CN108760590B - Kitchen oil smoke concentration detection and interference elimination method based on image processing - Google Patents
Kitchen oil smoke concentration detection and interference elimination method based on image processing Download PDFInfo
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- 239000000779 smoke Substances 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000001514 detection method Methods 0.000 title claims abstract description 17
- 230000008030 elimination Effects 0.000 title claims abstract description 10
- 238000003379 elimination reaction Methods 0.000 title claims abstract description 10
- 238000010411 cooking Methods 0.000 claims abstract description 8
- 238000003708 edge detection Methods 0.000 claims description 6
- 230000007797 corrosion Effects 0.000 claims description 4
- 238000005260 corrosion Methods 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 4
- 239000003517 fume Substances 0.000 description 2
- 238000011202 physical detection method Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000003595 mist Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 239000004071 soot Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
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- G01N15/075—Investigating concentration of particle suspensions by optical means
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Abstract
A kitchen oil smoke concentration detection and interference elimination method based on image processing comprises the following steps: collecting oil smoke images above a cooking bench in real time; the image processing unit carries out frame difference operation on the collected front and rear frame images to obtain dynamic region images after frame difference; the image processing unit carries out opening operation on the image after frame difference to remove image noise; detecting the edge of a highlight area of the frame difference image and marking by using wavelet transform; eliminating interference by using a method for comprehensively judging image smoothness and gray value threshold values, and identifying an oil smoke movement area; and carrying out gray level histogram statistics on the identified oil smoke region, and judging the oil smoke concentration level. The invention aims to provide a kitchen oil smoke concentration detection and interference elimination method based on image processing.
Description
Technical Field
The invention relates to the technical field of oil smoke detection, in particular to a kitchen oil smoke concentration detection and interference elimination method based on image processing.
Background
At present, aiming at the detection of the concentration of the kitchen oil fume, an infrared projection method and a physical detection method are mainly adopted. One end of the infrared detection method transmits infrared light, the other end of the infrared detection method receives the infrared light, the concentration of the oil smoke is judged according to the intensity of the received infrared light, however, the oil smoke is uncertain when floating, and interference caused by blocking of hands is avoided, so that the oil smoke detection can be relatively accurate only by installing a plurality of infrared transmitters at different positions, the cost is high, and the requirement on installation positions is high. The physical detection method is similar to the principle of a smoke alarm, and the oil smoke concentration is judged by detecting the number of floating particles in the air, but the method has two defects that firstly, the detection can be carried out only when the oil smoke contacts the alarm, and the remote detection cannot be realized; secondly, when floating in the air, not oil smoke but water mist can not be detected.
Disclosure of Invention
The invention aims to solve the problems and provides a kitchen oil smoke concentration detection and interference elimination method based on image processing.
In order to achieve the purpose, the invention adopts the following technical scheme:
a kitchen oil smoke concentration detection and interference elimination method based on image processing comprises the following steps:
step A1, collecting oil smoke images above a cooking bench in real time;
step A2, the image processing unit performs frame difference operation on the collected front and back frame images to obtain dynamic region images after frame difference;
step A3, the image processing unit carries out opening operation on the frame difference image to remove image noise;
step A4, detecting the edge of the highlight area of the frame difference image by utilizing wavelet transformation, marking the highlight area, and setting the marked area as an interested area;
a5, eliminating interference by using a method of comprehensively judging image smoothness and gray value threshold values, and identifying an oil smoke movement area;
and step A6, carrying out gray histogram statistics on the identified oil smoke region, and judging the oil smoke concentration level.
Preferably, the step a1 includes collecting the oil smoke image by a camera, and the camera is mounted on the range hood body.
Preferably, in step a2, the image processing unit performs a difference between the next frame image and the previous frame image according to the sequence of the received grayscale images.
Preferably, the step a3 further includes the following steps:
b1, carrying out corrosion operation on the image, eliminating noise points and fine spines in the image, and disconnecting narrow connection;
and step B2, performing expansion operation on the corroded image, and recovering the obvious characteristics on the original frame difference image.
