CN109137392B - Clothes washing method and device and clothes treatment device - Google Patents
Clothes washing method and device and clothes treatment device Download PDFInfo
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- CN109137392B CN109137392B CN201811261287.8A CN201811261287A CN109137392B CN 109137392 B CN109137392 B CN 109137392B CN 201811261287 A CN201811261287 A CN 201811261287A CN 109137392 B CN109137392 B CN 109137392B
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- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
- D06F35/00—Washing machines, apparatus, or methods not otherwise provided for
- D06F35/005—Methods for washing, rinsing or spin-drying
- D06F35/006—Methods for washing, rinsing or spin-drying for washing or rinsing only
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- D06F2202/12—
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Abstract
The invention provides a clothes washing method, a clothes washing device and a clothes treatment device, wherein the method comprises the following steps: the method comprises the steps of obtaining clothes images of clothes to be washed, inputting the clothes images into each classification model trained in advance to determine whether the clothes images have characteristics of target type clothes learned by each classification model, modifying a washing mode set by a user if the clothes images have the characteristics learned by at least one classification model, and washing the clothes to be washed by using the modified washing mode.
Description
Technical Field
The invention relates to the technical field of household appliances, in particular to a clothes washing method, a clothes washing device and a clothes treatment device.
Background
In the era of artificial intelligence, it has become a trend that a laundry processing device (e.g., a washing machine) automatically detects the type of laundry put in by a user and performs a washing mode adapted accordingly.
At present, most of clothes type detection methods are target detection methods based on deep learning, models capable of detecting different clothes types are obtained by marking and training a large number of clothes samples, the marking information is coarse in granularity, and the method only identifies the whole image of each type of clothes, so that the technical problem of inaccurate identification results is caused.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the invention provides a clothes washing method, which identifies the specific features in the clothes image through each pre-trained classification model which learns the features of the target type clothes, has finer identification granularity, improves the controllability and accuracy of target clothes identification, further improves the accuracy of washing mode determination, and solves the technical problem that the identification result is inaccurate because only the image of each type of clothes is integrally identified in the prior art.
The invention provides a clothes washing device.
The invention provides a clothes treatment device.
The invention provides a computer readable storage medium.
An embodiment of one aspect of the present invention provides a clothes washing method, including:
acquiring a clothes image of clothes to be washed;
inputting the clothes images into each classification model trained in advance to determine whether the clothes images have the characteristics of target type clothes learned by each classification model;
if the clothes image has at least one characteristic learned by the classification model, modifying the washing mode set by the user, and washing the clothes to be washed by using the modified washing mode.
In accordance with another aspect of the present invention, there is provided a laundry washing device, comprising:
the acquisition module is used for acquiring a clothes image of the clothes to be washed;
the recognition module is used for inputting the clothes image into each classification model trained in advance so as to determine whether the clothes image has the characteristics of the target type clothes learned by each classification model;
and the washing module is used for modifying the washing mode set by the user if the clothes image has at least one characteristic learned by the classification model and washing the clothes to be washed by using the modified washing mode.
An embodiment of another aspect of the present invention provides a clothes treating apparatus, including: a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the program, implementing a laundry washing method as described in the previous aspect.
Yet another embodiment of the present invention proposes a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements a laundry washing method as described in the previous aspect.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the method comprises the steps of obtaining clothes images of clothes to be washed, inputting the clothes images into each classification model trained in advance to determine whether the clothes images have characteristics of target type clothes learned by each classification model, modifying a washing mode set by a user if the clothes images have the characteristics learned by at least one classification model, and washing the clothes to be washed by using the modified washing mode.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a clothes washing method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a classification model training method according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating classification model training according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of another laundry washing method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a clothes image identified by a classification model according to an embodiment of the present invention; and
fig. 6 is a schematic structural diagram of a laundry washing device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Hereinafter, a laundry washing method, apparatus and laundry treating apparatus according to embodiments of the present invention will be described with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a clothes washing method according to an embodiment of the present invention.
As shown in fig. 1, the method comprises the steps of:
The execution subject of the embodiment of the invention is a clothes treatment device, and the clothes treatment device can be a washing machine, a washing and drying integrated machine and the like.
