Clothes material identification method for washing machine and washing machine

Abstract

The invention belongs to the technical field of clothes washing and care, and particularly relates to a clothes material identification method for a washing machine and the washing machine. The method comprises the following steps: acquiring first image data and second image data of clothes to be washed, wherein the clothes to be washed comprise a plurality of articles to be washed; performing region segmentation on the first image data to obtain image segmentation data of each to-be-washed article; according to the distribution position of the image segmentation data in the first image data, obtaining a corresponding relationship between the image segmentation data and a pixel region in the second image data; based on the corresponding relation, a first material category is determined according to the second image data, image recognition is conducted on the image segmentation data according to the first material category, the clothing material category of each to-be-washed article is determined, the clothing material category is a subdivided category of the first material category, and the clothing material category of each to-be-washed article is a subdivided category of the first material category. The method is used for improving the identification accuracy of the clothing material.

Description

Technical Field

[0001] The present application belongs to the technical field of clothing washing and care, and specifically relates to a clothing material identification method for a washing machine and a washing machine. Background Art

[0002] In order to prevent clothes from deforming or wearing out after washing, existing laundry equipment will first identify the material of the clothes and then match the corresponding washing and care program according to the material.

[0003] Existing clothing material recognition methods include spectral recognition, image recognition, etc. Image recognition is based on the surface features of clothing as the basis for judgment, and spectral recognition is based on the absorption capacity of the components of clothing to infrared light as the basis for judgment. However, similar surface features or similar components will affect the recognition results, and the recognition accuracy of clothing materials is low. Summary of the invention

[0004] The purpose of the present application is to provide a method for identifying clothing materials for a washing machine and a washing machine, so as to improve the accuracy of identifying clothing materials.

[0005] According to one aspect of an embodiment of the present application, a method for identifying clothing material for a washing machine is provided, the method comprising:

[0006] Acquire first image data and second image data of laundry to be washed, wherein the first image data and the second image data have the same shooting angle, and the first image data and the second image data have different data types, and the laundry to be washed includes a plurality of laundry items;

[0007] Performing region segmentation on the first image data to obtain image segmentation data of each of the items to be washed;

[0008] Acquire a correspondence between the image segmentation data and a pixel area in the second image data according to a distribution position of the image segmentation data in the first image data;

[0009] Based on the corresponding relationship, the first material category is determined according to the second image data, and image recognition is performed on the image segmentation data according to the first material category to determine the clothing material category of each of the items to be washed, where the clothing material category is a sub-category of the first material category.

[0010] According to one aspect of an embodiment of the present application, a washing machine is provided, the washing machine comprising:

[0011] An image acquisition module, wherein the image acquisition module comprises a first acquisition device and a second acquisition device, wherein the first acquisition device is used to acquire first image data, and the second acquisition device is used to acquire second image data, and the data types of the first image data and the second image data are different;

[0012] A control module, wherein the control module is used to control the image acquisition module to acquire the first image data and the second image data and to execute the above-mentioned clothing material recognition method for a washing machine.

[0013] In this embodiment, firstly, first image data and second image data of the clothes to be washed are obtained, and the data types of the first image data and the second image data are different. The first image data is segmented into regions to obtain image segmentation data of each item to be washed. The image segmentation data is distributed in the first image data, and the pixel region of the clothes to be washed on the second image data is obtained, so as to obtain the correspondence between the first image data and the second image data according to the same item to be washed. According to the correspondence, the first material category is determined according to the second image data. The first material category is used to limit the major category of the clothes material category finally obtained. On this basis, the possibility of misjudging the clothes material category obtained by performing image recognition on the first image data is low. In addition, by recognizing image data of different data types, the recognition accuracy of the clothes material is improved to a certain extent.

[0014] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0015] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The step flow of a clothing material method for a washing machine provided in one embodiment of the present application is schematically shown.

[0017] Figure 2 A schematic flow chart of a method for identifying clothing material for a washing machine provided in this embodiment is shown.

