CN108563991A - Kitchen fume concentration division methods and oil smoke concentration detection and interference elimination method - Google Patents
Kitchen fume concentration division methods and oil smoke concentration detection and interference elimination method Download PDFInfo
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- CN108563991A CN108563991A CN201810191906.4A CN201810191906A CN108563991A CN 108563991 A CN108563991 A CN 108563991A CN 201810191906 A CN201810191906 A CN 201810191906A CN 108563991 A CN108563991 A CN 108563991A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
Abstract
Kitchen fume concentration division methods, include the following steps：It is poor that frame is done to the kitchen fume image of collected serializing；Grey level histogram function is write, the grey level histogram of frame difference image is found out；The pixel number in each section of statistic histogram；According to the high, normal, basic three grades of kitchen fume concentration of setting, in conjunction with the statistical result of different frame difference figure, the kitchen fume at each moment is divided into corresponding concentration scale.It is an object of the invention to propose kitchen fume concentration division methods and oil smoke concentration detection and interference elimination method, this method is counted based on image grey level histogram, the method provided through the invention, the oil smoke concentration that pixel rank can be accurate to successive frame oil smoke image divides, and has many advantages, such as high accuracy and real-time continuous property.
Description
Technical field
The present invention relates to oil smoke detection technique field more particularly to kitchen fume concentration division methods and oil smoke concentration to detect
With interference elimination method.
Background technology
Being directed to kitchen fume Concentration Testing at this stage, there are mainly two types of methods (infrared projection method and physical measure), but this
A little methods are largely the feature extractions such as, oil smoke diffusion fuzzy for image caused by kitchen fume cannot embody oil smoke
Substantive characteristics, be easy to cause erroneous judgement.
Invention content
It is an object of the invention to solve the above problems propose kitchen fume concentration division methods and oil smoke concentration detection with
Interference elimination method.
In order to reach this purpose, the present invention uses following technical scheme：
Kitchen fume concentration division methods, include the following steps：
It is poor to do frame to the kitchen fume image of collected serializing by step A1；
Step A2 writes grey level histogram function, finds out the grey level histogram of frame difference image；
Step A3, the pixel number in each section of statistic histogram；
Step A4, according to the high, normal, basic three grades of kitchen fume concentration of setting, in conjunction with the statistical result of different frame difference figure,
The kitchen fume at each moment is divided into corresponding concentration scale.
More preferably, the concentration scale quantity divided as needed in the step A3, can use 10 is siding-to-siding block length, and statistics is every
Pixel number in a gray scale interval.
More preferably, to divide greasy smell if statistic meets corresponding grade classification scheme in the step A4 dense accordingly
Spend grade.
More preferably, including following using the detection of the oil smoke concentration of kitchen fume concentration division methods and interference elimination method
Step：
Step B1 acquires the oil smoke image above hearth in real time；
Step B2, image processing unit carry out frame difference operation to collected front and back frame image, obtain the dynamic after frame difference
Area image；
Step B3, image processing unit carry out opening operation to the image after frame difference, remove image noise；
Step B4, using wavelet transformation, the edge of detection frame difference figure highlight regions is simultaneously marked, the region that will be marked
It is set as area-of-interest；
Step B5 identifies oil smoke using Image Smoothness and the method for gray value threshold value comprehensive descision come exclusive PCR
Moving region；
Step B6 carries out statistics of histogram to the oil smoke region identified, judges oil smoke concentration grade.
More preferably, it is camera oil smoke image to be acquired in the step B1, and the camera is mounted on kitchen ventilator ontology.
More preferably, image processing unit can be utilized according to the sequencing of the gray level image received in the step B2
A later frame image makes the difference with previous frame image.
More preferably, further comprising the steps of in the step B3：
Step C1 carries out etching operation to image, eliminates the noise in image and tiny spine, disconnect narrow company
It connects；
Step C2 carries out expansive working to the image corroded, restores the obvious characteristic on former frame difference image.
