CN110009592A - Fall grey degree real-time monitoring system - Google Patents
Fall grey degree real-time monitoring system Download PDFInfo
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- CN110009592A CN110009592A CN201811094919.6A CN201811094919A CN110009592A CN 110009592 A CN110009592 A CN 110009592A CN 201811094919 A CN201811094919 A CN 201811094919A CN 110009592 A CN110009592 A CN 110009592A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 39
- 239000000428 dust Substances 0.000 claims abstract description 59
- 239000011521 glass Substances 0.000 claims abstract description 47
- 238000001514 detection method Methods 0.000 claims abstract description 12
- 238000012806 monitoring device Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 57
- 238000000034 method Methods 0.000 claims description 24
- 239000000284 extract Substances 0.000 claims description 19
- 230000007797 corrosion Effects 0.000 claims description 15
- 238000005260 corrosion Methods 0.000 claims description 15
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 230000003628 erosive effect Effects 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 6
- 238000005530 etching Methods 0.000 claims description 3
- 239000002956 ash Substances 0.000 claims 7
- 235000002918 Fraxinus excelsior Nutrition 0.000 claims 1
- 101150012579 ADSL gene Proteins 0.000 description 8
- 102100020775 Adenylosuccinate lyase Human genes 0.000 description 8
- 108700040193 Adenylosuccinate lyases Proteins 0.000 description 8
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 241000208340 Araliaceae Species 0.000 description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- CDBYLPFSWZWCQE-UHFFFAOYSA-L Sodium Carbonate Chemical compound [Na+].[Na+].[O-]C([O-])=O CDBYLPFSWZWCQE-UHFFFAOYSA-L 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 150000003839 salts Chemical class 0.000 description 2
- 239000000377 silicon dioxide Substances 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 235000019738 Limestone Nutrition 0.000 description 1
- BPQQTUXANYXVAA-UHFFFAOYSA-N Orthosilicate Chemical group [O-][Si]([O-])([O-])[O-] BPQQTUXANYXVAA-UHFFFAOYSA-N 0.000 description 1
- 239000006004 Quartz sand Substances 0.000 description 1
- 208000006673 asthma Diseases 0.000 description 1
- AYJRCSIUFZENHW-DEQYMQKBSA-L barium(2+);oxomethanediolate Chemical compound [Ba+2].[O-][14C]([O-])=O AYJRCSIUFZENHW-DEQYMQKBSA-L 0.000 description 1
- 238000009835 boiling Methods 0.000 description 1
- 229910021538 borax Inorganic materials 0.000 description 1
- KGBXLFKZBHKPEV-UHFFFAOYSA-N boric acid Chemical compound OB(O)O KGBXLFKZBHKPEV-UHFFFAOYSA-N 0.000 description 1
- 239000004327 boric acid Substances 0.000 description 1
- 229910052681 coesite Inorganic materials 0.000 description 1
- 229910052906 cristobalite Inorganic materials 0.000 description 1
- 239000002178 crystalline material Substances 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 239000010433 feldspar Substances 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000006028 limestone Substances 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 239000007769 metal material Substances 0.000 description 1
- 150000002739 metals Chemical class 0.000 description 1
- 229920003023 plastic Polymers 0.000 description 1
- 229920003229 poly(methyl methacrylate) Polymers 0.000 description 1
- 239000004926 polymethyl methacrylate Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 235000012239 silicon dioxide Nutrition 0.000 description 1
- 229910000029 sodium carbonate Inorganic materials 0.000 description 1
- 235000017550 sodium carbonate Nutrition 0.000 description 1
- 235000019795 sodium metasilicate Nutrition 0.000 description 1
- 229910052911 sodium silicate Inorganic materials 0.000 description 1
- 239000004328 sodium tetraborate Substances 0.000 description 1
- 235000010339 sodium tetraborate Nutrition 0.000 description 1
- 239000005315 stained glass Substances 0.000 description 1
- 229910052682 stishovite Inorganic materials 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 239000005341 toughened glass Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 229910052905 tridymite Inorganic materials 0.000 description 1
- 229910052882 wollastonite Inorganic materials 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- 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
- G06T2207/20004—Adaptive image processing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Processing (AREA)
Abstract
The present invention relates to one kind to fall grey degree real-time monitoring system, include: dust monitoring device, the side of house wall embedded glass is set, is monitored for the current dust thickness to house wall embedded glass, to obtain corresponding glass dust thickness, and export the glass dust thickness；The oblique upper of house wall embedded glass is arranged in real-time grasp shoot equipment, is mounted on the house wall, for carrying out real-time grasp shoot operation to the embedded glass, to obtain and export corresponding real-time grasp shoot image；Body detection device is connect with the real-time grasp shoot equipment, for receiving the real-time grasp shoot image, is detected to each object in the real-time grasp shoot image, to obtain each object region of each object respectively in the real-time grasp shoot image.Through the invention, real-time and accuracy that glass falls ash detection are improved.
