CN109028224A - A kind of kitchen ventilator and oil smoke concentration detection method having light self-adaptive visual function - Google Patents
A kind of kitchen ventilator and oil smoke concentration detection method having light self-adaptive visual function Download PDFInfo
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- CN109028224A CN109028224A CN201811151552.7A CN201811151552A CN109028224A CN 109028224 A CN109028224 A CN 109028224A CN 201811151552 A CN201811151552 A CN 201811151552A CN 109028224 A CN109028224 A CN 109028224A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24C—DOMESTIC STOVES OR RANGES ; DETAILS OF DOMESTIC STOVES OR RANGES, OF GENERAL APPLICATION
- F24C15/00—Details
- F24C15/20—Removing cooking fumes
- F24C15/2021—Arrangement or mounting of control or safety systems
Abstract
A kind of kitchen ventilator having light self-adaptive visual function, it is provided with the variable wavelength formula lighting module of smoke machine main body, the vision-based detection module of light intensity for detecting environment oil smoke concentration and environment and automatic adjusument optical wavelength, vision-based detection module is assemblied in smoke machine main body and the corresponding kitchen range region of vision-based detection module direction, variable wavelength formula lighting module is assemblied in smoke machine main body, and vision-based detection module and variable wavelength formula lighting module are electrically connected with smoke machine main body respectively.The kitchen ventilator of the tool light self-adaptive visual function, different light environments can be monitored, the light of the capable of emitting different wave length of variable wavelength formula lighting module, variable wavelength are conducive to the acquisition of image to improve the clarity and the interference for the light for avoiding external environment of image.A kind of oil smoke concentration detection method, the influence of hardly examined distance, it can be achieved that oil smoke concentration non-contact real-time detection, have many advantages, such as high accuracy and real-time.
Description
Technical field
The present invention relates to kitchen ventilator field, in particular to a kind of kitchen ventilator for having light self-adaptive visual function and oil smoke are dense
Spend detection method.
Background technique
In the modern life, many families are all cooked using kitchen range, but existing range hood can not to hearth into
Row video monitoring, more cannot be to the wavelength regulation for the light for carrying out kitchen ventilator sending.
In the prior art, for the detection of kitchen fume concentration, mainly there are infrared projection method and physical measure.Infrared throwing
It penetrates method and infrared light is emitted by one end, the other end is received, and judges that oil smoke concentration is big by the infrared luminous intensity received
It is small.But there is uncertainty since oil smoke drifts, can also there be manpower in practice and the interference such as block, therefore, it need to be in different location
Multiple infrared transmitters are installed just and can guarantee relatively accurate, the higher cost of oil smoke detection, installation site are required also higher.Object
The principle that detection method is similar to smoke alarm is managed, oil smoke concentration, but this method are judged by floating particle number in detection air
There are two disadvantages can not achieve remote detection first is that detection must just can be carried out when oil smoke touches alarm；Second is that working as
Float in air when being oil smoke but water mist can not detect.
Therefore in view of the shortcomings of the prior art, provide it is a kind of have light self-adaptive visual function kitchen ventilator and oil smoke concentration inspection
Survey method is very necessary to solve prior art deficiency.
Summary of the invention
One of purpose of the invention is to avoid in place of the deficiencies in the prior art and to provide a kind of tool light adaptive
The kitchen ventilator of visual performance.The kitchen ventilator of the tool light self-adaptive visual function, which has, carries out vision accommodation according to ambient brightness
The advantages of.
Above-mentioned purpose of the invention is realized by following technical measures:
A kind of kitchen ventilator having light self-adaptive visual function is provided, is provided with smoke machine main body, for detecting environment oil smoke
The vision-based detection module of the light intensity of concentration and environment and the variable wavelength formula lighting module of automatic adjusument optical wavelength,
Vision-based detection module is assemblied in smoke machine main body and vision-based detection module is towards corresponding kitchen range region, and variable wavelength formula lighting module fills
Assigned in smoke machine main body, vision-based detection module and variable wavelength formula lighting module are electrically connected with smoke machine main body respectively.
Preferably, above-mentioned variable wavelength formula lighting module is distributed in vision-based detection module surrounding.
Preferably, above-mentioned variable wavelength formula lighting module is assemblied in smoke machine body surfaces and appointing towards the one side of kitchen range
Meaning position.
Preferably, above-mentioned vision-based detection module is handled and is serialized by the initial pictures acquired, is passed sequentially through
The initial pictures of frame and the initial pictures of previous frame are handled afterwards, and obtain the moment locating for each rear frame initial pictures works as forward galley
Oil smoke concentration.
Preferably, above-mentioned smoke machine main body is additionally provided with the dress of the control for being controlled variable wavelength formula lighting module
It sets, vision-based detection module and variable wavelength formula lighting module are electrically connected with control device respectively.
Vision-based detection module acquires the light intensity of hearth corresponding region environment, and light intensity signal is sent to control
Device, control device, which receives light intensity signal and handles, determines the corresponding grade of light intensity signal, control device transmission pair
The wavelength of light signal answered is to variable wavelength formula lighting module, the wavelength of light of variable wavelength formula lighting module receiving control device
Signal and the light for issuing corresponding wavelength.
Preferably, above-mentioned vision-based detection module is provided with apparatus main body, vision-based detection portion and prevents oil smoke or steam close
The positive splenium in vision-based detection portion, vision-based detection portion and positive laminate section are not assemblied in apparatus main body.
Preferably, above-mentioned apparatus main body is provided with wind cavity portion, and vision-based detection portion is defined as top towards shooting area, depending on
Feel that test section is assemblied in the lower section of wind cavity portion.
Preferably, the vision lens in above-mentioned vision-based detection portion pass through the through-hole and upward of wind cavity portion.
Preferably, above-mentioned positive splenium is assemblied in the top of wind cavity portion and the small air outlet of positive splenium towards wind cavity portion.