More preferably, the step B4 is further comprising the steps of：
Step D1 traverses frame difference image according to the filter of one 3*3 size of feature-set at edge with filter；
Step D2, calculate each position central pixel point in field in the gray value and filter of eight pixels it is corresponding
Value be multiplied and seek the edge detection value of pixel centered on summation；
Step D3, if the edge detection value differs larger with the pixel gray value more than half in field, by this
One pixel is determined as marginal point, and is marked；
Step D4, after device to be filtered has traversed image, the edge of highlight regions can be detected and be marked, as next
Walk the object of interest of processing.
More preferably, the step B5 exclusive PCRs region includes the following steps：
Step E1 finds out the segmentation threshold of oil smoke region and interference region, is set when the gray average of area-of-interest is more than
When fixed gray threshold, judgement area-of-interest is that may interfere with region；When the gray average of area-of-interest is less than setting
When gray threshold, judgement area-of-interest is possible oil smoke region；
Step E2 calculates the smoothness of each area-of-interest on the basis of area grayscale mean value, if some region of interest
When the variance in domain is greater than the set value, judgement area-of-interest is that may interfere with region；If the variance of some area-of-interest is less than
When setting value, judgement area-of-interest is possible oil smoke region；
Step E3, when step E1 and step E2 be all possible oil smoke region when, then judge area-of-interest for oil smoke region,
Other regions are all determined as interference region.
It is an object of the invention to propose kitchen fume concentration division methods and oil smoke concentration detection and interference elimination method,
This method is counted based on image grey level histogram, and the method provided through the invention can be accurate to picture to successive frame oil smoke image
The oil smoke concentration of vegetarian refreshments rank divides, and has many advantages, such as high accuracy and real-time continuous property.
Description of the drawings
Fig. 1 is the flow chart of the kitchen fume concentration division methods of one embodiment of the present of invention；
Fig. 2 is the flow chart of one embodiment of the present of invention；
Fig. 3 is the schematic diagram of the oil smoke region recognition of one embodiment of the present of invention.
Specific implementation mode
The technical solution further illustrated the present invention below in conjunction with the accompanying drawings and by specific embodiment mode.
As shown in Figure 1, kitchen fume concentration division methods, include the following steps：
It is poor to do frame to the kitchen fume image of collected serializing by step A1；
Step A2 writes grey level histogram function, finds out the grey level histogram of frame difference image；
Step A3, the pixel number in each section of statistic histogram；
Step A4, according to the high, normal, basic three grades of kitchen fume concentration of setting, in conjunction with the statistical result of different frame difference figure,
The kitchen fume at each moment is divided into corresponding concentration scale.
It is long for section to can use 10 for further description, the concentration scale quantity divided as needed in the step A3
Degree, counts the pixel number in each gray scale interval.
Further description divides greasy smell in the step A4 if statistic meets corresponding grade classification scheme
Corresponding concentration scale.
Further description, as shown in Fig. 2, oil smoke concentration detection and interference elimination method, include the following steps：
Step B1 acquires the oil smoke image above hearth in real time；
Step B2, image processing unit carry out frame difference operation to collected front and back frame image, obtain the dynamic after frame difference
Area image；
Step B3, image processing unit carry out opening operation to the image after frame difference, remove image noise；
Step B4, using wavelet transformation, the edge of detection frame difference figure highlight regions is simultaneously marked, the region that will be marked
It is set as area-of-interest；
Step B5 identifies oil smoke using Image Smoothness and the method for gray value threshold value comprehensive descision come exclusive PCR
Moving region；
Step B6 carries out statistics of histogram to the oil smoke region identified, judges oil smoke concentration grade.
Further description, acquisition oil smoke image is camera in the step B1, and the camera is mounted on kitchen ventilator
On ontology.Camera fields of view can cover entire hearth, and through the real-time gray level image of lens protection glass acquisition hearth oil smoke
And it is transmitted to image processing unit.
Further description, image processing unit can be suitable according to the priority of the gray level image received in the step B2
Sequence is made the difference using a later frame image with previous frame image.Since static region is constant, dynamic area in front and back two field pictures
(such as oil smoke drifts, and human hand is brandished) is variation, so black is presented in static region after frame difference, table after the frame difference of dynamic area
It is now the highlight regions of edge blurry, so the highlighted frame difference image in dynamic area can be obtained by frame difference.