Preferably, the step a4 further includes the following steps:
step C1, setting a filter with the size of 3 x 3 according to the characteristics of the edge, and traversing the frame difference image by using the filter;
step C2, calculating the gray value of each position center pixel point and eight pixel points in the field to be multiplied by the corresponding value in the filter, and solving the sum to be used as the edge detection value of the center pixel point;
step C3, if the difference between the edge detection value and more than half of the gray value of the pixel point in the field is larger, the pixel point is determined as an edge point and marked;
and step C4, after the filter traverses the image, the edge of the highlight area is detected and marked as the interested object for the next processing.
Preferably, the step a5 of excluding the interference region includes the following steps:
step D1, finding out the segmentation threshold values of the oil smoke area and the interference area, and judging the area of interest as a possible interference area when the gray average value of the area of interest is greater than the set gray threshold value; when the gray average value of the region of interest is smaller than a set gray threshold value, judging the region of interest as a possible oil smoke region;
step D2, calculating the smoothness of each interested area on the basis of the area gray mean value, and if the variance of a certain interested area is larger than a set value, judging the interested area as a possible interference area; if the variance of a certain interested area is smaller than a set value, judging the interested area as a possible oil smoke area;
and D3, when the step D1 and the step D2 are both possible oil smoke areas, judging that the area of interest is an oil smoke area, and judging that other areas are interference areas.
The invention aims to provide a kitchen oil smoke concentration detection and interference elimination method based on image processing.
Drawings
FIG. 1 is a flow chart of one embodiment of the present invention;
fig. 2 is a schematic diagram of soot area identification according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further explained by the specific embodiment mode with reference to the attached drawings.
As shown in fig. 1, a method for detecting kitchen oil smoke concentration and eliminating interference based on image processing includes the following steps:
step A1, collecting oil smoke images above a cooking bench in real time;
step A2, the image processing unit performs frame difference operation on the collected front and back frame images to obtain dynamic region images after frame difference;
step A3, the image processing unit carries out opening operation on the frame difference image to remove image noise;
step A4, detecting the edge of the highlight area of the frame difference image by utilizing wavelet transformation, marking the highlight area, and setting the marked area as an interested area;
a5, eliminating interference by using a method of comprehensively judging image smoothness and gray value threshold values, and identifying an oil smoke movement area;
and step A6, carrying out gray histogram statistics on the identified oil smoke region, and judging the oil smoke concentration level.
Further, the step a1 is to collect the oil smoke image by a camera, and the camera is installed on the range hood body. The camera view can cover the whole cooking bench, and real-time gray images of cooking fume of the cooking bench are collected through the lens protective glass and transmitted to the image processing unit.
To be more specific, in step a2, the image processing unit uses the sequence of the received grayscale images to make a difference between the next frame image and the previous frame image. Because the static area in the two frames of images is not changed and the dynamic area (such as oil smoke drift, human hand waving, etc.) is changed, the static area appears black after the frame difference, and the dynamic area appears as a highlight area with fuzzy edges after the frame difference, so that the frame difference image with highlight in the dynamic area can be obtained through the frame difference.
To be further described, the step a3 further includes the following steps:
b1, carrying out corrosion operation on the image, eliminating noise points and fine spines in the image, and disconnecting narrow connection;
and step B2, performing expansion operation on the corroded image, and recovering the obvious characteristics on the original frame difference image.
The method for removing the noise of the frame difference image by utilizing the open operation method comprises the specific operation of firstly corroding and then expanding. The image is corroded first, so that noise and fine spikes in the image can be eliminated, and narrow connection is broken. And expansion is dual operation of corrosion, and expansion operation is carried out on the corroded image to recover obvious characteristics on the original frame difference image. The open operation can eliminate image noise points, separate objects at fine points, smoothen larger object boundaries, simultaneously ensure that the area of a highlight area in the original image is basically unchanged, and ensure that the accuracy of subsequent detection is not influenced.
