that object of reference is formed on various kinds block surface respectively；
Calculate the definition values of each image in one group of image；
According to the surface roughness of various kinds block and the image clarity values calculated, set up definition and surface roughness it
Between corresponding relation.
9. equipment according to claim 8, wherein, set up using least square method between definition and surface roughness
Corresponding relation.
10. equipment according to claim 7, wherein the object of reference is colored object of reference.
11. equipment according to claim 10, wherein the object of reference is red green block.
12. the equipment according to claim 7 or 8, wherein sharpness computation device calculate image clearly according to equation below
Spend F (I)：
<mrow>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mi>I</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>x</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>y</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mo>&part;</mo>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mo>&dtri;</mo>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&delta;</mi>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
In formula
<mrow>
<msub>
<mi>&delta;</mi>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>=</mo>
<msqrt>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>R</mi>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>G</mi>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>B</mi>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>G</mi>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
</mrow>
<mrow>
<msub>
<mo>&part;</mo>
<mrow>
<mi>x</mi>
<mi>y</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>l</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>+</mo>
<msub>
<mi>&Gamma;</mi>
<mi>i</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
Wherein, m, n represent the length pixel count and width pixel count of image respectively；
Rxy、Gxy、BxyThe red, green, blue component value of pixel in image is represented respectively；
It is brightness step function；
δxyFor aberration evaluating；
For non-linear amplification coefficient；
ΓijkFor color space, Γi(x, y) representation in components image is in the brightness of color space, Γi(x, y)=(Rxy+Gxy+Bxy)/
3。
13. equipment according to claim 7, wherein described image acquisition device includes：Charge coupled cell image sensing
Device.
14. the equipment according to claim 7 or 8, the wherein optical axis direction of described image acquisition device and the object of reference
Surface it is parallel, and with the surface of determinand and sample block in 45 ° of inclinations angle.
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| CN110793472B (en) * | 2019-11-11 | 2021-07-27 | 桂林理工大学 | Grinding surface roughness detection method based on quaternion singular value entropy index |
| CN110823139B (en) * | 2019-11-21 | 2021-03-23 | 苏州沛斯仁光电科技有限公司 | Measuring method of multi-angle reflector |
| CN113034482A (en) * | 2021-04-07 | 2021-06-25 | 山东大学 | Surface roughness detection method based on machine vision and machine learning |
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| CN1746666A (en) * | 2004-09-09 | 2006-03-15 | 大日本网目版制造株式会社 | Object based color image defect detection |
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