CN109447063A - A kind of kitchen fume concentration detection method based on image procossing - Google Patents
A kind of kitchen fume concentration detection method based on image procossing Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 35
- 239000003517 fume Substances 0.000 title claims abstract description 33
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- 239000008186 active pharmaceutical agent Substances 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims 2
- 238000011897 real-time detection Methods 0.000 abstract description 3
- 235000019504 cigarettes Nutrition 0.000 description 3
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- 230000000391 smoking effect Effects 0.000 description 1
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Abstract
A kind of kitchen fume concentration detection method based on image procossing, image processing unit is handled based on the initial pictures for the before and after frames that imaging device acquires, initial pictures are grayscale image, are carried out as follows: (1) carrying out frame difference to collected initial pictures and 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 are carried out to initial area-of-interest to calculate, the regions of gray average and smoothness requirements will be met simultaneously as next step area-of-interest, other regions are as interference elimination；(5) area-of-interest extracted to step (4) carries out statistics of histogram respectively, divides oil smoke concentration grade according to statistical result.The non-contact real-time detection of oil smoke concentration can be achieved in the present invention, has many advantages, such as high accuracy and real-time.
Description
Technical field
The present invention relates to kitchen fume processing technology fields, dense more particularly to a kind of kitchen fume based on image procossing
Spend detection method.
Background technique
Kitchen is one of configuration of herbal, and the effect of kitchen fume processing directly affects people's lives quality.With
Science and technology continuous development, it is also more and more to the monitoring and processing means of kitchen fume.It is several after traditional smoke exhaust ventilator setting
After a suction gear, has also appeared and kitchen fume concentration is carried out the technology such as to detect.
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 a kind of kitchen fume concentration detection method based on image procossing with gram
It is very necessary to take prior art deficiency.
Summary of the invention
A kind of kitchen oil based on image procossing is provided it is an object of the invention to avoid the deficiencies in the prior art place
Smoke density detection method has the characteristics that detection is real-time, oil smoke concentration testing result accuracy is high.
The object of the present invention is achieved by the following technical measures.
A kind of kitchen fume concentration detection method based on image procossing is provided, image processing unit is acquired with imaging device
Initial pictures based on handled, initial pictures are grayscale image, and initial pictures collected are serialized, and are 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；
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) counts respectively, divides oil smoke concentration etc. according to statistical result
Grade.
Further, in step (1), to collected initial pictures carry out frame difference operate to obtain frame difference image be specifically:
Image processing unit 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.
Further, the step (2) carries out denoising using opening operation to frame difference image, obtains denoising image, tool
Body carries out in the following way: first carrying out etching operation to frame difference image, to eliminate noise and the tiny spine in image, disconnects
Narrow connection；Expansive working is carried out to the image after corrosion again, restores the obvious characteristic in former frame difference image.
Further, the step (3) carries out edge detection to denoising image, and marker motion region is as initial interested
Region, specifically: utilizing wavelet transformation, detect the edge of frame difference image highlight regions and be marked, the region that will be marked
As initial area-of-interest.
Further, the step (4) is specifically to carry out gray average, segment smoothing degree to each initial area-of-interest
It calculates, obtains the corresponding gray average of each initial area-of-interest and gray scale smoothness, the ash being calculated will be met simultaneously
It spends mean value and is less than the initial area-of-interest of gray scale smoothness threshold as region of interest less than gray threshold, gray scale smoothness
Other initial area-of-interests are determined as interference region by domain.
Further, step (5), the area-of-interest extracted to step (4) carry out statistics of histogram, root respectively
Result divides oil smoke concentration grade according to statistics, 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.
Further, the above-mentioned kitchen fume concentration detection method based on image procossing, the target of imaging device acquisition
Region 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,jIt represents
I-th row, the corresponding gray value of jth column pixel in frame difference image D, the subregion in frame difference image D where the i-th row, jth column pixel
For DSi,j；
In frame difference image, | dhi,j|=0 region is in black；|dhi,j| ≠ 0 region is in be highlighted.
Further, etching operation is carried out to frame difference image in step (2), 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.
Further, 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 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, and 1≤f≤t, 1≤g≤t, e are pixel of the filter at each position
The gray value of the denoising image at place；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, 3 t.
Kitchen fume concentration detection method based on image procossing of the invention, provides one kind and is different from infrared projection method
With a kind of oil smoke concentration detection method of physical measure.The kitchen fume concentration detection method based on image procossing, almost
The influence of not 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.
Figure of description
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 the oil smoke region of method segmentation of the invention and the schematic diagram of interference region.
Specific embodiment
The invention will be further described with the following Examples.
Embodiment 1.
