CN109237582A - Range hood control method and system based on image recognition and range hood - Google Patents
Range hood control method and system based on image recognition and range hood Download PDFInfo
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- CN109237582A CN109237582A CN201811358631.5A CN201811358631A CN109237582A CN 109237582 A CN109237582 A CN 109237582A CN 201811358631 A CN201811358631 A CN 201811358631A CN 109237582 A CN109237582 A CN 109237582A
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- 238000000034 method Methods 0.000 title claims abstract description 55
- 239000000779 smoke Substances 0.000 claims abstract description 82
- 238000010411 cooking Methods 0.000 claims abstract description 55
- 230000008569 process Effects 0.000 claims abstract description 24
- 238000013527 convolutional neural network Methods 0.000 claims description 14
- 238000012549 training Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000001276 controlling effect Effects 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 230000000391 smoking effect Effects 0.000 abstract description 5
- 230000007613 environmental effect Effects 0.000 abstract 1
- 238000005070 sampling Methods 0.000 description 4
- 238000013507 mapping Methods 0.000 description 3
- 238000011176 pooling Methods 0.000 description 3
- 230000004913 activation Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004134 energy conservation Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 208000003443 Unconsciousness Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 235000019504 cigarettes Nutrition 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000008034 disappearance Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- 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
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- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
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Abstract
The invention discloses a range hood control method, a control system and a range hood based on image recognition, wherein the method comprises the following steps: acquiring a cooking environment image generated in the cooking process in real time; judging the oil smoke condition in the cooking environment according to the cooking environment image; determining a control instruction of the range hood according to the oil smoke condition; and controlling the working state of the range hood according to the control command. According to the invention, the cooking environment image generated in the cooking process is acquired in real time; judging the oil smoke condition in the cooking environment according to the cooking environment image; according to the operating condition of range hood of oil smoke state control, realize range hood along with the automatic intelligent control of oil smoke situation, range hood smoking effect is better, not only makes range hood environmental protection and energy saving more, also reduces the user and cooks the redundant action of the adjustment range hood gear of process, promotes user's use and experiences.
Description
Technical field
The present invention relates to home wiring control field, be specifically related to a kind of range hood control method based on image recognition,
Control system, range hood.
Background technique
Kitchen ventilator is in order to avoid people are during the cooking process by the injury of oil smoke.The wind of major part kitchen ventilator now
Power size is realized by gear, selects gear (fast, slow or fast, in, slow) to adjust lampblack-pumped effect by user
Fruit.In actual application, due to the fining degree of gear not enough or user due to it is unconscious go adjustment gear, cause
Existing wind-force and the oil smoke of generation mismatch, and user experience is poor.
With the raising of people's levels of substance, intelligent range hood slowly enters the people visual field.Existing kitchen ventilator automatically controls
The method of wind-force mainly has two methods of detection oil smoke situation and infrared detection temperature, and oil smoke shape is detected by smoke detector
The method of condition has certain delay, also needs the regular hour because generating since oil smoke into smoke detector.In addition,
Cookware temperature and oil smoke are not absolute linear relationships, and infrared mode is also not ideal enough, and very maximum probability will cause erroneous judgement, oil pumping
The smoking effect of smoke machine is bad, and then has seriously affected the health and usage experience of user.Above two method all cannot
Accomplish complete closed-loop control, practical value is limited, how user to be made easily to control kitchen ventilator, allow kitchen ventilator more intelligence at
For smart home field urgent problem.
Summary of the invention
It is an object of the invention to overcome the control of kitchen ventilator in the prior art not ideal enough, the bad technology of smoking effect
Problem provides a kind of range hood control method based on image recognition, control system, range hood.
To achieve the above object, The technical solution adopted by the invention is as follows: a kind of range hood control based on image recognition
Method processed, which comprises
The cooking environments image generated in cooking process is obtained in real time；
The oil smoke situation under cooking environments is judged according to the cooking environments image；
The control instruction of range hood is determined according to the oil smoke situation；
The working condition of range hood is controlled according to the control instruction.
Further, convolutional neural networks model is established, by trained convolutional neural networks model to the culinary art
Ambient image carries out identifying processing.
Further, by extracting the characteristic image of cooking environments image to cooking environments image progress process of convolution,
Classify after carrying out batch regularization, mapping, pondization processing to characteristic image again to pixel each in characteristic image, is divided into oil
Cigarette pixel and non-oil smoke pixel.
Further, oil smoke situation is obtained after carrying out statistics calculating to oil smoke pixel and non-oil smoke pixel.
Further, the control instruction includes power-on instruction, when oil smoke situation reaches preset threshold, controls fume-exhausting
Machine is opened.
Further, the control instruction includes air quantity regulating command, and the air quantity of range hood is adjusted according to oil smoke situation.