Description
Technical field
The present invention relates to glass detection fields more particularly to one kind to fall grey degree real-time monitoring system.
Background technique
The molecules align of glass be it is random, molecule has statistical uniformity in space.In perfect condition
Under, the physics of homogeneous glass, chemical property (such as refractive index, hardness, elasticity modulus, thermal expansion coefficient, thermal conductivity, conductivity)
It is all identical in all directions.
Because glass is mixture, noncrystal, so without fixed molten boiling point.It is certain temperature that glass, which is changed into liquid by solid,
Progress in region (i.e. softening range) is spent, it is different from crystalline material, the fusing point that do not fix.
Summary of the invention
It is not easy the technical issues of detecting in order to solve the grey degree that falls of current house wall embedded glass, the present invention provides
One kind falling grey degree real-time monitoring system.
For this purpose, the present invention at least has the important inventive point of following two:
(1) different parameters for falling grey situation based on multiple reactions divide the grey degree that falls of house wall embedded glass
Analysis improves the comprehensive and validity of analysis result；
(2) each object in image is detected, determines that wherein the smallest subject area of the depth of field as image to believe
Number processing reference zone, to realize the adapting to image filtering operation of image content-based and image procossing characteristic.
According to an aspect of the present invention, it provides one kind and falls grey degree real-time monitoring system, the system comprises:
The side of house wall embedded glass is arranged in dust monitoring device, for working as to house wall embedded glass
Preceding dust thickness is monitored, and to obtain corresponding glass dust thickness, and exports the glass dust thickness；Real-time grasp shoot is set
It is standby, the oblique upper of house wall embedded glass is set, is mounted on the house wall, for being carried out to the embedded glass
Real-time grasp shoot operation, to obtain and export corresponding real-time grasp shoot image；Body detection device connects with the real-time grasp shoot equipment
It connects, for receiving the real-time grasp shoot image, each object in the real-time grasp shoot image is detected, described in obtaining
The each object each object region in the real-time grasp shoot image respectively；Parameter extraction equipment is set with the object detection
Standby connection, for receiving each object region in the real-time grasp shoot image, and to each in the real-time grasp shoot image
The depth of field of the object in the real-time grasp shoot image extracts, and using subject area corresponding to the smallest object of the depth of field as
Pending area output；Signal analysis equipment is connect with the parameter extraction equipment, will for receiving the pending area
The pending area is divided into object outline subregion and contents of object subregion, the contents of object subregion be it is described to
Remaining pattern after being partitioned into the object outline subregion in processing region；Noise measuring equipment is set with signal analysis
Standby connection, for determine the various noises in the object outline subregion each amplitude maximum value using as profile noise
Maximum value, be also used to the maximum value of each amplitude of the various noises in the contents of object subregion using as content noise most
Big value；Self-adaptive processing equipment is connect with the body detection device and the noise measuring equipment respectively, in the wheel
When wide noise maximum value is greater than the content noise maximum value, the real-time grasp shoot image is executed and is opened after first corroding expansion process
The image processing mode of processing is closed, image is handled to obtain simultaneously output adaptive, is also used to small in the profile noise maximum value
When being equal to the content noise maximum value, the figure of first opening and closing processing post-etching expansion process is executed to the real-time grasp shoot image
As tupe, image is handled to obtain simultaneously output adaptive；Dust extract equipment is connect with the self-adaptive processing equipment,
For receiving the self-adaptive processing image, the red color component value of each pixel in the self-adaptive processing image is obtained,
The pixel that red color component value is fallen between default dust red lower threshold and default dust red upper limit threshold is confirmed as
Dust pixel；Image is fitted equipment, connect with the dust extract equipment, for receiving in the self-adaptive processing image
The multiple dust pixel is fitted to one or more dust patterns by multiple dust pixels；Reference value extract equipment, with
Described image is fitted equipment connection, for obtaining the maximum area dust pattern in one or more of dust patterns, and will
The area of maximum area dust pattern is as the first reference value, also using the quantity of one or more of dust patterns as second
Reference value；It falls ash and judges equipment, connect respectively with the reference value extract equipment and the dust monitoring device, being used for will be described
Glass dust thickness is based on first reference value, second reference value and the third reference value as third reference value
It is weighted, ash is fallen with acquisition and judges parameter.