Further description, it is further comprising the steps of in the step B3：
Step C1 carries out etching operation to image, eliminates the noise in image and tiny spine, disconnect narrow company
It connects；
Step C2 carries out expansive working to the image corroded, restores the obvious characteristic on former frame difference image.
The noise of frame difference image is removed using the method for opening operation, concrete operations are first to corrode reflation.First to image into
Row etching operation can eliminate the noise in image and tiny spine, disconnect narrow connection.Expansion is the antithesis behaviour of corrosion
Make, expansive working is carried out to the image corroded, restores the obvious characteristic on former frame difference image.Figure can be eliminated using opening operation
As noise, the separating objects at very thin point, smooth larger object boundary, while can ensure highlight regions in original image
Area is basically unchanged, and ensures that the accuracy of subsequent detection is unaffected.
Further description, the step B4 are further comprising the steps of：
Step D1 traverses frame difference image according to the filter of one 3*3 size of feature-set at edge with filter；
Step D2, calculate each position central pixel point in field in the gray value and filter of eight pixels it is corresponding
Value be multiplied and seek the edge detection value of pixel centered on summation；
Step D3, if the edge detection value differs larger with the pixel gray value more than half in field, by this
One pixel is determined as marginal point, and is marked；
Step D4, after device to be filtered has traversed image, the edge of highlight regions can be detected and be marked, as next
Walk the object of interest of processing.
Further description, the step B5 exclusive PCRs region include the following steps：
Step E1 finds out the segmentation threshold of oil smoke region and interference region, is set when the gray average of area-of-interest is more than
When fixed gray threshold, judgement area-of-interest is that may interfere with region；When the gray average of area-of-interest is less than setting
When gray threshold, judgement area-of-interest is possible oil smoke region；
Step E2 calculates the smoothness of each area-of-interest on the basis of area grayscale mean value, if some region of interest
When the variance in domain is greater than the set value, judgement area-of-interest is that may interfere with region；If the variance of some area-of-interest is less than
When setting value, judgement area-of-interest is possible oil smoke region；
Step E3, when step E1 and step E2 be all possible oil smoke region when, then judge area-of-interest for oil smoke region,
Other regions are all determined as interference region.
Because people is when cooking operation, hand can be brandished always, can include that oil smoke and human hand are grasped in the image after frame difference is complete
The interference region for the moving objects such as making, needs the influence in exclusive PCR region before carrying out oil smoke concentration identification, but oil smoke
The direction of motion have randomness, human hand, the direction of motion of slice are relatively unambiguous, to as shown in Figure 3：A, the image after frame difference
Upper oil smoke moving region is lower than the brightness of human hand, slice moving region, so the gray value mean value in corresponding oil smoke region is also low
Gray average in the moving region of human hand, slice；B, the grey value profile of oil smoke moving region relatively collects on the image after frame difference
In, and the gray value of the motion region boundary of human hand, slice is larger compared with the jump of the central area in region, so the image in the region
Not smooth enough, the variance of corresponding gray value is larger.Feature according to A, we are largely tested, and find out oil smoke area
The segmentation threshold in domain and interference region judges the region when the gray average of area-of-interest is more than the gray threshold of setting
To may interfere with region；When the gray average of area-of-interest is less than the gray threshold of setting, judge the region for possible oil
Cigarette district domain.Feature according to B calculates the smoothness of each area-of-interest on the basis of area grayscale mean value, the present invention
It is middle to be indicated with gray value variance, if the variance of some area-of-interest is greater than the set value, judge that the region is that may interfere with area
Domain；If the variance of some area-of-interest is less than setting value, judge the region for possible oil smoke region.Only judge when twice
When (gray average and variance) is all possible oil smoke region, then the region is judged for oil smoke region, other area-of-interests are all sentenced
It is set to interference region.Complete the exclusion of the identification and interference region in oil smoke region.
The technical principle of the present invention is described above in association with specific embodiment.These descriptions are intended merely to explain the present invention's
Principle, and it cannot be construed to limiting the scope of the invention in any way.Based on the explanation herein, the technology of this field
Personnel would not require any inventive effort the other specific implementation modes that can associate the present invention, these modes are fallen within
Within protection scope of the present invention.