Preferably, above-mentioned wind cavity portion, which is provided with, generates the first wind chamber of air-flow and for from for accommodating positive splenium
The second wind chamber that the gas that one wind chamber air-flow enters raises speed, positive splenium are assemblied in the first wind chamber, vision lens position
In the second wind chamber interior, the first wind chamber and the second wind chamber.
Preferably, above-mentioned apparatus main body is additionally provided with upper cover and lower cover, top of the upper cover fixing buckle together in wind cavity portion, lower cover
It is assemblied in the bottom in vision-based detection portion.
Preferably, above-mentioned upper cover is provided with the small air inlet matched with positive splenium and small air outlet, vision lens pass through
Small air inlet and with the surface of upper cover maintain an equal level.
A kind of kitchen ventilator of tool light self-adaptive visual function of the invention, is provided with smoke machine main body, for detecting environment
The vision-based detection module of the light intensity of oil smoke concentration and environment and the variable wavelength formula illumination of automatic adjusument optical wavelength
Module, vision-based detection module are assemblied in smoke machine main body and the corresponding kitchen range region of vision-based detection module direction, variable wavelength formula illumination
Module is assemblied in smoke machine main body, and vision-based detection module and variable wavelength formula lighting module are electrically connected with smoke machine main body respectively.The tool
The kitchen ventilator of light self-adaptive visual function can be monitored different light environments, the variable wavelength formula lighting module
The light of capable of emitting different wave length, variable wavelength are conducive to the acquisition of image to improve the clarity of image and avoid outside
The interference of the light of environment.
Another goal of the invention of the invention is to avoid the deficiencies in the prior art place and provide a kind of oil smoke concentration detection
Method.The oil smoke concentration detection method has the characteristics that detection is real-time, oil smoke concentration testing result accuracy is high.
A kind of oil smoke concentration detection method is provided, there is the oil smoke of the tool light self-adaptive visual function such as features described above
Machine, vision-based detection module are handled based on the initial pictures that imaging device acquires, and initial pictures are grayscale image, are adopted
The initial pictures of collection are serialized, and the initial pictures of the initial pictures and previous frame that pass sequentially through rear frame are handled, and are obtained each
The current kitchen fume concentration at moment locating for frame initial pictures afterwards；
It is handled every time by the initial pictures of rear frame and the initial pictures of previous frame, when obtaining locating for rear frame initial pictures
The step process for the current kitchen fume concentration carved is as follows:
(1) initial pictures of rear frame and the initial pictures of previous frame frame difference is carried out to handle to obtain frame difference image；
(2) denoising is carried out to frame difference image in a manner of opening operation, obtains denoising image；
(3) edge detection is carried out to denoising image, marker motion region is as initial area-of-interest；
(4) gray average calculating is carried out to initial area-of-interest and segment smoothing degree calculates, it is equal gray scale will to be met simultaneously
The region of value and smoothness requirements is as next step area-of-interest, and other regions are as interference elimination；
(5) area-of-interest extracted to step (4) carries out statistics of histogram respectively, is divided according to statistical result
Oil smoke concentration grade.
In step (1), to collected initial pictures carry out frame difference operate to obtain frame difference image be specifically:
Vision-based detection module does a later frame image with previous frame image according to the sequencing of the initial pictures received
Difference obtains the highlighted frame difference image in dynamic area；
Preferably, above-mentioned steps (2) carry out denoising using opening operation to frame difference image, obtain denoising image, specifically
It carries out in the following way: etching operation first being carried out to frame difference image, to eliminate noise and the tiny spine in image, disconnect narrow
Small connection；Expansive working is carried out to the image after corrosion again, restores the smoke characteristics in former frame difference image.
Preferably, above-mentioned steps (3) carry out edge detection to denoising image, and marker motion region is as initial region of interest
Domain, specifically: utilizing wavelet transformation, detect the edge of frame difference image highlight regions and be marked, the region marked is made
For initial area-of-interest.
Preferably, above-mentioned steps (4) are specifically to carry out gray average, segment smoothing degree meter to each initial area-of-interest
It calculates, obtains the corresponding gray average of each initial area-of-interest and gray scale smoothness, the gray scale being calculated will be met simultaneously
Mean value is less than gray threshold, gray scale smoothness is less than the initial area-of-interest of gray scale smoothness threshold as area-of-interest,
Other initial area-of-interests are determined as interference region.
Preferably, the area-of-interest extracted in above-mentioned steps (5) to step (4) carries out grey level histogram system respectively
Meter divides oil smoke concentration grade according to statistical result, specifically.
By all pixels in region of interest area image, according to the size of gray value, the frequency of its appearance is counted；
Further according to the concentration scale quantity that needs divide, 10 are taken as siding-to-siding block length, counts the pixel in each gray scale interval
Number is put, the corresponding oil smoke that divides of the pixel number in each gray scale interval is corresponding concentration scale.
The target area of imaging device acquisition indicates that any one frame initial pictures are the imaging of corresponding region S with region S.
Initial pictures are made of m*n pixel.
The gray value of the pixel of frame initial pictures A is indicated afterwards with matrix A H, AH={ ahi,j, ahi,jFrame initial graph after representative
As the i-th row, the corresponding gray value of jth column pixel in A, i is the row where pixel, and j is the column where pixel, 1≤i≤m, 1≤j
≤n；The subregion in frame initial pictures A where the i-th row, jth column pixel is AS afterwardsi,j。
The gray value of the pixel of previous frame initial pictures B indicates with matrix B H, BH={ bhi,j, bhi,jRepresent previous frame initial graph
As the i-th row, the corresponding gray value of jth column pixel in B, the subregion in previous frame initial pictures B where the i-th row, jth column pixel is
BSi,j。
The grey scale pixel value of frame difference image D indicates with matrix D H,
DH={ dhi,j}={ ahi,j-bhi,j, dhi,jRepresent the i-th row in frame difference image D, the corresponding gray scale of jth column pixel
It is worth, the subregion in frame difference image D where the i-th row, jth column pixel is DSi,j。
In frame difference image, | dhi,j|=0 region is in black；|dhi,j| ≠ 0 region is in be highlighted.