More specifically, being fallen in grey degree real-time monitoring system described: judge in equipment in the ash that falls, be based on described the
During one reference value, second reference value and the third reference value are weighted, each weight system of each reference value
Number is different.
More specifically, falling in grey degree real-time monitoring system described: the self-adaptive processing equipment includes numerical value judgement
Sub- equipment, microcontroller, the sub- equipment of corrosion expansion process and opening and closing handle sub- equipment, the microcontroller respectively with the numerical value
Judge that sub- equipment, the sub- equipment of the corrosion expansion process handle sub- equipment with the opening and closing and connects.
More specifically, falling in grey degree real-time monitoring system described: the sub- equipment of the corrosion expansion process includes image
Erosion unit and image expansion unit, described image erosion unit are connected with described image expansion cell, opening and closing processing
Equipment includes that image opens processing unit and image closes processing unit, and described image opens processing unit and described image closes processing unit
Connection.
More specifically, being fallen in grey degree real-time monitoring system described: in the self-adaptive processing equipment, the numerical value
Judge sub- equipment for carrying out numerical values recited judgement to the profile noise maximum value and the content noise maximum value.
More specifically, being fallen in grey degree real-time monitoring system described: in the self-adaptive processing equipment, the corrosion
The sub- equipment of expansion process handles sub- equipment with the opening and closing and connect.
More specifically, falling in grey degree real-time monitoring system described: the opening and closing handles the image that sub- equipment interconnection is received
The processing of make before break is executed, the processing expanded afterwards is first corroded in the image execution that the sub- equipment interconnection of the corrosion expansion process is received.
More specifically, being fallen in grey degree real-time monitoring system described: judging in equipment in the ash that falls, fall ash judgement ginseng
Number is bigger, house wall embedded glass to fall grey degree bigger.
More specifically, falling in grey degree real-time monitoring system described: the dust extract equipment, described image fitting are set
Standby, the described reference value extract equipment and the ash that falls judge the electronic-control box that equipment is arranged near house wall embedded glass
It is interior.
Detailed description of the invention
Embodiment of the present invention is described below with reference to attached drawing, in which:
Fig. 1 is according to the structure for falling ash and judging equipment for falling grey degree real-time monitoring system shown in embodiment of the present invention
Block diagram.
Specific embodiment
The embodiment for falling grey degree real-time monitoring system of the invention is described in detail below with reference to accompanying drawings.
Glass is amorphous inorganic non-metallic material, and usually with a variety of inorganic minerals, (such as quartz sand, borax, boric acid, weight are brilliant
Stone, barium carbonate, lime stone, feldspar, soda ash etc.) it is primary raw material, it additionally incorporates made of a small amount of auxiliary material.It it is main at
It is divided into silica and other oxides.
The chemical composition of simple glass is Na2SiO3, CaSiO3, SiO2 or Na2OCaO6SiO2 etc., main component
It is silicate double salt, is a kind of non-crystalline solids of random structure.It is widely used in building, is used to belong to every wind light transmission
Mixture.
Separately have the oxide for being mixed into certain metals or salt and show the coloured glass of color, and by physics or
Tempered glass made from the method for person's chemistry etc..Sometimes some transparent plastics (such as polymethyl methacrylate) are also referred to as had
Machine glass.
In order to overcome above-mentioned deficiency, the present invention has built one kind and has fallen grey degree real-time monitoring system, can effectively solve the problem that phase
The technical issues of answering.
Fig. 1 is according to the structure for falling ash and judging equipment for falling grey degree real-time monitoring system shown in embodiment of the present invention
Block diagram.