[0018] Figure 3 A schematic flow chart of a method for identifying clothing material for a washing machine provided in another embodiment is shown.

[0019] Figure 4 The structural diagram of a washing machine provided in another embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0021] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0022] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0023] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0024] The clothing material method for a washing machine provided by the present application is described in detail below in conjunction with specific implementation methods.

[0025] See also Figure 1 , Figure 1 The step flow of a clothing material method for a washing machine provided in one embodiment of the present application is schematically shown.

[0026] This embodiment provides a method for determining clothing material for a washing machine, the method comprising:

[0027] S110, acquiring first image data and second image data of laundry to be washed, wherein the first image data and the second image data have the same shooting angle, and the first image data and the second image data have different data types, and the laundry to be washed includes a plurality of items to be washed.

[0028] Specifically, when the laundry to be washed is put into the washing drum of the washing machine, the laundry to be washed is photographed by an image capturing device equipped in the washing drum to obtain the first image data and the second image data.

[0029] The first image data and the second image data are obtained at the same shooting position and the same angle of the laundry to be washed. Therefore, the position distribution of each item to be washed in the laundry to be washed in the first image data and the second image data is consistent.

[0030] The data types of the first image data and the second image data are inconsistent. The data types referred to here refer to the types of image features caused by the imaging mechanisms of the first image data and the second image data.

[0031] For example, the first image data is an image formed within the visible light range, and the second image data is an image formed by infrared light reflection. On this basis, the first image data is obtained by taking pictures with an ordinary camera, and the second image data is obtained by taking pictures with an infrared spectrum camera.

[0032] The laundry to be washed includes a plurality of items to be washed, and the first image data and the second image data referred to in this embodiment include imaging features of at least one item to be washed.

[0033] S120, performing region segmentation on the first image data to obtain image segmentation data of each item to be washed.

[0034] Specifically, region segmentation refers to segmenting the first image data into an image of only one object to be washed. The image segmentation data is an image containing only the object to be washed obtained by segmenting the pixel region of each object to be washed in the first image data.

[0035] S130: Acquire a correspondence between the image segmentation data and a pixel area in the second image data according to a distribution position of the image segmentation data in the first image data.

[0036] Specifically, since the first image data and the second image data are shot at the same angle, the distribution position of the same item to be washed in the first image data is the same as the position of the item to be washed in the second image data.

[0037] The distribution position of the image segmentation data in the first image data is obtained to obtain the corresponding relationship between the image segmentation data and the pixel area in the second image data. The corresponding relationship is to characterize the association between the first image data and the second image data of the same object to be washed.

[0038] S140, based on the corresponding relationship, determine the first material category according to the second image data, perform image recognition on the image segmentation data according to the first material category, and determine the clothing material category of each item to be washed, where the clothing material category is a subdivision of the first material category.

[0039] According to the corresponding relationship, the second image data of the laundry object is identified with the laundry object as a unit to obtain the first material category.

[0040] After obtaining the first material category, image recognition is performed on the first image data based on the first material category to obtain the clothing material category.

[0041] The clothing material category refers to the material of the items to be washed, and the first material category is a superordinate type of the material of the clothing to be washed, and the clothing material category is a part of the first material category.

[0042] For example, the clothing material category finally determined is wool, and the first material category may be animal fiber.

[0043] The clothing material recognition method for a washing machine provided in the present embodiment obtains first image data and second image data of clothing to be washed, wherein the data types of the first image data and the second image data are different; firstly, the pixel area of ​​each article to be washed in the first image data is image segmented to obtain the image segmentation data of each article to be washed; because the shooting angles of the first image data and the second image data are the same, the correspondence between the image segmentation data and the pixel area in the second image data is obtained according to the distribution position of the image segmentation data on the first image data. This step is to associate the first image data and the second image data of the same article to be washed, and determine the first material category based on the second image data according to the correspondence; based on the first material category, the second image data is processed by image recognition technology to finally determine the clothing material category, and the clothing material information is jointly confirmed by the two recognition methods, thereby improving the recognition accuracy of the clothing material to a certain extent.