To be further described, the step a4 further includes the following steps:
step C1, setting a filter with the size of 3 x 3 according to the characteristics of the edge, and traversing the frame difference image by using the filter;
step C2, calculating the gray value of each position center pixel point and eight pixel points in the field to be multiplied by the corresponding value in the filter, and solving the sum to be used as the edge detection value of the center pixel point;
step C3, if the difference between the edge detection value and more than half of the gray value of the pixel point in the field is larger, the pixel point is determined as an edge point and marked;
and step C4, after the filter traverses the image, the edge of the highlight area is detected and marked as the interested object for the next processing.
To be further described, the step a5 of excluding the interference region includes the following steps:
step D1, finding out the segmentation threshold values of the oil smoke area and the interference area, and judging the area of interest as a possible interference area when the gray average value of the area of interest is greater than the set gray threshold value; when the gray average value of the region of interest is smaller than a set gray threshold value, judging the region of interest as a possible oil smoke region;
step D2, calculating the smoothness of each interested area on the basis of the area gray mean value, and if the variance of a certain interested area is larger than a set value, judging the interested area as a possible interference area; if the variance of a certain interested area is smaller than a set value, judging the interested area as a possible oil smoke area;
and D3, when the step D1 and the step D2 are both possible oil smoke areas, judging that the area of interest is an oil smoke area, and judging that other areas are interference areas.
Because a person always waves his hand during a cooking operation, an image after frame difference contains interference areas of moving objects such as oil smoke, hand operation and the like, and the influence of the interference areas needs to be eliminated before oil smoke concentration identification, but the movement direction of the oil smoke is random, and the movement directions of the hand and the slice are relatively clear, so that as shown in fig. 2: A. the brightness of the oil smoke moving area on the image after the frame difference is lower than that of the moving areas of the human hand and the slice, so the mean value of the gray values of the corresponding oil smoke areas is also lower than that of the moving areas of the human hand and the slice; B. the gray values of the oil smoke movement area on the image after the frame difference are distributed more intensively, and the gray values of the boundaries of the movement areas of hands and a slice are larger than the jump of the central area of the area, so the image of the area is not smooth enough, and the variance of the corresponding gray values is larger. According to the characteristics A, a large number of experiments are carried out to find out the segmentation threshold values of the oil smoke area and the interference area, and when the gray average value of the area of interest is larger than the set gray threshold value, the area is judged to be the possible interference area; and when the gray average value of the region of interest is smaller than the set gray threshold value, judging the region as a possible oil smoke region. According to the characteristic B, on the basis of the area gray mean value, the smoothness of each interested area is calculated, the smoothness is expressed by gray value variance, and if the variance of a certain interested area is larger than a set value, the area is judged to be a possible interference area; and if the variance of a certain interested area is smaller than a set value, judging the area as a possible oil smoke area. And only when the two judgments (the mean value and the variance of the gray scale) are both possible oil smoke areas, the area is judged to be the oil smoke area, and other interested areas are all judged to be interference areas. And finishing the identification of the oil smoke area and the elimination of the interference area.
The invention utilizes a gray level histogram statistical method to demarcate the oil smoke concentration level. The gray histogram is a function of gray level distribution, and is a statistic of gray level distribution in an image. The gray histogram is to count the occurrence frequency of all pixels in the digital image according to the size of the gray value. According to the concentration grade quantity needing to be divided, 10 is taken as the interval length, the number of pixel points in each gray level interval is counted, and the oil smoke is divided into corresponding concentration grades when a set grade division scheme is achieved.
The technical principle of the present invention is described above in connection with specific embodiments. The description is made for the purpose of illustrating the principles of the invention and should not be construed in any way as limiting the scope of the invention. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive effort, which would fall within the scope of the present invention.