The grey degree real-time monitoring system that falls shown according to an embodiment of the present invention includes:
The side of house wall embedded glass is arranged in dust monitoring device, for working as to house wall embedded glass
Preceding dust thickness is monitored, and to obtain corresponding glass dust thickness, and exports the glass dust thickness；
The oblique upper of house wall embedded glass is arranged in real-time grasp shoot equipment, is mounted on the house wall, is used for
Real-time grasp shoot operation is carried out to the embedded glass, to obtain and export corresponding real-time grasp shoot image；
Body detection device is connect with the real-time grasp shoot equipment, for receiving the real-time grasp shoot image, to the reality
When capture image in each object detected, it is each in the real-time grasp shoot image respectively to obtain each object
A subject area；
Parameter extraction equipment is connect with the body detection device, each in the real-time grasp shoot image for receiving
Subject area, and the depth of field of each object in the real-time grasp shoot image in the real-time grasp shoot image is extracted,
And it is exported subject area corresponding to the smallest object of the depth of field as pending area；
Signal analysis equipment is connect with the parameter extraction equipment, for receiving the pending area, by described wait locate
Managing region division is object outline subregion and contents of object subregion, and the contents of object subregion is the pending area
In be partitioned into the remaining pattern after the object outline subregion；
Noise measuring equipment is connect with the signal analysis equipment, each in the object outline subregion for determining
The maximum value of each amplitude of kind noise is to be also used to various in the contents of object subregion as profile noise maximum value
The maximum value of each amplitude of noise is using as content noise maximum value；
Self-adaptive processing equipment is connect with the body detection device and the noise measuring equipment respectively, in institute
When stating profile noise maximum value greater than the content noise maximum value, expansion process is first corroded to real-time grasp shoot image execution
It is opened and closed the image processing mode of processing afterwards, handles image to obtain simultaneously output adaptive, is also used to maximum in the profile noise
When value is less than or equal to the content noise maximum value, first opening and closing processing post-etching expansion process is executed to the real-time grasp shoot image
Image processing mode, with obtain and output adaptive handle image；
Dust extract equipment is connect with the self-adaptive processing equipment, for receiving the self-adaptive processing image, is obtained
Red color component value is fallen in default dust red lower limit by the red color component value of each pixel in the self-adaptive processing image
Pixel between threshold value and default dust red upper limit threshold is confirmed as dust pixel；
Image is fitted equipment, connect with the dust extract equipment, more in the self-adaptive processing image for receiving
The multiple dust pixel is fitted to one or more dust patterns by a dust pixel；
Reference value extract equipment is connect, for obtaining one or more of dust patterns with described image fitting equipment
In maximum area dust pattern, and using the area of maximum area dust pattern as the first reference value, also by one or
The quantity of multiple dust patterns is as the second reference value；
It falls ash and judges equipment, connect respectively with the reference value extract equipment and the dust monitoring device, is used for institute
Glass dust thickness is stated as third reference value, based on first reference value, second reference value and third reference
Value is weighted, and falls ash with acquisition and judges parameter.
Then, continue that the specific structure for falling grey degree real-time monitoring system of the invention is further detailed.
Fallen in grey degree real-time monitoring system described: it is described fall ash judge in equipment, be based on first reference value,
During second reference value and the third reference value are weighted, each weight coefficient of each reference value is different.
Fall in grey degree real-time monitoring system described: the self-adaptive processing equipment includes that numerical value judges sub- equipment, micro-
Controller, the sub- equipment of corrosion expansion process and opening and closing handle sub- equipment, and the microcontroller judges that son is set with the numerical value respectively
The sub- equipment of standby, the described corrosion expansion process handles sub- equipment with the opening and closing and connects.
Fallen in grey degree real-time monitoring system described: the sub- equipment of the corrosion expansion process include Image erosion unit and
Image expansion unit, described image erosion unit are connected with described image expansion cell, and it includes figure that the opening and closing, which handles sub- equipment,
As opening processing unit and image closes processing unit, described image, which opens processing unit and closes processing unit with described image, to be connected.
Fall in grey degree real-time monitoring system described: in the self-adaptive processing equipment, the numerical value judges that son is set
It is ready for use on and numerical values recited judgement is carried out to the profile noise maximum value and the content noise maximum value.
It is fallen in grey degree real-time monitoring system described: in the self-adaptive processing equipment, the corrosion expansion process
Sub- equipment handles sub- equipment with the opening and closing and connect.
It is fallen in grey degree real-time monitoring system described: after the image execution that the opening and closing handles sub- equipment interconnection receipts is first opened
The processing expanded afterwards is first corroded in the processing closed, the image execution that the sub- equipment interconnection of the corrosion expansion process is received.