Etching operation is carried out to frame difference image in step (2), is specifically comprised the following steps:
2-11 arbitrarily defines a convolution kernel θ；
Convolution kernel θ and frame difference image are carried out convolution by 2-12；When convolution kernel θ traverses frame difference image, convolution kernel institute is extracted
The pixel grey scale minimum value p of the convolution results and pixel C being overlapped with convolution kernel center in overlay area；
The gray scale of pixel C passes through Matrix C H={ ck,qIndicate, k, q are the row serial number and column serial number of pixel C,
Obtain the convolution results minimum value pixel matrix P obtained in convolution kernel θ traversal frame difference image process, minimum value
The gray scale of pixel matrix P passes through matrix PH={ pk,qIndicate；
The corresponding imparting pixel C of the gray scale of pixel matrix P is obtained corrosion image by 2-13；
Expansive working is carried out to corrosion image in step (2), is specifically comprised the following steps:
2-21 arbitrarily defines a convolution kernel β；
Convolution kernel β and corrosion image are carried out convolution by 2-22；When convolution kernel β traverses corrosion image, convolution kernel institute is extracted
The pixel grey scale maximum value o of the convolution results and pixel R being overlapped with convolution kernel center in overlay area；
The gray scale of pixel R passes through matrix RH={ rl,vIndicate, l, v are the row serial number and column serial number of pixel R,
Obtain the convolution results maximum value pixel matrix O obtained in convolution kernel β traversal corrosion image process, maximum value
The gray scale of pixel matrix O passes through matrix OH={ ol,vIndicate；
The corresponding imparting pixel R of the gray scale of maximum value pixel matrix O is obtained expanding image, obtained expansion by 2-13
Image is to denoise image.
Preferably, above-mentioned steps (3) carry out as follows:
3-1 defines a filter Y, and filter is t*t matrix, and t is odd number；
3-2 makes filter Y traversal denoising image, calculates filter and go where the central pixel point at each position
It makes an uproar the gray values of other pixels in the gray value and center pixel vertex neighborhood of image, and filter is calculated according to formula (I)
The edge detection value X of central pixel point at each positionz, z is label when filter Y traversal denoises image,
F, g is the matrix serial number of pixel, the pixel institute that 1≤f≤t, 1≤g≤t, e are filter at each position
Denoising image gray value；α is weight coefficient, corresponding with filter location；
3-3, by central pixel point edge detection value X of the filter at each positionzWith center pixel vertex neighborhood its
The gray value of its pixel subtracts each other, and judges whether the absolute value of difference is greater than threshold value Δ；
Statistics is greater than the quantity of threshold value, if quantity is more thanDetermine the central pixel point pair of filter present position
The pixel position for the denoising image answered is marginal point, and is marked；
3-4, complete denoising image of filter traversal, obtains the markd marginal point of institute, obtains preliminary area-of-interest.
Preferably, above-mentioned t is 3.
Oil smoke concentration detection method of the invention provides a kind of one kind for being different from infrared projection method and physical measure
Oil smoke concentration detection method.The oil smoke concentration detection method, the influence of hardly examined distance, it can be achieved that oil smoke concentration it is non-
Real-time detection is contacted, has many advantages, such as high accuracy and real-time.
Detailed description of the invention
Using attached drawing, the present invention is further illustrated, but the content in attached drawing is not constituted to any limit of the invention
System.
Fig. 1 is that a kind of kitchen ventilator signal for having light self-adaptive visual function of the present invention transmits relationship.
Fig. 2 is a kind of structural perspective of the kitchen ventilator of tool light self-adaptive visual function of embodiment 1.
Fig. 3 is the schematic cross-section of vision-based detection module.
Fig. 4 is vision-based detection module decomposition diagram.
Fig. 5 is the air current flow direction schematic diagram in Fig. 3.
Fig. 6 is a kind of structural perspective of the kitchen ventilator of tool light self-adaptive visual function of embodiment 2.
Fig. 7 is the oil smoke region of method segmentation of the invention and the schematic diagram of interference region.
Fig. 1 includes into Fig. 7:
Vision-based detection module 1,
Apparatus main body 11,
Wind cavity portion 111, the first wind chamber 1111, the second wind chamber 1112,
Upper cover 112, small air inlet 1121, small air outlet 1122,
Lower cover 113,
Positive splenium 12,
Vision-based detection portion 13, vision lens 131,
Variable wavelength formula lighting module 2,
Smoke machine main body 3,
Kitchen range 4.
Specific embodiment
Technical solution of the present invention is described further with the following Examples.
Embodiment 1.
It is a kind of have light self-adaptive visual function kitchen ventilator be provided with smoke machine main body 3, for examining as shown in Fig. 1 to 5
Survey the vision-based detection module 1 of the light intensity of environment oil smoke concentration and environment and the variable wavelength of automatic adjusument optical wavelength
Formula lighting module 2, vision-based detection module 1 is assemblied in smoke machine main body 3 and vision-based detection module 1 is towards corresponding kitchen range region, can be changed
Wavelength type lighting module 2 is assemblied in smoke machine main body 3, vision-based detection module 1 and variable wavelength formula lighting module 2 respectively with smoke machine master
Body 3 is electrically connected.
The variable wavelength formula lighting module 2 of the present embodiment is distributed in 1 surrounding of vision-based detection module.
Vision-based detection module 1 is handled and is serialized by the initial pictures acquired, and the initial of rear frame is passed sequentially through
Image and the initial pictures of previous frame are handled, and the current kitchen fume concentration at moment locating for each rear frame initial pictures is obtained.
Smoke machine main body 3 is additionally provided with the control device for being controlled variable wavelength formula lighting module 2, vision-based detection
Module 1 and variable wavelength formula lighting module 2 are electrically connected with control device respectively.