A kind of range hood control system based on image recognition, including
Module is obtained, for obtaining the cooking environments image generated in cooking process；
Judgment module, for judging oil smoke situation according to the cooking environments image；
Analysis module, for determining the control instruction of range hood according to the oil smoke situation；
Control module, for controlling the working condition of range hood according to the control instruction.
It further, further include study module, the study module is used for after cooking environments image study training
Carry out identifying processing.
Further, the study module includes convolutional neural networks model.
A kind of range hood further includes Image Acquisition including the above-mentioned range hood control system based on image recognition
Device, described image acquisition device is connect with the range hood control system signal based on image recognition, for acquiring fume-exhausting
Cooking environments image near machine is simultaneously sent to acquisition module.
Further, described image acquisition device includes the camera being set on kitchen ventilator.
By the above-mentioned description of this invention it is found that compared with prior art, one kind provided by the invention is based on image recognition
Range hood control method, control system, range hood, obtain the cooking environments image that generates in cooking process in real time；Root
The oil smoke situation under cooking environments is judged according to the cooking environments image；The control of range hood is determined according to the oil smoke situation
Instruction；The working condition of range hood is controlled according to the control instruction, realizes range hood with the automated intelligent of oil smoke situation
Control, range hood smoking effect is more preferable, not only makes the more environmentally-friendly energy conservation of range hood, also reduces user in cooking process
The redundant actions for adjusting range hood gear, promote the usage experience of user.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is that the present invention is based on the range hood control flow charts of image recognition；
Fig. 2 is convolutional neural networks model structure of the present invention；
Fig. 3 is convolutional neural networks model based coding procedure chart of the present invention；
Fig. 4 is that convolutional neural networks model of the present invention decodes procedure chart；
Fig. 5 is the flow chart that the present invention controls range hood working condition according to oil smoke state；
Fig. 6 is that the present invention is based on the range hood control block diagrams of image recognition；
Fig. 7 is extractor hood structure block diagram of the present invention.
Specific embodiment
The technical solution in the present invention is clearly and completely retouched below with reference to the attached drawing in the embodiment of the present invention
It states, it is clear that the described embodiments are merely a part of the embodiments of the present invention, instead of all the embodiments.Based on the present invention
In embodiment, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, should fall within the scope of the present invention.
It should be noted that term " includes " of the invention and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to
Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product
Or other step or units that equipment is intrinsic.
As shown in Figure 1, a kind of range hood control method based on image recognition, the described method comprises the following steps:
S1: the cooking environments image generated in cooking process is obtained in real time；
S2: the oil smoke situation under cooking environments is judged according to the cooking environments image；
S3: the control instruction of range hood is determined according to the oil smoke situation；
S4: the working condition of range hood is controlled according to the control instruction.
In the present embodiment, it using image collecting device such as camera captured in real-time or timed shooting, obtains user and is cooking
The cooking environments image generated during preparing food, such as the cooking environments image in acquisition kitchen, compartment, can be according to acquisition environment
The camera of different number is arranged in size, such as a camera is arranged in common household kitchen,
After the cooking environments image got, cooking environments image is identified, the oil smoke in image is identified, in oil smoke
Identification during this, the present embodiment is using convolutional neural networks model, as shown in Fig. 2, convolutional neural networks model
By input layer (Input images), convolutional encoding network (code module), deconvolution decoding network (decode
Module), output layer (output images) forms.Wherein, Input images is used to the image of acquisition being input to model
In, code module is the full convolutional neural networks of a multilayer, is all connected to down-sampling layer after every layer of convolutional layer, the convolutional encoding
Network structure is similar to the preceding 16 layers of convolutional Neural layer of VGG-19 network structure designed for object classification, while abandoning
The full articulamentum of VGG-19 is conducive to export high-resolution characteristic pattern in the encoder of bottommost layer in this way, and reduces net
The parameter of network, to reduce the training time of network.Corresponding deconvolution decoding network decode module also has 16
Convolutional layer, so whole network structure has reached 32 layers, output images is made of a classifier (Softmax), is used
The pixel of corresponding position is classified as each classification, calculates the probability for belonging to which classification, the identification for oil smoke
For, input image pixels are mainly divided into two classes: oil smoke pixel, non-oil smoke pixel,
For cataloged procedure as shown in figure 3, in convolutional encoding network, convolution operation each time is all by big with 3x3
The convolutional layer (Conv) of small convolution kernel carries out feature extraction to the output image of up-sampling layer (Upsamleing), then to institute
The feature of extraction carries out batch regularization operation (Batch Normalization, abbreviation BN) in batch regularization layer, and utilizes activation
Function (ReLu) carries out Nonlinear Mapping to feature, and the Dropout layers of node made in network is recycled to close with certain probability