It is fallen in grey degree real-time monitoring system described: judging in equipment in the ash that falls, fall ash and judge that parameter is bigger, room
Room wall embedded glass to fall grey degree bigger.
It is fallen in grey degree real-time monitoring system described: the dust extract equipment, described image fitting equipment, the ginseng
It examines value extract equipment and the ash that falls judges that equipment is arranged in the electronic-control box near house wall embedded glass.
In addition, being fallen in grey degree real-time monitoring system described, further includes: ADSL connection equipment falls grey judgement with described
Equipment connection judges parameter for receiving the ash that falls, and falls ash described in transmission and judge parameter.
It by existing ordinary telephone line is family that ADSL, which is a kind of, office provides the technology of broadband data transmission service,
He can provide very high data transmitting bandwidth, it is wide to be enough to allow the asthma of telecommunication sparetime university without a break.ADSL scheme does not need transformation signal
Transmission line, it only needs a pair of special MODEM, and one of MODEM is connected on the computer of user, and another then
It is mounted in the communication center of telecommunications company, is still common telephone line for what they were connected.After using ADSL scheme,
The speed of data transmission improves much really.The transmission speed of ADSL scheme is about 50 times of ISDN scheme, satellite scheme
20 times, while he does not need restructuring route again, therefore ADSL is current more feasible online speeding scheme.
ADSL aims at video play-on-demand program and designs in initial stage of development.With the rapid development of internet, ADSL changes
Head changes face and appears in face of people as a kind of technology of high-peed connection internet, and user is allowed to feel fresh and new, he makes existing
There is offer multimedia service on internet to be possibly realized.For the company for providing telecommunications service, they do not have to again for replacement
Route to be put into astronomical fund and be worried, they extremely flexibly can configure ASDL equipment according to user volume, is
User provides more online services.
Grey degree real-time monitoring system is fallen using of the invention, falls ash for house wall embedded glass in the prior art
Degree is not easy the technical issues of detecting, by falling the different parameters of grey situation to house wall embedded glass based on multiple reactions
It falls grey degree to be analyzed, improves the comprehensive and validity of analysis result；Meanwhile each object in image is examined
It surveys, determines that wherein the smallest subject area of the depth of field is using the reference zone as image signal process, to realize based in image
Hold the adapting to image filtering operation with image procossing characteristic.
It is understood that although the present invention has been disclosed in the preferred embodiments as above, above-described embodiment not to
Limit the present invention.For any person skilled in the art, without departing from the scope of the technical proposal of the invention,
Many possible changes and modifications all are made to technical solution of the present invention using the technology contents of the disclosure above, or are revised as
With the equivalent embodiment of variation.Therefore, anything that does not depart from the technical scheme of the invention are right according to the technical essence of the invention
Any simple modifications, equivalents, and modifications made for any of the above embodiments still fall within the range of technical solution of the present invention protection
It is interior.
Claims (9)
1. one kind falls grey degree real-time monitoring system, which is characterized in that the system comprises:
The side of house wall embedded glass is arranged in dust monitoring device, for the current ash to house wall embedded glass
Dirt thickness is monitored, and to obtain corresponding glass dust thickness, and exports the glass dust thickness；
The oblique upper of house wall embedded glass is arranged in real-time grasp shoot equipment, is mounted on the house wall, for institute
It states embedded glass and carries out real-time grasp shoot operation, to obtain and export corresponding real-time grasp shoot image；
Body detection device is connect with the real-time grasp shoot equipment, for receiving the real-time grasp shoot image, is grabbed in real time to described
The each object clapped in image is detected, each right in the real-time grasp shoot image respectively to obtain each object
As region；
Parameter extraction equipment is connect with the body detection device, for receiving each object in the real-time grasp shoot image
Region, and the depth of field of each object in the real-time grasp shoot image in the real-time grasp shoot image is extracted, and will
Subject area corresponding to the smallest object of the depth of field is exported as pending area；
Signal analysis equipment is connect with the parameter extraction equipment, for receiving the pending area, by the pending district
Domain is divided into object outline subregion and contents of object subregion, and the contents of object subregion is to divide in the pending area
Remaining pattern after cutting out the object outline subregion；
Noise measuring equipment is connect with the signal analysis equipment, various is made an uproar for determine in the object outline subregion
The maximum value of each amplitude of sound is using as profile noise maximum value, the various noises being also used in the contents of object subregion
Each amplitude maximum value using as content noise maximum value；