Vision-based detection module 1 acquires the light intensity of hearth corresponding region environment, and light intensity signal is sent to control
Device processed, control device, which receives light intensity signal and handles, determines the corresponding grade of light intensity signal, and control device is sent
Corresponding wavelength of light signal is to variable wavelength formula lighting module 2, the light of 2 receiving control device of variable wavelength formula lighting module
Wavelength signals and the light for issuing corresponding wavelength.
The course of work of the invention is as follows: vision-based detection module 1 acquires the light intensity of hearth corresponding region environment, and will
Light intensity signal is sent to control device, and control device, which receives light intensity signal and handles, determines that light intensity signal is corresponding
Grade, control device sends corresponding wavelength of light signal to variable wavelength formula lighting module 2, variable wavelength formula lighting module
The wavelength of light signal of 2 receiving control devices and the light for issuing corresponding wavelength, the variable wavelength formula lighting module 2 is according to difference
The capable of emitting different wave length of environment light, variable wavelength be conducive to image acquisition to improve image clarity and keep away
Exempt from the interference of the light of external environment.
Vision-based detection module 1 is provided with apparatus main body 11, vision-based detection portion 13 and oil smoke or steam is prevented to examine close to vision
The positive splenium 12 in survey portion 13, vision-based detection portion 13 and positive splenium 12 are assemblied in apparatus main body 11 respectively.
Apparatus main body 11 is provided with wind cavity portion 111, and vision-based detection portion 13 is defined as top, vision inspection towards shooting area
Survey portion 13 is assemblied in the lower section of wind cavity portion 111.
The vision lens 131 in vision-based detection portion 13 pass through the through-hole and upward of wind cavity portion 111.Positive splenium 12 is assemblied in
The small air outlet 1122 of the top of wind cavity portion 111 and positive splenium 12 is towards wind cavity portion 111.
Wind cavity portion 111, which is provided with, generates the first wind chamber 1111 of air-flow and for from first for accommodating positive splenium 12
The second wind chamber 1112 that the gas that 1111 air-flow of wind chamber enters raises speed, positive splenium 12 are assemblied in the first wind chamber
1111, vision lens 131 are located inside the second wind chamber 1112, and the first wind chamber 1111 is connected to the second wind chamber 1112.
Apparatus main body 11 is additionally provided with upper cover 112 and lower cover 113,112 fixing buckle of upper cover together in wind cavity portion 111 top,
Lower cover 113 is assemblied in the bottom in vision-based detection portion 13.
Upper cover 112 is provided with the small air inlet 1121 and small air outlet 1122 matched with positive splenium 12, vision lens 131
Maintain an equal level across small air inlet 1121 and with the surface of upper cover 112.
The air-flow flow process of vision-based detection module 1 of the invention is as follows: small air inlet of the positive splenium 12 from upper cover 112
1121 sucking gases, positive splenium 12 again arrange gas to the first wind cavity, and gas flows into the second wind chamber from the first wind chamber 1111
Room 1112, because the unappropriated volume of the second wind chamber 1112 is less than the unappropriated volume of the second wind chamber 1112,
Gas is raised speed in the second wind chamber 1112, gap of the gas to be raised speed using vision lens 131 and cone structure, gas
Body finally at full throttle leaves the positive pressure anti-soil type sighting device, and gas is formed centainly between vision lens 131 and smog
Positive pressure, so that smog can not contact vision lens 131.
The positive splenium 12 of the vision-based detection module 1 generates gas at a high speed from the table of the vision lens 131 of vision-based detection module 1
Surface current mistake, so that certain positive pressure is formed between vision lens 131 and smog, so that smog can not contact vision lens 131.
The vision-based detection module 1 can prevent the attachment of oil smoke or steam.
The kitchen ventilator of the tool light self-adaptive visual function, be provided with smoke machine main body 3, for detect environment oil smoke concentration with
And the light intensity of environment vision-based detection module 1 and automatic adjusument optical wavelength variable wavelength formula lighting module 2, vision
Detection module 1 is assemblied in smoke machine main body 3 and vision-based detection module 1 is towards corresponding kitchen range region, and variable wavelength formula lighting module 2 fills
Assigned in smoke machine main body 3, vision-based detection module 1 and variable wavelength formula lighting module 2 are electrically connected with smoke machine main body 3 respectively.The tool light
The kitchen ventilator of line self-adaptive visual function can be monitored different light environments, which can
The light of different wave length is issued, variable wavelength is conducive to the acquisition of image to improve the clarity of image and avoid external rings
The interference of the light in border.
Embodiment 2.
A kind of kitchen ventilator having light self-adaptive visual function, as shown in fig. 6, other features are same as Example 1, it is different
Place is, the variable wavelength formula lighting module 2 of the present embodiment is assemblied in 3 surface of smoke machine main body and towards the one side of kitchen range
Any position.
Compared with Example 1, the variable wavelength formula lighting module 2 of the present embodiment is assemblied in 3 surface of smoke machine main body and direction
Any position of the one side of kitchen range can make light source dispersion have more accurately detection to oil smoke.
Embodiment 3.
A kind of oil smoke concentration detection method, vision-based detection module 1 based on the initial pictures that imaging device acquires into
Row processing, initial pictures are grayscale image, and initial pictures collected are serialized, and pass sequentially through the initial pictures and previous frame of rear frame
Initial pictures handled, obtain it is each after the moment locating for frame initial pictures current kitchen fume concentration.In this way,
The oil smoke concentration situation that can also obtain the present frame moment in real time also can according to need even if monitoring each moment current frame image
Oil smoke concentration situation, provide foundation for the automatic smoking dynamics of kitchen ventilator.
It is handled every time by the initial pictures of rear frame and the initial pictures of previous frame, when obtaining locating for rear frame initial pictures
The step process for the current kitchen fume concentration carved is as follows:
(1) initial pictures of rear frame and the initial pictures of previous frame frame difference is carried out to handle to obtain frame difference image；
(2) denoising is carried out to frame difference image in a manner of opening operation, obtains denoising image；
(3) edge detection is carried out to denoising image, marker motion region is as initial area-of-interest；
(4) gray average calculating is carried out to initial area-of-interest and segment smoothing degree calculates, it is equal gray scale will to be met simultaneously
The region of value and smoothness requirements is as next step area-of-interest, and other regions are as interference elimination；
(5) area-of-interest extracted to step (4) carries out statistics of histogram respectively, is divided according to statistical result
Oil smoke concentration grade.Statistical method can be statistics of histogram, also can choose other statistical methods.