[0044] As an optional implementation, in S110, obtaining first image data and second image data of the laundry to be washed includes:

[0045] S111, obtaining a door closing instruction after the laundry is put into the washing machine, and rotating the washing drum of the washing machine according to a preset frequency.

[0046] S113, after the washing drum rotates, the laundry to be washed is photographed to obtain multiple sets of first image data and second image data with the same time stamp.

[0047] Optionally, the user can put the laundry to be washed into the washing machine at once or put the laundry to be washed into the washing machine one by one.

[0048] When the user puts the clothes to be washed into the washing machine at one time, after receiving the door closing instruction, the washing drum rotates according to the preset frequency.

[0049] After the washing drum rotates, the laundry to be washed is photographed to obtain multiple sets of first image data and second image data with the same time stamp.

[0050] The first image data and the second image data with the same time stamp are grouped together, and the number of groups is the same as the number of times the washing machine rotates.

[0051] When performing S120 to S140 of the above embodiment, operations are performed on the same set of first image data and second image data.

[0052] Multiple groups of first image data and second image data may be identified simultaneously, so as to determine the clothing material category of the same to-be-washed article according to the identification result of each group.

[0053] It should be noted that the image capture device does not rotate with the rotation of the washing drum, that is, when the washing drum rotates, the relationship between the items to be washed in the washing drum will change accordingly. This process ensures that the first image data and the second image data of all the items to be washed can be obtained.

[0054] In this embodiment, the relative positions of the items to be washed are adjusted by rotating the washing drum, so as to obtain the clothing material categories of all the items to be washed.

[0055] As an optional implementation, in S120, the first image data is segmented into regions to obtain image segmentation data of each item to be washed, including:

[0056] S121, performing target recognition on the first image data through an image segmentation algorithm to determine the pixel area of ​​each item to be washed.

[0057] S123, segmenting the first image data based on the pixel area to obtain image segmentation data of the items to be washed.

[0058] Specifically, the first image data is subjected to target recognition through an image segmentation algorithm to determine the pixel area of ​​each item to be washed.

[0059] The first image data is segmented according to the pixel area to obtain image segmentation data containing only the items to be washed.

[0060] In this embodiment, the first image data is segmented in order to distinguish the items to be washed, so as to ensure the accuracy of the subsequent identification of the material of the clothes.

[0061] As an optional implementation manner, in S130, obtaining a correspondence between the image segmentation data and a pixel area in the second image data according to a distribution position of the image segmentation data in the first image data includes:

[0062] S131, for each item to be washed, determining the pixel distribution position of the image segmentation data in the first image data.

[0063] S133: Determine the pixel area of ​​the item to be washed on the second image data according to the pixel distribution position.

[0064] S135, obtaining pixel features of the second image data in the pixel area, and associating the pixel features with the image segmentation data to obtain a corresponding relationship.

[0065] Specifically, the pixel distribution position of the image segmentation data in the first image data refers to the position of the image segmentation data in the first image data.

[0066] For example, the first image data is divided into 9 regions, region 1 is the object A to be washed, and the image segmentation data of the object A to be washed is located in region 1 of the first image data.

[0067] The second image data is divided in the same manner, and region 1 of the second image data also corresponds to the article A to be washed.

[0068] Based on the relationship between the two, the pixel area of ​​the items to be washed on the second image data is determined according to the pixel distribution position of the image segmentation data, and the pixel features of the pixel area are associated with the image segmentation data to obtain a corresponding relationship.

[0069] In this embodiment, the purpose of obtaining the correspondence between the image segmentation data and the second image data is to associate the image features of the items to be washed on the first image data with the image features on the second image data, and then identify the clothing material category of the items to be washed based on the two image features.