Self-adaptive processing equipment is connect with the body detection device and the noise measuring equipment respectively, in the wheel
When wide noise maximum value is greater than the content noise maximum value, the real-time grasp shoot image is executed and is opened after first corroding expansion process
The image processing mode of processing is closed, image is handled to obtain simultaneously output adaptive, is also used to small in the profile noise maximum value
When being equal to the content noise maximum value, the figure of first opening and closing processing post-etching expansion process is executed to the real-time grasp shoot image
As tupe, image is handled to obtain simultaneously output adaptive；
Dust extract equipment is connect with the self-adaptive processing equipment, for receiving the self-adaptive processing image, described in acquisition
Red color component value is fallen in default dust red lower threshold by the red color component value of each pixel in self-adaptive processing image
Pixel between default dust red upper limit threshold is confirmed as dust pixel；
Image is fitted equipment, connect with the dust extract equipment, for receiving multiple ashes in the self-adaptive processing image
The multiple dust pixel is fitted to one or more dust patterns by dirt pixel；
Reference value extract equipment is connect, for obtaining in one or more of dust patterns with described image fitting equipment
Maximum area dust pattern, and using the area of maximum area dust pattern as the first reference value, it will also be one or more of
The quantity of dust pattern is as the second reference value；
It falls ash and judges equipment, connect respectively with the reference value extract equipment and the dust monitoring device, is used for the glass
Glass dust thickness as third reference value, based on first reference value, second reference value and the third reference value into
Row weighted calculation falls ash with acquisition and judges parameter.
2. falling grey degree real-time monitoring system as described in claim 1, it is characterised in that:
It is described fall ash judge in equipment, be based on first reference value, second reference value and the third reference value into
In row weighted calculation, each weight coefficient of each reference value is different.
3. falling grey degree real-time monitoring system as claimed in claim 2, it is characterised in that:
The self-adaptive processing equipment includes that numerical value judges sub- equipment, microcontroller, the sub- equipment of corrosion expansion process and opening and closing
Manage sub- equipment, the microcontroller judges sub- equipment, the sub- equipment of the corrosion expansion process with the numerical value respectively and described opens
Close the sub- equipment connection of processing.
4. falling grey degree real-time monitoring system as claimed in claim 3, it is characterised in that:
The sub- equipment of the corrosion expansion process includes Image erosion unit and image expansion unit, described image erosion unit and institute
State the connection of image expansion unit, the sub- equipment of opening and closing processing includes that image opens processing unit and image closes processing unit, described
Image, which opens processing unit and closes processing unit with described image, to be connected.
5. falling grey degree real-time monitoring system as claimed in claim 4, it is characterised in that:
In the self-adaptive processing equipment, the numerical value judges sub- equipment for the profile noise maximum value and described interior
Hold noise maximum value and carries out numerical values recited judgement.
6. falling grey degree real-time monitoring system as claimed in claim 5, it is characterised in that:
In the self-adaptive processing equipment, the sub- equipment of the corrosion expansion process handles sub- equipment with the opening and closing and connect.
7. falling grey degree real-time monitoring system as claimed in claim 6, it is characterised in that:
The opening and closing handles the processing that the image that sub- equipment interconnection is received executes make before break, the sub- equipment pair of the corrosion expansion process
The processing expanded afterwards is first corroded in received image execution.
8. falling grey degree real-time monitoring system as claimed in claim 7, it is characterised in that:
Judge in equipment in the ash that falls, fall it is grey judge that parameter is bigger, house wall embedded glass to fall grey degree bigger.
9. falling grey degree real-time monitoring system as claimed in claim 8, it is characterised in that:
The dust extract equipment, described image fitting equipment, the reference value extract equipment and the ash that falls judge equipment quilt
It is arranged in the electronic-control box near house wall embedded glass.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811094919.6A CN110009592A (en) | 2018-09-19 | 2018-09-19 | Fall grey degree real-time monitoring system |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811094919.6A CN110009592A (en) | 2018-09-19 | 2018-09-19 | Fall grey degree real-time monitoring system |
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| Publication Number | Publication Date |
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| CN110009592A true CN110009592A (en) | 2019-07-12 |
Family
ID=67164936
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|---|---|---|---|
| CN201811094919.6A Withdrawn CN110009592A (en) | 2018-09-19 | 2018-09-19 | Fall grey degree real-time monitoring system |
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| CN (1) | CN110009592A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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