It closes, finally carries out pondization operation (Pooling) in pond layer, maximum pond (Max-Pooling), Chi Huahou are used in this implementation
The length and width of each feature become original half, and the maximum available image of pondization is flat in the variation of small space displacement
Motion immovability, and multiple maximum pondizations can obtain the feature of more robust property for classifier (Softmax), it is continuous to carry out
Pond down-sampling will cause picture and constantly be distorted, and boundary information is lost, and be unfavorable for the segmentation task of image, so corresponding
It is subsequent setting decoding network, in order to restore image information as far as possible, it is also necessary to be recorded during pond maximum special
The index position of value indicative.Decoded process is as shown in figure 4, each decoder uses the aspect indexing of pond process record to input
Feature is up-sampled, reuse with can training convolutional core convolutional layer (Conv) to up-sampling layer (Upsamleing) output
Sparse features figure carry out convolution operation and obtain dense characteristic pattern, the process is similar with cataloged procedure, and then carries out batch just
Then change operation (Batch Normalization, abbreviation BN), activation primitive ReLu Nonlinear Mapping, pondization operation
(Pooling), output layer (output images) is that each pixel is classified using classifier (SoftMax), output
It is the corresponding class probability of each pixel, the classification of the maximum probability of each pixel is exactly the classification predicted, in the network model
In structure, in order to overcome deep neural network to be difficult to trained disadvantage and accelerate the training process of network, after each convolutional layer
Addition batch regularization layer, it is therefore prevented that the gradient disappearance problem that depth network is easy to appear in the training process helps to improve instruction
Experienced convergence rate and model accuracy, while network is designed using Dropout layers to prevent model from the phenomenon that over-fitting occur
After structure, need to be trained it to obtain prediction model.Prepare data set for training process, acquires some image making numbers
According to collection, data set claps the image for taking camera to install under visual angle, includes oil smoke and not no oil smoke, and carry out hand to data set
Work mark, is divided into 2 kinds of semantic classes: oil smoke, non-oil smoke for each pixel, reads data in order to facilitate computer, marks respectively
It is denoted as 0 (non-oil smoke) and 1 (oil smoke).Can be obtained by the parted pattern of oil smoke by training sample, can by input picture into
Row segmentation, its pixel is classified, oil smoke pixel, non-oil smoke pixel are divided into,
All pixels are carried out oil smoke situation is calculated, such as the number of pixels of oil smoke is m, non-oil smoke
Number of pixels is n, then oil smoke size P=m/ (m+n), and the value range of P is [0,1],
As shown in figure 5, for range hood set a booting threshold value k, as P<k, range hood remains turned off state, work as P>
When k, i.e., when oil smoke reaches certain oil smoke concentration, power-on instruction is generated, control range hood is opened, and at this moment range hood starts
Work can also be adjusted, according to the size of P value after range hood work according to wind-force of the oil smoke concentration to range hood
And air quantity regulating command, such as the revolving speed by controlling motor are generated according to oil smoke concentration, reach the control to range hood wind-force
System, and then the air quantity of range hood is adjusted, the both hands of user can be liberated during user cooks in this way, improve user
Usage experience.
The present embodiment additionally provides a kind of range hood control system based on image recognition, as shown in fig. 6, including obtaining
Module 1, judgment module 2, analysis module 3, control module 4, study module 5,
The module 1 that obtains obtains the cooking environments image generated in cooking process, and the judgment module 2 is cooked according to described
Ambient image of preparing food judges oil smoke situation；The analysis module 3 determines the control instruction of range hood according to the oil smoke situation；Institute
State the working condition that control module 4 controls range hood according to the control instruction；The study module 5 is to the cooking environments
Identifying processing is carried out after image study training, which is convolutional neural networks model, specifically may refer to above-mentioned be based on
Cooking environments image is divided into oil smoke by convolutional neural networks model by the description in the range hood control method of image recognition
It after non-oil smoke, is calculated, judges oil smoke state by calculating, the working condition of range hood is controlled according to oil smoke state,
For example, be that range hood sets a booting threshold value according to oil smoke concentration, when oil smoke concentration is less than booting threshold value, range hood
State is remained turned off, when oil smoke concentration is greater than booting threshold value, control range hood is opened, and at this moment range hood is started to work,
It after range hood work, can also be adjusted according to wind-force of the oil smoke concentration to range hood, such as pass through control motor
Revolving speed reaches the control to range hood wind-force, and then adjusts the air quantity of range hood.
The present embodiment additionally provides a kind of range hood, as shown in fig. 7, comprises the above-mentioned fume-exhausting based on image recognition
Machine control system 100 further includes image collecting device 200, described image acquisition device 200 and the fume-exhausting based on image recognition
The connection of 100 signal of machine control system obtains module 1, institute for acquiring the cooking environments image near range hood and being sent to
Stating image collecting device includes the camera being set on kitchen ventilator, which can be set on kitchen ventilator, for example, setting
It at the suction opening of range hood, alternatively, the marginal position of range hood is arranged in, or can be set on hearth, with suction
The connection of kitchen ventilator signal, camera use the above-mentioned range hood control method based on image recognition to oil suction after taking image
Smoke machine is controlled.