In the embodiment of the invention, because the clothes to be washed are easy to wind in the washing barrel, the winding of the clothes to be washed in the rolling process can be reduced by controlling the movement of the washing barrel of the clothes processing device, at least one frame of image is collected by the imaging device so as to improve the accuracy of the characteristics contained in the obtained image of the clothes to be washed, for example, the image only contains one type of clothes by comparing the collected at least one frame of image, thereby improving the accuracy of characteristic identification.
Specifically, the acquired clothes image is divided into a plurality of regions, each of the plurality of regions obtained by division is respectively input into each classification model trained in advance to determine whether each region has at least one feature learned by the classification model, further, whether each region has at least one feature learned by the classification model is determined, and when at least one region in the acquired clothes image has at least one feature learned by the classification model, the clothes image is determined to have the features of the target type clothes.
It should be understood that the target type of clothes is to identify the determined clothes type, for example, in the embodiment, if the target type of clothes is jeans type clothes, it is identified whether the clothes image of the clothes to be washed has features of jeans type clothes; if the target type of laundry is a woolen type of laundry, it is identified whether the image of the laundry to be washed has the characteristics of the woolen type of laundry, and the target type of laundry may be other types of laundry, which is not limited in this embodiment.
It should be noted that the training method of the classification model will be specifically described in the following embodiment.
And 103, if the clothes image has at least one characteristic learned by the classification model, modifying the washing mode set by the user, and washing the clothes to be washed by using the modified washing mode.
Specifically, if the target type of clothes in the clothes to be washed is determined after the characteristics learned by at least one classification model in the acquired clothes image are determined through each classification model, the washing mode set by the user is modified, and the clothes to be washed are washed by using the modified washing mode, so that the modified washing mode is more suitable for washing the target type of clothes.
In the clothes washing method of the embodiment, the clothes image of the clothes to be washed is obtained, the clothes image is input into each classification model trained in advance, to determine whether the clothes image has the characteristics of the target type clothes learned by each classification model, if the clothes image has at least one characteristic learned by the classification model, the washing mode set by the user is modified, and the clothes to be washed are washed by using the modified washing mode, so that a plurality of classification models which are trained in advance and have learned the characteristics of the target type clothes are realized, the feature recognition is carried out on the same clothes image, because each classification model correspondingly recognizes the corresponding detail feature of the same clothes type, according to the result of the identification of each classification model, whether the corresponding clothes category exists is determined, so that the accuracy of the identification result is improved, and the accuracy of the washing mode determination is further improved.
In the above embodiment, it is described that the pre-trained classification models can be used to perform the feature recognition on the clothes, and for this reason, the embodiment proposes a training method of the classification models, and further describes a method for training the classification models before performing the feature recognition on the clothes by using the classification models.
Fig. 2 is a schematic flow chart of a classification model training method according to an embodiment of the present invention.
As shown in fig. 2, the method may include the steps of:
The number of the clothing image sets to be acquired may be determined according to the characteristics of the target type clothing to be identified, for example, the number of the clothing image sets is 5, and this is not limited in this embodiment.
The jeans-type clothes feature obvious features, and people can identify the jeans-type clothes according to the features, so the target type clothes in the embodiment are described by taking the jeans-type clothes as an example, the components of the jeans-type clothes comprise at least one or more combinations of fabrics, stitches, buttons, button holes and trousers waists, and each component of the clothes can correspond to one clothes image set.
Specifically, according to the classification model to be trained, the corresponding clothes sample image sets are respectively adopted for training.
Fig. 3 is a schematic diagram of classification model training provided in an embodiment of the present invention, and as shown in fig. 3, a sample image set of jeans-type clothing is collected, where components of the jeans-type clothing include fabric, stitches, buttons, grommets, and a waist of trousers, and 5 features are corresponded to the 5 features, a batch of sample images are respectively collected for the 5 features, and a sample image set corresponding to each feature is used for training a classification model. By adopting the same method, training can be completed on classification models corresponding to other characteristics of the jean-type clothes, the principle is the same, and the details are not repeated here.