A kind of kitchen fume concentration detection method based on image procossing, image processing unit are acquired first with imaging device
It is handled based on beginning image, initial pictures are grayscale image, and initial pictures collected are serialized, and pass sequentially through rear frame
Initial pictures and the initial pictures of previous frame 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 at present frame moment can also be obtained in real time, also can according to need each even if monitoring
The oil smoke concentration situation of a moment current frame image provides 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) counts respectively, divides oil smoke concentration etc. according to statistical result
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: image procossing list
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 member
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,jIt represents
I-th row, the corresponding gray value of jth column pixel in frame difference image D, the subregion in frame difference image D where the i-th row, jth column pixel
For 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 obvious characteristic 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, and 1≤f≤t, 1≤g≤t, e are pixel of the filter at each position
The gray value of the denoising image at place；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. 1 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.
Kitchen fume concentration detection method based on image procossing of the invention, provides one kind and is different from infrared projection method
With a kind of oil smoke concentration detection method of physical measure.The kitchen fume concentration detection method based on image procossing, almost
The influence of not 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.
The present invention is based on the kitchen fume concentration detection methods of image procossing, can be set in kitchen ventilator, pass through oil smoke
The image of the imaging device acquisition kitchen ventilator stove head region of machine setting, and it is delivered to image processing unit, image processing unit will be located
The oil smoke hierarchical organization of reason is delivered to main control unit, and main control unit controls smoke machine extracting force according to the oil smoke grade of smoke machine.More
Add and suction process accurately is carried out to kitchen fume.
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 with modification or equivalent replacement of the technical solution of the present invention are made, without departing from technical solution of the present invention essence and
Range.
Claims (10)
1. a kind of kitchen fume concentration detection method based on image procossing, which is characterized in that image processing unit is set with being imaged
It being handled based on the initial pictures of standby acquisition, initial pictures are grayscale image, and initial pictures collected are serialized, according to
It is secondary to be handled by the initial pictures of rear frame and the initial pictures of previous frame, obtain working as moment locating for each rear frame initial pictures
Forward galley oil smoke concentration；
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) counts respectively, divides oil smoke concentration grade according to statistical result.
2. the kitchen fume concentration detection method according to claim 1 based on image procossing, which is characterized in that step
(1) in, to collected initial pictures carry out frame difference operate to obtain frame difference image be specifically:
Image processing unit 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.
3. the kitchen fume concentration detection method according to claim 2 based on image procossing, which is characterized in that the step
Suddenly (2) carry out denoising using opening operation to frame difference image, obtain denoising image, carry out especially by such as under type: first right
Frame difference image carries out etching operation and disconnects narrow connection to eliminate noise and the tiny spine in image；Again to corrosion after
Image carries out expansive working, restores the smoke characteristics in former frame difference image.
4. the kitchen fume concentration detection method according to claim 3 based on image procossing, which is characterized in that described
Step (3) carries out edge detection to denoising image, and marker motion region is as initial area-of-interest, specifically: detection frame is poor
The edge of image highlight area is simultaneously marked, using the region marked as initial area-of-interest.
5. the kitchen fume concentration detection method according to claim 4 based on image procossing, which is characterized in that described
Step (4) be specifically gray average is carried out to each initial area-of-interest, segment smoothing degree calculates, obtain each initially feeling emerging
The corresponding gray average in interesting region and gray scale smoothness will meet the gray average being calculated simultaneously and be less than gray threshold, ash
Initial area-of-interest of the smoothness less than gray scale smoothness threshold is spent as area-of-interest, by other initial area-of-interests
It is determined as interference region.
6. the kitchen fume concentration detection method according to claim 5 based on image procossing, which is characterized in that step
(5), the area-of-interest extracted to step (4) carries out statistics of histogram respectively, and it is dense to divide oil smoke according to statistical result
Grade is spent, 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.
7. the kitchen fume concentration detection method according to claim 6 based on image procossing, which is characterized in that imaging is set
The target area of standby 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 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,jIt is poor to represent frame
I-th row, the corresponding gray value of jth column pixel in image D, the subregion in frame difference image D where the i-th row, jth column pixel are
DSi,j；
In frame difference image, | dhi,j|=0 region is in black；|dhi,j| ≠ 0 region is in be highlighted.
8. the kitchen fume concentration detection method according to claim 7 based on image procossing, it is characterised in that: step
(2) etching operation is carried out to frame difference image in, 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.
9. the kitchen fume concentration detection method according to claim 8 based on image procossing, which is characterized in that the step
Suddenly (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 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
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 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.
10. the kitchen fume concentration detection method according to claim 9 based on image procossing, it is characterised in that: t 3.
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