By the above-mentioned description of this invention it is found that compared with prior art, one kind provided by the invention is based on image recognition
Range hood control method, control system, range hood, obtain the cooking environments image that generates in cooking process in real time, adopt
It is identified after carrying out learning training with convolutional neural networks model, the oil smoke under cooking environments is judged according to the cooking environments image
Situation；The control instruction of range hood is determined according to the oil smoke situation；The work of range hood is controlled according to the control instruction
Make state, range hood can be enabled according to the automatic start and stop of oil smoke situation, according to the adjustment wind-force of oil smoke concentration intelligence
Size, realize range hood with oil smoke situation automated intelligent control, range hood smoking effect is more preferable, not only makes oil suction
The more environmentally-friendly energy conservation of smoke machine also reduces user in the redundant actions of the adjustment range hood gear of cooking process, promotes user's
Usage experience.
It above are only several specific embodiments of the invention, but the design concept of the present invention is not limited to this, all benefits
It is made a non-material change to the present invention, should all be belonged to behavior that violates the scope of protection of the present invention with this design.
Claims (11)
1. a kind of range hood control method based on image recognition, which is characterized in that the described method includes:
The cooking environments image generated in cooking process is obtained in real time；
The oil smoke situation under cooking environments is judged according to the cooking environments image；
The control instruction of range hood is determined according to the oil smoke situation；
The working condition of range hood is controlled according to the control instruction.
2. the range hood control method according to claim 1 based on image recognition, it is characterised in that: establish convolution mind
Through network model, identifying processing is carried out to the cooking environments image by trained convolutional neural networks model.
3. the range hood control method according to claim 2 based on image recognition, it is characterised in that: by culinary art
Ambient image carries out process of convolution to extract the characteristic image of cooking environments image, then carries out batch regularization to characteristic image, reflects
It penetrates, classify after pondization processing to pixel each in characteristic image, be divided into oil smoke pixel and non-oil smoke pixel.
4. the range hood control method according to claim 3 based on image recognition, it is characterised in that: to oil smoke pixel
Point and non-oil smoke pixel obtain oil smoke situation after carrying out statistics calculating.
5. the range hood control method according to claim 1 or 4 based on image recognition, it is characterised in that: the control
System instruction includes power-on instruction, and when oil smoke situation reaches preset threshold, control range hood is opened.
6. the range hood control method according to claim 1 or 4 based on image recognition, it is characterised in that: the control
System instruction includes air quantity regulating command, and the air quantity of range hood is adjusted according to oil smoke situation.
7. a kind of range hood control system based on image recognition, it is characterised in that: including
Module is obtained, for obtaining the cooking environments image generated in cooking process；
Judgment module, for judging oil smoke situation according to the cooking environments image；
Analysis module, for determining the control instruction of range hood according to the oil smoke situation；
Control module, for controlling the working condition of range hood according to the control instruction.
8. the range hood control system according to claim 7 based on image recognition, it is characterised in that: further include study
Module, the study module are used to carry out identifying processing to after cooking environments image study training.
9. the range hood control system according to claim 8 based on image recognition, it is characterised in that: the study mould
Block includes convolutional neural networks model.
10. a kind of range hood, it is characterised in that: including the suction based on image recognition described in claim 7-9 any one
Oil smoke machine control system further includes image collecting device, described image acquisition device and the range hood control based on image recognition
System signal connection processed, for acquiring the cooking environments image near range hood and being sent to acquisition module.
11. range hood according to claim 10, it is characterised in that: described image acquisition device includes being set to oil smoke
Camera on machine.
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Cited By (13)
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| CN109961070A (en) * | 2019-03-22 | 2019-07-02 | 国网河北省电力有限公司电力科学研究院 | The method of mist body concentration is distinguished in a kind of power transmission line intelligent image monitoring |
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| CN109961070A (en) * | 2019-03-22 | 2019-07-02 | 国网河北省电力有限公司电力科学研究院 | The method of mist body concentration is distinguished in a kind of power transmission line intelligent image monitoring |
| CN110296448A (en) * | 2019-06-26 | 2019-10-01 | 杭州九阳小家电有限公司 | A kind of smog recognition effect modification method, system and the kitchen ventilator of kitchen ventilator |
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| CN113124432A (en) * | 2019-12-31 | 2021-07-16 | 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 |
| 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 |
| CN113124432B (en) * | 2019-12-31 | 2023-05-30 | 青岛海尔智慧厨房电器有限公司 | Control method and device for range hood, range hood and storage medium |
| CN113124433A (en) * | 2019-12-31 | 2021-07-16 | 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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Application publication date: 20190118 |