[0070] As an optional implementation, when the second image data is a spectral image, in S140, based on the corresponding relationship, the first material category is determined according to the second image data, and image recognition is performed on the image segmentation data according to the first material category to determine the clothing material category of each item to be washed, including:

[0071] S141, acquiring the spectrum curve data of the item to be washed in the second image data according to the corresponding relationship.

[0072] S143, comparing the spectrum curve data with a standard curve in a preset database, and determining a first material category according to the comparison result.

[0073] S145, performing image recognition on the image segmentation data to obtain a second material category.

[0074] S147, determining the material category of the clothes to be washed according to the first material category and the second material category, the first material category and the second material category including different material types.

[0075] Specifically, the second image data is a spectral image, which is generated by an infrared spectral camera. The working principle of the infrared spectral camera is based on the irradiation of infrared light and the reflection and absorption of infrared light by an object.

[0076] Optionally, the first image data is an image taken by a common camera, which has image features in the visible light range.

[0077] The spectral curve represents the absorption and reflection capabilities of the items to be washed to infrared light, and clothes made of different materials have different absorption and reflection capabilities to infrared light. Therefore, based on analyzing the spectral curve, the first material category of the items to be washed can be determined.

[0078] After the first material category is determined, image recognition is performed on the image segmentation data based on the first material category to determine the second material category.

[0079] The material category of the clothing is determined according to the first material category and the second material category. For example, the first material category is natural fiber, the second material category is wool, and the clothing material category finally determined is a knitted sweater made of cotton.

[0080] The first material category and the second material category include different material types. More specifically, the second material category includes the second material category.

[0081] For example, if the first material category is natural fiber, then the second material category is cotton, wool, silk, etc. Cotton material or wool material is a lower type of natural fiber.

[0082] For example, if the first material category is man-made fiber, then the second material category is polyester, polyester-cotton, etc., and polyester or polyester-cotton are both lower types of man-made fiber.

[0083] In this embodiment, different materials have different components and have different reflection and absorption capabilities for infrared light. Different materials have different wiring patterns, and image recognition has limited ability to recognize the texture of clothing. Therefore, the first material category is first obtained based on spectral image recognition, and then the second material category is determined based on the first material category. Finally, the clothing material category is determined based on the first material category and the second material category, which is beneficial to improving the recognition accuracy of clothing materials.

[0084] As an optional implementation, in S145, image recognition is performed on the image segmentation data to obtain a second material category, including:

[0085] S1451, perform image recognition on the image segmentation data to obtain actual surface features.

[0086] S1453, comparing the actual surface features with the standard material features to obtain a second material category of the items to be washed, wherein the first material category of the standard material features is the same as the first material category of the clothes to be washed.

[0087] Specifically, image recognition can utilize an image recognition model, and the image recognition model can be trained using training data to distinguish clothing material features.

[0088] The second material category of the object to be washed is obtained by comparing the actual surface features of the object to be washed in the image segmentation data with the standard material features.

[0089] In this embodiment, further and more detailed clothing material identification can be performed through image recognition, thereby improving the recognition accuracy of clothing material, and then a more matching washing and care program can be generated according to the clothing material to ensure the washing and care effect of the clothes to be washed.

[0090] Optionally, the material type of the second material category belongs to the material type of the first material category.

[0091] As an optional implementation, in S140, based on the corresponding relationship, the first material category is determined according to the second image data, image recognition is performed on the image segmentation data according to the first material category, and after the material category of each item to be washed is determined, the method further includes:

[0092] S151, generating washing program recommendation information based on clothing material category.

[0093] S153, push the clothing material category and washing program information to the user so that the user can determine the final washing program.

[0094] Specifically, different clothing materials require different washing methods.

[0095] The recommended information for washing programs is generated based on the clothing material categories of all items to be washed.

[0096] For example, cotton garments require a stronger wash cycle and a higher water temperature, while wool garments require a gentler wash cycle and a lower water temperature to avoid shrinkage and damage.