In step (1), to collected initial pictures carry out frame difference operate to obtain frame difference image be specifically: vision-based detection mould
A later frame image is made the difference with previous frame image according to the sequencing of the initial pictures received, obtains dynamic area height by block 1
Bright frame difference image.Due in the two field pictures of front and back static region be it is constant, (such as oil smoke drifts, and manpower is waved for dynamic area
Move) it is variation, so black is presented in static region after frame difference, the highlight bar of edge blurry is shown as after dynamic area frame difference
Domain, therefore the frame difference image highlighted by the available dynamic area of frame difference.
The target area of imaging device acquisition indicates that any one frame initial pictures are the imaging of corresponding region S with region S；
Initial pictures are made of m*n pixel.
The gray value of the pixel of frame initial pictures A is indicated afterwards with matrix A H, AH={ ahi,j, ahi,jFrame initial graph after representative
As the i-th row, the corresponding gray value of jth column pixel in A, i is the row where pixel, and j is the column where pixel, 1≤i≤m, 1≤j
≤n；The subregion in frame initial pictures A where the i-th row, jth column pixel is AS afterwardsi,j。
The gray value of the pixel of previous frame initial pictures B indicates with matrix B H, BH={ bhi,j, bhi,jRepresent previous frame initial graph
As the i-th row, the corresponding gray value of jth column pixel in B, the subregion in previous frame initial pictures B where the i-th row, jth column pixel is
BSi,j。
The grey scale pixel value of frame difference image D indicates with matrix D H,
DH={ dhi,j}={ ahi,j-bhi,j, dhi,jRepresent the i-th row in frame difference image D, the corresponding gray scale of jth column pixel
It is worth, the subregion in frame difference image D where the i-th row, jth column pixel is DSi,j；
In frame difference image, | dhi,j|=0 region is in black；|dhi,j| ≠ 0 region is in be highlighted.
After the operation of frame difference, (2) are entered step.Denoising is carried out using opening operation to frame difference image, obtains denoising image,
It is carried out especially by such as under type: etching operation first being carried out to frame difference image, to eliminate noise and the tiny spine in image, broken
Open narrow connection；Expansive working is carried out to the image after corrosion again, restores the smoke characteristics in former frame difference image.
Etching operation is carried out to frame difference image in step (2), is specifically comprised the following steps:
2-11 arbitrarily defines a convolution kernel θ；
Convolution kernel θ and frame difference image are carried out convolution by 2-12；When convolution kernel θ traverses frame difference image, convolution kernel institute is extracted
The pixel grey scale minimum value p of the convolution results and pixel C being overlapped with convolution kernel center in overlay area；
The gray scale of pixel C passes through Matrix C H={ ck,qIndicate, k, q are the row serial number and column serial number of pixel C,
Obtain the convolution results minimum value pixel matrix P obtained in convolution kernel θ traversal frame difference image process, minimum value
The gray scale of pixel matrix P passes through matrix PH={ pk,qIndicate；
The corresponding imparting pixel C of the gray scale of pixel matrix P is obtained corrosion image by 2-13.
Expansive working is carried out to corrosion image in step (2), is specifically comprised the following steps:
2-21 arbitrarily defines a convolution kernel β；
Convolution kernel β and corrosion image are carried out convolution by 2-22；When convolution kernel β traverses corrosion image, convolution kernel institute is extracted
The pixel grey scale maximum value o of the convolution results and pixel R being overlapped with convolution kernel center in overlay area；
The gray scale of pixel R passes through matrix RH={ rl,vIndicate, l, v are the row serial number and column serial number of pixel R,
Obtain the convolution results maximum value pixel matrix O obtained in convolution kernel β traversal corrosion image process, maximum value
The gray scale of pixel matrix O passes through matrix OH={ ol,vIndicate；
The corresponding imparting pixel R of the gray scale of maximum value pixel matrix O is obtained expanding image, obtained expansion by 2-13
Image is to denoise image.
Image noise can be eliminated using opening operation, the separating objects at very thin point, smooth biggish object boundary, simultaneously
Also it can guarantee that the area of highlight regions in original image is basically unchanged, guarantee that the accuracy of subsequent detection is unaffected.
Step (3) carries out edge detection to denoising image, and marker motion region is as initial area-of-interest, specifically:
Using wavelet transformation, the edge for detecting frame difference image highlight regions is simultaneously marked, using the region marked as initially feeling emerging
Interesting region.
Since the gray value of image border and the gray value of neighbor pixel can generate biggish gray value gradient, according to side
This feature of edge sets a filter, traverses frame difference image with the filter.Step (3) carries out as follows:
3-1 defines a filter Y, and filter is t*t matrix, and t is odd number.Filter selects odd matrix, to ensure
Only one central point, preferably 3*3 matrix, have the characteristics that calculation amount is small.
3-2 makes filter Y traversal denoising image, calculates filter and go where the central pixel point at each position
It makes an uproar the gray values of other pixels in the gray value and center pixel vertex neighborhood of image, and filter is calculated according to formula (I)
The edge detection value X of central pixel point at each positionz, z is label when filter Y traversal denoises image,
F, g is the matrix serial number of pixel, the pixel institute that 1≤f≤t, 1≤g≤t, e are filter at each position
Denoising image gray value；α is weight coefficient, corresponding with filter location.
3-3, by central pixel point edge detection value X of the filter at each positionzWith center pixel vertex neighborhood its
The gray value of its pixel subtracts each other, and judges whether the absolute value of difference is greater than threshold value Δ；
Statistics is greater than the quantity of threshold value, if quantity is more thanDetermine the central pixel point pair of filter present position
The pixel position for the denoising image answered is marginal point, and is marked；
3-4, complete denoising image of filter traversal, obtains the markd marginal point of institute, obtains preliminary area-of-interest.