Further, in the training process, feature recognition may be performed on a single image by using a trained classification model, as shown in fig. 3, the single image is divided into a plurality of regions, for example, 24 regions shown in fig. 3, each region of each image is input into 5 classification models respectively for feature recognition, the image is marked as Y if it is a feature of the jeans-type clothing, and when feature recognition is performed on one image, it is recognized that the image includes a feature learned by the classification model with the number of 1, which is marked as Y1 in fig. 3. Furthermore, analysis and adjustment are performed according to the recognition result, and since the characteristics of the classification model recognition are known, the method for training the classification model is adjusted according to the recognition result, so that the recognition accuracy of the classification model obtained by training is improved.
In this embodiment, a method for training a classification model by taking a target type garment as a jeans type garment as an example is described, optionally, the target type garment may also be a woolen type garment, and the target type garment includes: after the target type clothes are changed and the model is trained, the component corresponding to the training sample set to be obtained needs to correspond to the component of the woolen type clothes, and the training method for the classification model is the same as the training method for the jeans type clothes described above, and is not repeated here.
In addition, the target type clothes can also be silk type clothes and the like, the training method for each classification model is also the same as the training method for the classification model of jeans type clothes, the principle is the same, the description is not repeated, and the target type of the clothes is not limited in the embodiment.
In the classification model training method of the embodiment, a large number of image samples are obtained to form an image sample set corresponding to each feature, the image sample set is used for training the corresponding classification model, the training of the classification model is realized, the classification model finished through the training can be used for identifying clothes to be washed, and the accuracy is better.
Based on the above embodiments, the embodiment of the present invention provides a possible implementation manner of another laundry washing method, and fig. 4 is a schematic flow chart of another laundry washing method provided by the embodiment of the present invention, as shown in fig. 4, the method includes the following steps:
Specifically, reference may be made to step 101 in the corresponding embodiment of fig. 1, which is not described herein again.
Specifically, each classification model trained in advance is used for identifying one specific feature in the clothes image, and feature identification is performed on the clothes image to be washed according to each classification model so as to determine whether the clothes image has the features of the target type clothes learned by at least one classification model, so that the identification granularity is finer, and the identification accuracy is higher.
In a scene, the target type clothes are jeans type clothes, namely whether the clothes to be washed have the characteristics of the jeans type clothes or not needs to be identified, 5 classification models are determined according to the characteristics of the jeans type clothes, the characteristics of the fabric, the sewing lines, the buttons, the button holes and the trouser waist of the 5 jeans type clothes are learned respectively, and for the convenience of distinguishing, the 5 classification models are numbered according to numbers 1-5. However, the numbering order is not limited in this embodiment.
In another scenario, the target type clothes are woolen type clothes, that is, whether the clothes to be washed have characteristics of the woolen type clothes is required to be identified, 3 classification models are determined according to the characteristics of the woolen type clothes, the clothes characteristics of the thick line texture fabric, the thin line texture fabric and the fluff-containing fabric are learned respectively, for convenience of distinguishing, the 3 classification models are numbered according to numbers 1-3, and the numbering sequence is not limited.
It should be noted that, when the target type of clothes is other types of clothes, the components of the characteristics of the type of clothes can be determined according to the clothes type of the target type of clothes, and the type of the target type of clothes is not limited in this embodiment.
Fig. 5 is a schematic diagram of a clothing image identified by a classification model according to an embodiment of the present invention, as shown in fig. 5, an acquired clothing image is divided into 24 regions, the 24 regions are respectively input into 5 classification models to determine whether each region has at least one feature learned by the classification model, if one of the features learned by the classification model exists, the region is marked as Y and a feature number, if the region does not have the feature learned by each classification model, the region is marked as Y1, if the image does not have the feature learned by each classification model, the region is marked as Y2, and if the image also has the feature learned by the classification model 2, the region is marked as Y3. And finally, performing statistical judgment on the whole image, if one area is marked as Y or more, determining that the acquired image contains the characteristics of the target type clothes, and if 24 areas are marked as N, determining that the acquired image does not contain the characteristics of the target type clothes.
For example, when the image of the laundry is identified by each classification model, wherein an area is marked as Y2, that is, the image of the laundry identifies features of stitches of jeans-type laundry, the image of the laundry is considered to have features of jeans-type laundry.
In step 403, the clothes are washed in the washing mode set by the user.
Specifically, if the clothes image does not have the characteristics of the target type of clothes learned by each classification model, the clothes is washed by adopting a washing mode set by a user.
And step 404, sending out a prompt for taking out the target type of clothes, starting countdown, and recognizing that the target type of clothes is in the washing tub or detecting no door opening operation when the countdown is finished.