Claims (9)
1. kitchen fume concentration division methods, which is characterized in that include the following steps：
It is poor to do frame to the kitchen fume image of collected serializing by step A1；
Step A2 writes grey level histogram function, finds out the grey level histogram of frame difference image；
Step A3, the pixel number in each section of statistic histogram；
Step A4, will be every in conjunction with the statistical result of different frame difference figure according to the high, normal, basic three grades of kitchen fume concentration of setting
The kitchen fume at one moment is divided into corresponding concentration scale.
2. kitchen fume concentration division methods according to claim 1, it is characterised in that：In the step A3 as needed
The concentration scale quantity of division, can use 10 is siding-to-siding block length, counts the pixel number in each gray scale interval.
3. kitchen fume concentration division methods according to claim 1, it is characterised in that：If statistic in the step A4
Meet corresponding grade classification scheme and then divides the corresponding concentration scale of greasy smell.
4. using the oil smoke concentration detection of kitchen fume concentration division methods and interference elimination side according to claim 1-3
Method, which is characterized in that include the following steps：
Step B1 acquires the oil smoke image above hearth in real time；
Step B2, image processing unit carry out frame difference operation to collected front and back frame image, obtain the dynamic area after frame difference
Image；
Step B3, image processing unit carry out opening operation to the image after frame difference, remove image noise；
Step B4, using wavelet transformation, the edge of detection frame difference figure highlight regions is simultaneously marked, and the region marked is set as
Area-of-interest；
Step B5 identifies that oil smoke moves using Image Smoothness and the method for gray value threshold value comprehensive descision come exclusive PCR
Region；
Step B6 carries out statistics of histogram to the oil smoke region identified, judges oil smoke concentration grade.
5. oil smoke concentration detection according to claim 4 and interference elimination method, it is characterised in that：It is adopted in the step B1
Oil smoke collection image is camera, and the camera is mounted on kitchen ventilator ontology.
6. oil smoke concentration detection according to claim 4 and interference elimination method, it is characterised in that：Scheme in the step B2
As processing unit can be made the difference using a later frame image with previous frame image according to the sequencing of the gray level image received.
7. oil smoke concentration detection according to claim 4 and interference elimination method, it is characterised in that：In the step B3 also
Include the following steps：
Step C1 carries out etching operation to image, eliminates the noise in image and tiny spine, disconnect narrow connection；
Step C2 carries out expansive working to the image corroded, restores the obvious characteristic on former frame difference image.
8. oil smoke concentration detection according to claim 4 and interference elimination method, it is characterised in that：The step B4 is also wrapped
Include following steps：
Step D1 traverses frame difference image according to the filter of one 3*3 size of feature-set at edge with filter；
Step D2 calculates the gray value value corresponding in filter of eight pixels in each position central pixel point and field
It is multiplied and asks the edge detection value of pixel centered on summation；
Step D3, if the edge detection value differs larger with the pixel gray value more than half in field, by this picture
Vegetarian refreshments is determined as marginal point, and is marked；
Step D4, after device to be filtered has traversed image, the edge of highlight regions can be detected and be marked, in next step
The object of interest of reason.
9. oil smoke concentration detection according to claim 4 and interference elimination method, it is characterised in that：The step B5 is excluded
Interference region includes the following steps：
Step E1 finds out the segmentation threshold of oil smoke region and interference region, when the gray average of area-of-interest is more than setting
When gray threshold, judgement area-of-interest is that may interfere with region；When the gray average of area-of-interest is less than the gray scale of setting
When threshold value, judgement area-of-interest is possible oil smoke region；
Step E2 calculates the smoothness of each area-of-interest on the basis of area grayscale mean value, if some area-of-interest
When variance is greater than the set value, judgement area-of-interest is that may interfere with region；If the variance of some area-of-interest is less than setting
When value, judgement area-of-interest is possible oil smoke region；
Step E3, when step E1 and step E2 be all possible oil smoke region when, then judge area-of-interest for oil smoke region, other
Region is all determined as interference region.
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