Claims (4)
1. A kitchen oil smoke concentration detection and interference elimination method based on image processing is characterized by comprising the following steps:
step A1, collecting oil smoke images above a cooking bench in real time;
step A2, the image processing unit performs frame difference operation on the collected front and back frame images to obtain dynamic region images after frame difference;
step A3, the image processing unit carries out opening operation on the frame difference image to remove image noise;
step A4, detecting the edge of the highlight area of the frame difference image by using wavelet transform, marking the highlight area, and setting the marked area as an area of interest, wherein the step A4 specifically comprises the following steps:
step C1, setting a filter with the size of 3 x 3 according to the characteristics of the edge, and traversing the frame difference image by using the filter;
step C2, calculating the gray value of each position center pixel point and eight pixel points in the field to be multiplied by the corresponding value in the filter, and solving the sum to be used as the edge detection value of the center pixel point;
step C3, if the difference between the edge detection value and more than half of the gray value of the pixel point in the field is larger, the pixel point is determined as an edge point and marked;
step C4, after the filter traverses the image, the edge of the highlight area is detected and marked as the interested object for the next processing;
a5, eliminating interference by using a method of comprehensively judging image smoothness and gray value threshold values, and identifying an oil smoke movement area; wherein, the step A5 specifically comprises the following steps:
step D1, finding out the segmentation threshold values of the oil smoke area and the interference area, and judging the area of interest as a possible interference area when the gray average value of the area of interest is greater than the set gray threshold value; when the gray average value of the region of interest is smaller than a set gray threshold value, judging the region of interest as a possible oil smoke region;
step D2, calculating the smoothness of each interested area on the basis of the area gray mean value, and if the variance of a certain interested area is larger than a set value, judging the interested area as a possible interference area; if the variance of a certain interested area is smaller than a set value, judging the interested area as a possible oil smoke area;
step D3, when the step D1 and the step D2 are both possible oil smoke areas, the area of interest is judged to be an oil smoke area, and other areas are judged to be interference areas;
and step A6, carrying out gray histogram statistics on the identified oil smoke region, and judging the oil smoke concentration level.
2. The method for detecting kitchen oil smoke concentration and eliminating interference based on image processing according to claim 1, characterized in that: and in the step A1, a camera is used for collecting the oil smoke image, and the camera is installed on the range hood body.
3. The method for detecting kitchen oil smoke concentration and eliminating interference based on image processing according to claim 1, characterized in that: in step a2, the image processing unit makes a difference between the next frame image and the previous frame image according to the sequence of the received grayscale images.
4. The method for detecting kitchen oil smoke concentration and eliminating interference based on image processing according to claim 1, characterized in that: the step a3 further includes the following steps:
b1, carrying out corrosion operation on the image, eliminating noise points and fine spines in the image, and disconnecting narrow connection;
and step B2, performing expansion operation on the corroded image, and recovering the obvious characteristics on the original frame difference image.
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| CN109210600B (en) * | 2018-11-30 | 2020-01-21 | 中山市浩帆电子电器有限公司 | Electric power on-off system of range hood |
| CN109669007B (en) * | 2018-12-29 | 2023-09-22 | 佛山市云米电器科技有限公司 | Household non-invasive on-line food detection equipment |
| CN109798565B (en) * | 2018-12-29 | 2023-06-13 | 佛山市云米电器科技有限公司 | Oil absorption system with function of identifying harmful substances in oil smoke |
| CN109827612B (en) * | 2018-12-29 | 2024-02-23 | 佛山市云米电器科技有限公司 | Replaceable polycyclic aromatic hydrocarbon detection device for smoke machine |
| CN109655585B (en) * | 2018-12-29 | 2021-08-31 | 佛山市云米电器科技有限公司 | Range hood capable of identifying kitchen air quality |
| CN109657640B (en) * | 2018-12-29 | 2024-07-23 | 佛山市云米电器科技有限公司 | Range hood capable of conducting health grade division according to food materials |
| CN116758489B (en) * | 2023-08-17 | 2023-10-27 | 山东传奇新力科技有限公司 | Intelligent kitchen lampblack detection and identification method based on image processing |
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