In practical applications, when a user washes the laundry, there may be more than one laundry placed in the laundry processing apparatus, which may include a target type of laundry, and as a possible implementation manner, if it is determined that the laundry image has at least one feature learned by the classification model, it indicates that the laundry to be washed includes the target type of laundry, and before the laundry to be washed is washed by using the modified washing mode, a prompt for taking out the target type of laundry may be issued.
In one scenario, if the target type clothing is jeans type clothing, the jeans type clothing is easy to fade, and other clothing is dyed, so that when the characteristics of the jeans type clothing are detected in the image, the user is reminded to take out the clothing for washing independently.
In another scenario, if the target type clothes is wool type clothes, and the wool type clothes is easy to shrink and pilling in machine washing, when the characteristics of the wool type clothes in the image are detected, the user is reminded to take out the clothes for washing separately.
Further, after the prompt for taking out the target type of laundry is issued, a countdown is started, for example, the countdown duration is 1 minute, and when the countdown is finished, it is recognized that the target type of laundry is still in the washing tub or the door opening operation is not detected, and the washing mode set by the user is modified according to the recognition or detection result.
Step 405, modifying the washing mode set by the user, and washing the laundry using the modified washing mode.
Specifically, if it is recognized that the target type of laundry is still in the washing tub or the door opening operation is not detected, the washing mode set by the user is modified, and the laundry to be washed is washed using the modified washing mode.
In a scene, if the target type clothes are jeans type clothes, because the relationship between the color losing condition of the jeans type clothes and the water temperature of washing is large, parameters for controlling the washing temperature in the washing mode set by a user are modified to reduce the washing temperature, avoid the color losing of the jeans type clothes and prevent dyeing.
In another scenario, if the target type of clothes is a wool type of clothes, the problem of shrinkage occurs due to a fast rotation speed when the wool type of clothes is washed by a machine, so that a parameter for controlling the rotation speed of the motor in the washing mode set by a user is modified to reduce the rotation speed of the motor to a suitable rotation speed range of the wool type of clothes, and the shrinkage and deformation of the wool type of clothes are avoided.
It should be noted that if it is recognized that the target type of laundry is no longer in the washing tub, and the obtaining is detecting the operation of opening the door by the user, it is considered that the target type of laundry is taken out by the user, and the washing mode set by the user in step 403 may be adopted for washing.
It should be added that, as a possible implementation manner, before acquiring the laundry image of the laundry to be washed, the washing mode set by the user is acquired, and when the washing mode set by the user is the non-target type laundry washing mode, the laundry washing method of the present invention is used for performing the recognition washing.
Such as: in a scene, if the target type clothes is jeans, when the washing mode set by a user is not the jeans washing mode, acquiring clothes images of the clothes to be washed, and inputting the clothes images into each classification model trained in advance to determine whether the clothes images have the characteristics of the jeans learned by each classification model; if the clothes image is determined to have at least one characteristic learned by the classification model, sending a prompt for taking out the jeans, starting countdown, and recognizing that the jeans is in the washing barrel or not detecting the door opening operation when the countdown is finished; and modifying parameters for controlling the washing temperature in the washing mode set by the user to reduce the washing temperature, avoid the color loss of the jeans and prevent dyeing.
In another scene, if the target type clothes is wool clothes, when the washing mode set by the user is not a wool washing mode, acquiring clothes images of the clothes to be washed, and inputting the clothes images into each classification model trained in advance to determine whether the clothes images have the characteristics of the wool clothes learned by each classification model; if the clothes image is determined to have at least one characteristic learned by the classification model, sending a prompt for taking out the wool sweater, starting countdown, and recognizing that the wool sweater is in the washing barrel or the door opening operation is not detected when the countdown is finished; and modifying parameters for controlling the rotating speed of the motor in the washing mode set by the user so as to reduce the rotating speed of the motor to a proper rotating speed range of the woolen sweater and avoid shrinkage and deformation of the woolen sweater.