Because people is when cooking operation, hand can brandished always, can include oil smoke and manpower in the image after frame difference is complete
The interference region of the moving objects such as operation, needs the influence in exclusive PCR region, this is also before carrying out oil smoke concentration identification
Where the difficult point of the invention patent.
But the direction of motion of oil smoke has randomness, the direction of motion of manpower, slice is relatively unambiguous and feature is different,
Numerically performance is exactly that grey value difference is larger, thus:
1) oil smoke moving region is lower than the brightness of manpower, slice moving region on the image after frame difference, so corresponding oil
The gray value mean value in cigarette district domain is also below manpower, the gray average of slice moving region；
2) grey value profile of oil smoke moving region is relatively concentrated on the image after frame difference, and the moving region of manpower, slice
The gray value on boundary is larger compared with the jump of the central area in region, so the image in the region is not smooth enough, corresponding gray value
Variance is larger.
Using the two characteristics, step (4) is specifically to carry out gray average, segment smoothing to each initial area-of-interest
Degree calculates, and obtains the corresponding gray average of each initial area-of-interest and gray scale smoothness, and satisfaction simultaneously is calculated
Gray average is less than gray threshold, gray scale smoothness is less than the initial area-of-interest of gray scale smoothness threshold as region of interest
Other initial area-of-interests are determined as interference region by domain.
Gray threshold, gray scale smoothness threshold magnitude can flexible setting according to specific needs, details are not described herein.Step
Suddenly (4) complete the identification in oil smoke region and the exclusion of interference region.
Fig. 7 illustrates the schematic diagram in oil smoke region and interference region that one is divided using method of the invention, it is seen then that this
The method of invention can effectively exclude interference region.
The area-of-interest extracted in step (5) to step (4) carries out statistics of histogram respectively, is tied according to statistics
Fruit divides oil smoke concentration grade, specifically:
By all pixels in region of interest area image, according to the size of gray value, the frequency of its appearance is counted；
Further according to the concentration scale quantity that needs divide, 10 are taken as siding-to-siding block length, counts the pixel in each gray scale interval
Number is put, the corresponding oil smoke that divides of the pixel number in each gray scale interval is corresponding concentration scale.
It should be noted that the selection of siding-to-siding block length is not limited to 10, other quantity also can choose.
The criteria for classifying of oil smoke concentration can specifically be set, such as setting dense smoke, medium grade cigarette or low cigarette, specific value with
Subject to actual demand, details are not described herein.
Oil smoke concentration detection method of the invention provides a kind of one kind for being different from infrared projection method and physical measure
Oil smoke concentration detection method.The oil smoke concentration detection method, the influence of hardly examined distance, it can be achieved that oil smoke concentration it is non-
Real-time detection is contacted, has many advantages, such as high accuracy and real-time.
Oil smoke concentration detection method of the present invention, can be set in kitchen ventilator, be adopted by the imaging device that kitchen ventilator is arranged
The image for collecting kitchen ventilator stove head region, and is delivered to vision-based detection module 1, and vision-based detection module 1 is by the oil smoke hierarchical organization of processing
It is delivered to main control unit, main control unit controls smoke machine extracting force according to the oil smoke grade of smoke machine.More accurately to kitchen oil
Cigarette carries out suction process.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention rather than protects to the present invention
The limitation of range, although the invention is described in detail with reference to the preferred embodiments, those skilled in the art should be managed
Solution, can be with modifying or equivalently replacing the technical solution of the present invention, without departing from the essence and model of technical solution of the present invention
It encloses.
Claims (10)
1. a kind of kitchen ventilator for having light self-adaptive visual function, it is characterised in that: it is provided with smoke machine main body, for detecting environment
The vision-based detection module of the light intensity of oil smoke concentration and environment and the variable wavelength formula illumination of automatic adjusument optical wavelength
Module, vision-based detection module are assemblied in smoke machine main body and the corresponding kitchen range region of vision-based detection module direction, variable wavelength formula illumination
Module is assemblied in smoke machine main body, and vision-based detection module and variable wavelength formula lighting module are electrically connected with smoke machine main body respectively.
2. the kitchen ventilator of tool light self-adaptive visual function according to claim 1, it is characterised in that: the variable wavelength
Formula lighting module is distributed in vision-based detection module surrounding.
3. the kitchen ventilator of tool light self-adaptive visual function according to claim 1, it is characterised in that: the variable wavelength
Formula lighting module is assemblied in any position of smoke machine body surfaces and the one side towards kitchen range.
4. having the kitchen ventilator of light self-adaptive visual function according to claim 2 or 3, it is characterised in that: the view
Feel that detection module is handled and is serialized by the initial pictures of acquisition, passes sequentially through the initial pictures and previous frame of rear frame
Initial pictures are handled, and the current kitchen fume concentration at moment locating for each rear frame initial pictures is obtained；
The smoke machine main body is additionally provided with the control device for being controlled variable wavelength formula lighting module, vision-based detection mould
Block and variable wavelength formula lighting module are electrically connected with control device respectively；
Vision-based detection module acquires the light intensity of hearth corresponding region environment, and light intensity signal is sent to control dress
It sets, control device, which receives light intensity signal and handles, determines the corresponding grade of light intensity signal, and control device, which is sent, to be corresponded to
Wavelength of light signal to variable wavelength formula lighting module, the wavelength of light of variable wavelength formula lighting module receiving control device is believed
Number and issue the light of corresponding wavelength.
5. the kitchen ventilator of tool light self-adaptive visual function according to claim 4, it is characterised in that: the vision-based detection
Module is provided with apparatus main body, vision-based detection portion and prevents oil smoke or steam close to the positive splenium in vision-based detection portion, vision-based detection
Portion and positive laminate section are not assemblied in apparatus main body.