[0097] Different clothing materials have different requirements for detergents and drying needs. On this basis, the clothing material information of all items to be washed is pushed to the user, and a more suitable washing and care program is recommended to the user.

[0098] The washing program includes information such as water requirements, detergent dosage, water temperature, as well as drying time and washing time.

[0099] The function of this embodiment is to receive the user's confirmation instruction for the washing and care program.

[0100] As an optional implementation, in S151, according to the clothing material category, wash program recommendation information is generated, including:

[0101] S1511, identifying the color category of the items to be washed based on the image segmentation data.

[0102] S1513, generate washing program recommendation information based on color category and clothing material information.

[0103] Specifically, the color category of the items to be washed is identified based on the image segmentation data, and the washing and care program recommendation information is generated based on the color category and the clothing material information. The washing and care program recommendation information includes the washing and care process and may also include laundry suggestions.

[0104] Laundry recommendations are to take out certain colored laundry items to prevent the colored laundry items from fading and staining other colored laundry items.

[0105] Figure 2 A schematic flow chart of a method for identifying clothing material for a washing machine provided in this embodiment is shown.

[0106] like Figure 2 As shown, when the method provided by this embodiment is implemented, the first image data and the second image data are collected simultaneously; the image segmentation data of the items to be washed is determined according to the first image data, and based on the correspondence between the first image data and the second image data, the correspondence between the image segmentation data of the items to be washed and the second image data is obtained.

[0107] The first material category of the item to be washed is identified through the second image data; based on the first material category, image recognition is performed on the image segmentation data to obtain the second material category, and finally the material category of the clothing is determined based on the first material category and the second material category, and finally the washing and care program recommendation information is generated based on the clothing material category.

[0108] Figure 3 A schematic flow chart of a method for identifying clothing material for a washing machine provided in another embodiment is shown.

[0109] exist Figure 2 On the basis of, this embodiment performs image recognition on the image segmentation data to obtain the color category, and generates the washing and care program recommendation information according to the color category and the identified clothing material category.

[0110] Figure 4 FIG. 2 shows a schematic diagram of the structure of the washing machine provided in this embodiment. Figure 4 As shown, an embodiment of the present application provides a washing machine 40, which includes:

[0111] The image acquisition module 410 includes a first acquisition device and a second acquisition device. The first acquisition device is used to acquire first image data, and the second acquisition device is used to acquire second image data. The data types of the first image data and the second image data are different.

[0112] The control module 420 is used to control the image acquisition module 410 to acquire the first image data and the second image data and the above-mentioned clothing material recognition method for the washing machine.

[0113] The image acquisition module 410 can be set at the door of the washing drum, and its position can capture the image of the washing drum without blind spots. The image acquisition module 410 is set separately from the washing drum. When the washing drum rotates, the image acquisition module 410 does not rotate with the washing drum.

[0114] Optionally, a light source is provided in the image acquisition device, and the light source is used to emit light that can cover the washing drum.

[0115] The first acquisition device may be a high-definition camera, and the second acquisition device may be an infrared spectrum camera.

[0116] The control module 420 is electrically connected to the image acquisition device, and is used to control the image acquisition device to capture the first image data and the second image data, and to execute the clothing material recognition method for the washing machine.

[0117] It should be noted that although the steps of the method in the present application are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0118] In particular, according to an embodiment of the present application, the process described in each method flow chart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program contains a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions defined in the system of the present application are executed.

[0119] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0120] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0121] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0122] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0123] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary technical means in the art that are not disclosed in the present application.