In the clothes washing method of the embodiment of the invention, the specific characteristics of clothes are identified through each pre-trained classification model which has learned the characteristics of the target type clothes, the identification granularity is finer, the identification accuracy of the target clothes is improved, if the clothes image has at least one characteristic learned by the classification model, before the clothes are washed, a user can be prompted to take out the target type clothes to avoid the dyeing or shrinkage problem of the target type clothes in the washing process, after the prompting time is over, whether the target type clothes are taken out is judged, if not, the washing mode set by the user is modified, the clothes to be washed are washed by using the modified washing mode, the determination accuracy of the washing mode is improved, if the clothes image does not have the characteristics learned by the classification model, the washing mode of user equipment is adopted for washing, the accuracy of laundry washing pattern determination is improved.
In order to realize the above embodiment, the invention also provides a clothes washing device.
Fig. 6 is a schematic structural diagram of a laundry washing device according to an embodiment of the present invention.
As shown in fig. 6, the apparatus includes: an acquisition module 61, an identification module 62 and a washing module 63.
An obtaining module 61 for obtaining a laundry image of the laundry to be washed.
And the recognition module 62 is used for inputting the clothes images into the classification models trained in advance to determine whether the clothes images have the characteristics of the target type clothes learned by the classification models.
And a washing module 63, configured to modify a washing mode set by a user if the laundry image has at least one feature learned by the classification model, and wash the laundry using the modified washing mode.
Further, in a possible implementation manner of the embodiment of the present invention, the apparatus further includes a training module.
The training module is used for acquiring a clothes image set of target type clothes, wherein images in the same clothes image set show the same component of the clothes; and training a corresponding classification model by adopting the sample image set.
As a possible implementation, the target type garment is a jeans type garment, the components comprising: at least one or more of a fabric, a suture, a button, and a waist of pants.
As another possible implementation, the target type of garment is a wool type garment, the components comprising: at least one or more of a coarse grain textured facestock, a fine grain textured facestock, and a fluff containing facestock.
As a possible implementation manner, the identification module 62 is specifically configured to:
dividing the clothes image into a plurality of areas; inputting each region into each classification model respectively to determine whether each region has at least one characteristic learned by the classification model; when there is at least one region having features learned by the at least one classification model, determining that the clothing image has features of the target type clothing.
As a possible implementation manner, the washing module 63 is specifically further configured to:
if the clothes image does not have the characteristics learned by each classification model, the clothes are washed by adopting a washing mode set by a user.
As a possible implementation, the apparatus may further include: the device comprises a prompt module and a processing module.
And the prompt module is used for sending out a prompt for taking out the target type clothes and starting countdown.
And the processing module is used for recognizing that the target type of clothes is in the washing barrel or the door opening operation is not detected when the countdown is finished.
As a possible implementation manner, when the target type of clothes is jeans type clothes, the washing module 63 may be further configured to:
the parameter for controlling the washing temperature in the washing mode set by the user is modified to lower the washing temperature.
As a possible implementation manner, when the target type of clothes is wool type clothes, the washing module 63 may be further configured to:
and modifying parameters for controlling the rotating speed of the motor in the washing mode set by the user so as to reduce the rotating speed of the motor to a proper rotating speed range of woolen-type clothes.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and is not repeated herein.
In the clothes washing device of the embodiment of the invention, the specific characteristics of clothes are identified through each pre-trained classification model which is learned to the characteristics of the target type clothes, the identification granularity is finer, the identification accuracy of the target clothes is improved, if the clothes image has at least one characteristic learned by the classification model, before the clothes are washed, a user can be prompted to take out the target type clothes to avoid the dyeing or shrinkage problem of the target type clothes in the washing process, after the prompting time is over, whether the target type clothes are taken out is judged, if the target type clothes are not taken out, the washing mode set by the user is modified, the clothes to be washed are washed by using the modified washing mode, the determination accuracy of the washing mode is improved, if the clothes image does not have the characteristics learned by the classification model, the washing mode of user equipment is adopted for washing, the accuracy of laundry washing pattern determination is improved.
In order to achieve the above embodiments, an embodiment of the present invention further provides a laundry treating apparatus, including: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, performs a laundry washing method as described in the preceding method embodiments.