6. the kitchen ventilator of tool light self-adaptive visual function according to claim 5, it is characterised in that: described device main body
It is provided with wind cavity portion, vision-based detection portion is defined as top towards shooting area, vision-based detection portion is assemblied in the lower section of wind cavity portion；
The vision lens in the vision-based detection portion pass through the through-hole and upward of wind cavity portion；
The positive splenium is assemblied in the small air outlet of the top of wind cavity portion and positive splenium towards wind cavity portion.
7. the kitchen ventilator of tool light self-adaptive visual function according to claim 6, it is characterised in that: the wind cavity portion is set
It is equipped with and generates the first wind chamber of air-flow and for carrying out to the gas entered from the first wind chamber air-flow for accommodating positive splenium
Second wind chamber of speed-raising, positive splenium are assemblied in the first wind chamber, and vision lens are located at the second wind chamber interior, the first wind chamber
With the second wind chamber；
Described device main body is additionally provided with upper cover and lower cover, and for upper cover fixing buckle together in the top of wind cavity portion, lower cover is assemblied in vision
The bottom of test section；
The upper cover is provided with the small air inlet and small air outlet matched with positive splenium, vision lens pass through small air inlet and with
The surface of upper cover maintains an equal level.
8. a kind of oil smoke concentration detection method, which is characterized in that have the tool light such as claim 1 to 7 any one feature
The kitchen ventilator of self-adaptive visual function, vision-based detection module are handled based on the initial pictures that imaging device acquires,
Initial pictures are grayscale image, and initial pictures collected are serialized, pass sequentially through rear frame initial pictures and previous frame it is initial
Image is handled, and the current kitchen fume concentration at moment locating for each rear frame initial pictures is obtained；
It is handled every time by the initial pictures of rear frame and the initial pictures of previous frame, obtains the moment locating for rear frame initial pictures
The step process of current kitchen fume concentration is as follows:
(1) initial pictures of rear frame and the initial pictures of previous frame frame difference is carried out to handle to obtain frame difference image；
(2) denoising is carried out to frame difference image in a manner of opening operation, obtains denoising image；
(3) edge detection is carried out to denoising image, marker motion region is as initial area-of-interest；
(4) gray average calculating and segment smoothing degree is carried out to initial area-of-interest to calculate, will meet simultaneously gray average and
The region of smoothness requirements is as next step area-of-interest, and other regions are as interference elimination；
(5) area-of-interest extracted to step (4) carries out statistics of histogram respectively, divides oil smoke according to statistical result
Concentration scale.
9. oil smoke concentration detection method according to claim 8, which is characterized in that in step (1), to collected initial
Image progress frame difference operates to obtain frame difference image:
Vision-based detection module makes the difference a later frame image with previous frame image according to the sequencing of the initial pictures received,
Obtain the highlighted frame difference image in dynamic area；
The step (2) carries out denoising using opening operation to frame difference image, denoising image is obtained, especially by such as under type
It carries out: etching operation first being carried out to frame difference image and disconnects narrow connection to eliminate noise and the tiny spine in image；Again
Expansive working is carried out to the image after corrosion, restores the smoke characteristics in former frame difference image；
The step (3) carries out edge detection to denoising image, and marker motion region is as initial area-of-interest, specifically:
Using wavelet transformation, the edge for detecting frame difference image highlight regions is simultaneously marked, using the region marked as initially feeling emerging
Interesting region；
The step (4) is specifically to carry out gray average, the calculating of segment smoothing degree to each initial area-of-interest, is obtained each
The initial corresponding gray average of area-of-interest and gray scale smoothness will meet the gray average being calculated simultaneously and be less than gray scale
Threshold value, gray scale smoothness are less than the initial area-of-interest of gray scale smoothness threshold as area-of-interest, by other initial senses
Interest region is determined as interference region；
The area-of-interest extracted in the step (5) to step (4) carries out statistics of histogram respectively, is tied according to statistics
Fruit divides oil smoke concentration grade, specifically:
By all pixels in region of interest area image, according to the size of gray value, the frequency of its appearance is counted；
Further according to the concentration scale quantity that needs divide, 10 are taken as siding-to-siding block length, count the pixel in each gray scale interval
It counts, the corresponding oil smoke that divides of the pixel number in each gray scale interval is corresponding concentration scale.
10. oil smoke concentration detection method according to claim 9, which is characterized in that the target area of imaging device acquisition
It is indicated with region S, any one frame initial pictures are the imaging of corresponding region S；
Initial pictures are made of m*n pixel,
The gray value of the pixel of frame initial pictures A is indicated afterwards with matrix A H, AH={ ahi,j, ahi,jFrame initial pictures A after representative
In the i-th row, the corresponding gray value of jth column pixel, i be pixel where row, j be pixel where column, 1≤i≤m, 1≤j≤
n；The subregion in frame initial pictures A where the i-th row, jth column pixel is AS afterwardsi,j；
The gray value of the pixel of previous frame initial pictures B indicates with matrix B H, BH={ bhi,j, bhi,jRepresent previous frame initial pictures B
In the i-th row, the corresponding gray value of jth column pixel, the subregion in previous frame initial pictures B where the i-th row, jth column pixel is
BSi,j；
The grey scale pixel value of frame difference image D indicates with matrix D H, DH={ dhi,j}={ ahi,j-bhi,j, dhi,jRepresent frame difference figure