[0124] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for identifying clothing material for a washing machine, characterized in that: The method comprises: Acquire first image data and second image data of laundry to be washed, wherein the first image data and the second image data have the same shooting angle, and the first image data and the second image data have different data types, and the laundry to be washed includes a plurality of laundry items; Performing region segmentation on the first image data to obtain image segmentation data of each of the items to be washed; Acquire a correspondence between the image segmentation data and a pixel area in the second image data according to a distribution position of the image segmentation data in the first image data; Based on the corresponding relationship, the first material category is determined according to the second image data, and image recognition is performed on the image segmentation data according to the first material category to determine the clothing material category of each of the items to be washed, where the clothing material category is a sub-category of the first material category.

2. The method for identifying clothing material for a washing machine according to claim 1, characterized in that: The step of acquiring the first image data and the second image data of the laundry to be washed comprises: Obtaining a door closing instruction after the laundry to be washed is placed in the washing machine, and rotating the washing drum of the washing machine according to a preset frequency; After the washing drum rotates, the laundry to be washed is photographed to obtain multiple sets of first image data and second image data with the same time stamp.

3. The method for identifying clothing material for a washing machine according to claim 1, characterized in that: The performing of region segmentation on the first image data to obtain image segmentation data of each of the items to be washed comprises: Using an image segmentation algorithm, target recognition is performed on the first image data to determine the pixel area of ​​each item to be washed; The first image data is segmented based on the pixel area to obtain image segmentation data of the object to be washed.

4. The method for identifying clothing material for a washing machine according to claim 1, characterized in that: The acquiring, according to the distribution position of the image segmentation data in the first image data, a correspondence between the image segmentation data and a pixel area in the second image data comprises: For each of the items to be washed, determining a pixel distribution position of the image segmentation data in the first image data; Determining a pixel area of ​​the item to be washed on the second image data according to the pixel distribution position; Pixel features of the second image data in the pixel area are obtained, and the pixel features are associated with the image segmentation data to obtain a corresponding relationship.

5. The method for identifying clothing material for a washing machine according to claim 1, characterized in that: The second image data is a spectral image, and based on the corresponding relationship, determining the first material category according to the second image data, performing image recognition on the image segmentation data according to the first material category to determine the material category of each piece of clothing to be washed includes: According to the corresponding relationship, obtaining the spectrum curve data of the object to be washed in the second image data; Comparing the spectral curve data with a standard curve in a preset database, and determining the first material category according to the comparison result; Performing image recognition on the image segmentation data to obtain a second material category; The material category of the clothes to be washed is determined according to the first material category and the second material category, and the first material category and the second material category include different material types.

6. The method for identifying clothing material for a washing machine according to claim 5, characterized in that: The performing image recognition on the image segmentation data to obtain a second material category includes: Performing image recognition on the image segmentation data to obtain actual surface features; The actual surface feature is compared with the standard material feature to obtain the second material category of the object to be washed, and the first material category of the standard material feature is the same as the first material category of the clothes to be washed.

7. The method for identifying clothing material for a washing machine according to claim 6, characterized in that: The material type of the second material category belongs to the material type of the first material category.

8. The method for identifying clothing material for a washing machine according to claim 1, characterized in that: After determining the first material category according to the second image data based on the corresponding relationship, performing image recognition on the image segmentation data according to the first material category, and determining the material category of each piece of clothing to be washed, the method further includes: Generate washing program recommendation information according to the clothing material category; The clothing material category and the washing and care program information are pushed to the user so that the user can determine the final washing and care program.

9. The method for identifying clothing material for a washing machine according to claim 8, characterized in that: Generating the washing program recommendation information according to the clothing material category includes: identifying the color category of the items to be washed based on the image segmentation data; The washing program recommendation information is generated based on the color category and the clothing material information.

10. A washing machine, characterized in that: The washing machine comprises: An image acquisition module, wherein the image acquisition module comprises a first acquisition device and a second acquisition device, wherein the first acquisition device is used to acquire first image data, and the second acquisition device is used to acquire second image data, and the data types of the first image data and the second image data are different; A control module, wherein the control module is used to control the image acquisition module to acquire the first image data and the second image data and to execute the clothing material recognition method for a washing machine according to any one of claims 1 to 9.

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