In order to implement the above embodiments, the present invention also proposes a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a laundry washing method as described in the aforementioned method embodiments.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A method for washing laundry, characterized in that it comprises the following steps:
acquiring a clothes image of clothes to be washed;
inputting the clothes images into each classification model trained in advance to determine whether the clothes images have the characteristics of target type clothes learned by each classification model;
if the clothes image has at least one characteristic learned by the classification model, modifying a washing mode set by a user, and washing the clothes to be washed by using the modified washing mode;
before inputting the clothes image into each classification model trained in advance to determine whether the clothes image has the characteristics of the target type clothes learned by each classification model, the method further comprises the following steps:
acquiring a clothes sample image set of the target type clothes; the method comprises the steps that images in the same clothes sample image set show the same component of clothes, and the number of the clothes sample image sets of the clothes of the target type to be obtained is determined according to the characteristics of the clothes of the target type to be identified;
and training a corresponding classification model by adopting the clothes sample image set.
2. A laundry washing method according to claim 1, wherein said inputting the laundry image into each classification model trained in advance to determine whether the laundry image has the characteristics of the target type of laundry learned by each classification model comprises:
dividing the laundry image into a plurality of regions;
inputting each region into each classification model respectively to determine whether each region has at least one characteristic learned by the classification model;
determining that the clothing image has the features of the target type of clothing when there is at least one region having the features learned by the at least one classification model.
3. A laundry washing method according to claim 1, characterized in that the target type of laundry is a jeans type of laundry, the components comprising: at least one or more of fabric, stitching, buttons, and waist of pants;
the target type of garment is a wool type garment, the components comprising: at least one or more of a coarse grain textured facestock, a fine grain textured facestock, and a fluff containing facestock.
4. A laundry washing method according to any one of claims 1-3, wherein after inputting the laundry image into each classification model trained in advance to determine whether the laundry image has the characteristics of the target type of laundry learned by each classification model, further comprising:
and if the clothes image does not have the characteristics learned by each classification model, washing the clothes by adopting a washing mode set by a user.
5. A method for washing clothes according to any of claims 1-3, wherein said modifying the washing mode set by the user, before washing said clothes to be washed using the modified washing mode, further comprises:
sending out a prompt for taking out the target type clothes, and starting countdown;
when the countdown is over, it is recognized that the target type of laundry is in the washing tub, or, a door opening operation is not detected.
6. A laundry washing method according to any one of claims 1-3, wherein the target type of laundry is a jeans type of laundry, the modifying the washing mode set by the user, and washing the laundry using the modified washing mode comprises:
the parameter for controlling the washing temperature in the washing mode set by the user is modified to lower the washing temperature.
7. A laundry washing method according to any one of claims 1-3, wherein the target type of laundry is a wool type of laundry, the modifying the washing mode set by the user, and washing the laundry using the modified washing mode comprises:
and modifying parameters for controlling the rotating speed of the motor in the washing mode set by the user so as to reduce the rotating speed of the motor to a proper rotating speed range of the woolen type clothes.
8. A laundry washing apparatus, characterized in that it comprises:
the acquisition module is used for acquiring a clothes image of the clothes to be washed;
the recognition module is used for inputting the clothes image into each classification model trained in advance so as to determine whether the clothes image has the characteristics of the target type clothes learned by each classification model;
the washing module is used for modifying a washing mode set by a user if the clothes image has at least one characteristic learned by the classification model, and washing the clothes to be washed by using the modified washing mode;
the device, still include:
the training module is used for acquiring a clothes sample image set of the target type clothes, wherein images in the same clothes sample image set show the same component of the clothes, and the number of the clothes sample image sets of the target type clothes to be acquired is determined according to the characteristics of the target type clothes to be identified; and training a corresponding classification model by adopting the clothes sample image set.
9. A laundry treating apparatus, comprising: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements a laundry washing method as claimed in any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a laundry washing method according to any one of claims 1-7.
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| CN115272780B (en) * | 2022-09-29 | 2022-12-23 | 北京鹰瞳科技发展股份有限公司 | Method for training multi-label classification model and related product |
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| KR102478453B1 (en) * | 2016-01-28 | 2022-12-16 | 엘지전자 주식회사 | Laundry machine and Controlling Method of the Same |
| CN106854808B (en) * | 2017-01-22 | 2020-07-14 | 无锡小天鹅电器有限公司 | Washing machine and washing control method and device thereof |
| CN107574625A (en) * | 2017-08-18 | 2018-01-12 | 珠海格力电器股份有限公司 | Washing methods, device, storage device, mobile terminal and the washing machine of clothing |
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