As the i-th row, the corresponding gray value of jth column pixel in D, the subregion in frame difference image D where the i-th row, jth column pixel is
DSi,j；
In frame difference image, | dhi,j|=0 region is in black；|dhi,j| ≠ 0 region is in be highlighted；
Etching operation is carried out to frame difference image in step (2), is specifically comprised the following steps:
2-11 arbitrarily defines a convolution kernel θ；
Convolution kernel θ and frame difference image are carried out convolution by 2-12；When convolution kernel θ traverses frame difference image, extracts convolution kernel and covered
The pixel grey scale minimum value p of the convolution results and pixel C being overlapped with convolution kernel center in region；
The gray scale of pixel C passes through Matrix C H={ ck,qIndicate, k, q are the row serial number and column serial number of pixel C,
Obtain the convolution results minimum value pixel matrix P obtained in convolution kernel θ traversal frame difference image process, minimum value pixel
The gray scale of dot matrix P passes through matrix PH={ pk,qIndicate；
The corresponding imparting pixel C of the gray scale of pixel matrix P is obtained corrosion image by 2-13；
Expansive working is carried out to corrosion image in step (2), is specifically comprised the following steps:
2-21 arbitrarily defines a convolution kernel β；
Convolution kernel β and corrosion image are carried out convolution by 2-22；When convolution kernel β traverses corrosion image, extracts convolution kernel and covered
The pixel grey scale maximum value o of the convolution results and pixel R being overlapped with convolution kernel center in region；
The gray scale of pixel R passes through matrix RH={ rl,vIndicate, l, v are the row serial number and column serial number of pixel R,
Obtain the convolution results maximum value pixel matrix O obtained in convolution kernel β traversal corrosion image process, maximum value pixel
The gray scale of dot matrix O passes through matrix OH={ ol,vIndicate；
The corresponding imparting pixel R of the gray scale of maximum value pixel matrix O is obtained expanding image, obtained expanding image by 2-13
As denoise image；
The step (3) carries out as follows:
3-1 defines a filter Y, and filter is t*t matrix, and t is odd number；
3-2 makes filter Y traversal denoising image, calculates filter in the denoising figure where the central pixel point at each position
The gray value of other pixels in the gray value and center pixel vertex neighborhood of picture, and filter is calculated every according to formula (I)
The edge detection value X of central pixel point at one positionz, z is label when filter Y traversal denoises image,
F, g is the matrix serial number of pixel, and 1≤f≤t, 1≤g≤t, e are filter where the pixel at each position
Denoise the gray value of image；α is weight coefficient, corresponding with filter location；
3-3, by central pixel point edge detection value X of the filter at each positionzWith other pixels of center pixel vertex neighborhood
The gray value of point subtracts each other, and judges whether the absolute value of difference is greater than threshold value Δ；
Statistics is greater than the quantity of threshold value, if quantity is more thanDetermine that the central pixel point of filter present position is corresponding
The pixel position for denoising image is marginal point, and is marked；
3-4, complete denoising image of filter traversal, obtains the markd marginal point of institute, obtains preliminary area-of-interest；
The t is 3.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109654560A (en) * | 2018-12-29 | 2019-04-19 | 佛山市云米电器科技有限公司 | It is a kind of can according to use oils carry out kitchen air quality assessment kitchen ventilator |
| CN111006261A (en) * | 2019-12-31 | 2020-04-14 | 青岛海尔智慧厨房电器有限公司 | Range hood control method and device, range hood and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070103408A1 (en) * | 2005-11-09 | 2007-05-10 | Mitsuru Tada | Self-luminous display apparatus, light emission condition control apparatus, light emission condition control method and program |
| CN202353878U (en) * | 2011-11-24 | 2012-07-25 | 樱花卫厨（中国）股份有限公司 | Automatic brightness regulating and controlling system of lighting lamp of range hood |
| CN204350385U (en) * | 2015-01-08 | 2015-05-20 | 清华大学苏州汽车研究院（吴江） | A kind of Intelligent street lamp control system of based target identification |
| CN107690217A (en) * | 2017-11-23 | 2018-02-13 | 成都长宇老司机网络科技有限公司 | A kind of energy-saving intelligent street lamp |
| CN107726399A (en) * | 2017-11-07 | 2018-02-23 | 佛山市云米电器科技有限公司 | Cooking fume-collecting system, method and device |
| CN207280492U (en) * | 2017-09-14 | 2018-04-27 | 安徽瀚和智能科技有限公司 | A kind of detecting system based on machine vision |
| CN108564091A (en) * | 2018-03-08 | 2018-09-21 | 佛山市云米电器科技有限公司 | Target area weak boundary extracting method and oil smoke concentration detection and interference elimination method |
| CN209013296U (en) * | 2018-09-29 | 2019-06-21 | 佛山市云米电器科技有限公司 | A kind of kitchen ventilator having light self-adaptive visual function |
-
2018
- 2018-09-29 CN CN201811151552.7A patent/CN109028224A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070103408A1 (en) * | 2005-11-09 | 2007-05-10 | Mitsuru Tada | Self-luminous display apparatus, light emission condition control apparatus, light emission condition control method and program |
| CN202353878U (en) * | 2011-11-24 | 2012-07-25 | 樱花卫厨（中国）股份有限公司 | Automatic brightness regulating and controlling system of lighting lamp of range hood |
| CN204350385U (en) * | 2015-01-08 | 2015-05-20 | 清华大学苏州汽车研究院（吴江） | A kind of Intelligent street lamp control system of based target identification |
| CN207280492U (en) * | 2017-09-14 | 2018-04-27 | 安徽瀚和智能科技有限公司 | A kind of detecting system based on machine vision |
| CN107726399A (en) * | 2017-11-07 | 2018-02-23 | 佛山市云米电器科技有限公司 | Cooking fume-collecting system, method and device |
| CN107690217A (en) * | 2017-11-23 | 2018-02-13 | 成都长宇老司机网络科技有限公司 | A kind of energy-saving intelligent street lamp |
| CN108564091A (en) * | 2018-03-08 | 2018-09-21 | 佛山市云米电器科技有限公司 | Target area weak boundary extracting method and oil smoke concentration detection and interference elimination method |
| CN209013296U (en) * | 2018-09-29 | 2019-06-21 | 佛山市云米电器科技有限公司 | A kind of kitchen ventilator having light self-adaptive visual function |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109654560A (en) * | 2018-12-29 | 2019-04-19 | 佛山市云米电器科技有限公司 | It is a kind of can according to use oils carry out kitchen air quality assessment kitchen ventilator |
| CN111006261A (en) * | 2019-12-31 | 2020-04-14 | 青岛海尔智慧厨房电器有限公司 | Range hood control method and device, range hood and storage medium |
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