CN111144514A - Household appliance model identification method and device, storage medium and electronic equipment - Google Patents
Household appliance model identification method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The present disclosure relates to the field of image recognition technologies, and in particular, to a method and an apparatus for recognizing a home appliance model, a storage medium, and an electronic device. The method comprises the following steps: the method comprises the steps of obtaining images of the household appliances, judging whether the images meet preset identification conditions or not, and identifying the images through a trained neural network model when the images meet the preset identification conditions so as to obtain the household appliance models corresponding to the images and display the household appliance models. The problem of can not directly obtain the technical problem of corresponding product model and service instruction according to the discernment to the product sign among the prior art is solved.
Description
Technical Field
The present disclosure relates to the field of image recognition technologies, and in particular, to a method and an apparatus for recognizing a home appliance model, a storage medium, and an electronic device.
Background
Through the corresponding introduction and the use instruction of the product, the product can be efficiently and quickly known and the use method of the product can be mastered. The corresponding introduction and the instruction of the product are important in the process of knowing the product and mastering the using method of the product.
And the traditional paper specification is inconvenient to carry, easy to lose, inconvenient to modify and resource-wasting. The electronic specification only electronizes the content of the paper specification, and in the using process of the product, a user needs to actively search the corresponding introduction and the use specification, so that the electronic specification is not simple and convenient, and the paper specification is easy to lose.
The technical problem that the corresponding product model and the use instruction cannot be directly obtained according to the identification of the product mark exists in the related technology.
Disclosure of Invention
In view of the above problems, the present disclosure provides a method and an apparatus for identifying a home appliance model, a storage medium, and an electronic device, which solve the technical problem in the related art that a corresponding product model and a usage specification cannot be directly obtained according to identification of a product identifier.
In a first aspect, the present disclosure provides a method for identifying a model of a home appliance, where the method includes:
acquiring an image of the household appliance;
judging whether the image meets a preset identification condition;
and when the image meets a preset identification condition, identifying the image through a trained neural network model to obtain the household appliance model corresponding to the image and displaying the household appliance model.
According to an embodiment of the present disclosure, optionally, in the method for identifying a home appliance model, determining whether the home appliance image meets a preset identification condition includes:
identifying the image, and judging whether the image contains a preset household appliance mark or not and whether the image quality meets a preset requirement or not;
when the image contains a preset household appliance mark and the image quality meets a preset requirement, the image meets a preset identification condition.
According to an embodiment of the present disclosure, optionally, in the method for identifying a home appliance model, determining whether the image quality meets a preset requirement includes:
judging whether the integrity of the household appliance marks in the image meets a preset condition or not;
when the integrity of the household appliance mark in the image meets a preset condition, acquiring a fuzzy value of the household appliance mark;
and judging whether the fuzzy value is less than or equal to a preset fuzzy value or not, wherein when the fuzzy value is less than or equal to the preset fuzzy value, the image quality meets the preset requirement.
According to an embodiment of the present disclosure, optionally, in the method for identifying a home appliance model, determining whether the integrity of the home appliance mark in the image meets a preset condition includes:
acquiring the illumination of the image;
when the illumination of the image is determined to be greater than or equal to the preset illumination, acquiring the resolution of the household appliance mark in the image;
judging whether the resolution of the household appliance mark in the image is greater than or equal to a preset value or not;
and when the resolution ratio is greater than or equal to a preset value, the integrity of the household appliance mark in the image meets a preset condition.
According to an embodiment of the present disclosure, optionally, in the method for identifying a model of a home appliance, the training process of the neural network model includes:
acquiring an image set of the household appliance;
extracting the household appliance marks in the image to obtain a household appliance mark training set;
and training the neural network model to be trained through the household appliance sign training set to obtain the trained neural network model.
According to an embodiment of the present disclosure, optionally, in the method for identifying a home appliance model, after obtaining the home appliance model corresponding to the image, the method further includes:
according to the type of the household appliance, inquiring in a preset database to obtain household appliance information corresponding to the type of the household appliance and displaying the household appliance information;
and each household appliance model in the database corresponds to one household appliance information respectively.
According to an embodiment of the present disclosure, optionally, in the method for identifying a model of a home appliance, a storage format of the home appliance information in the preset database includes at least one of an image format, a text format, a video format, an audio format, and an AR format.
In a second aspect, the present disclosure provides an apparatus for identifying a model of an appliance, the apparatus including:
an acquisition module configured to acquire an image of the home appliance;
a judging module configured to judge whether the image satisfies a preset recognition condition;
the processing module is configured to recognize the images through the trained neural network model when the images meet preset recognition conditions so as to obtain the home appliance models corresponding to the images;
a display module configured to display the appliance model.
In a third aspect, the present disclosure provides a storage medium storing a computer program, executable by one or more processors, for implementing the method for identifying a model of an appliance as described above.
In a fourth aspect, the present disclosure provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, performs the method for identifying the model of the household appliance.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
the method comprises the steps of obtaining images of household appliances, judging whether the images meet preset identification conditions, and identifying the images through a trained neural network model when the images meet the preset identification conditions so as to obtain the household appliance models corresponding to the images and display the household appliance models. The product specification is paperless, and product information does not need to be identified through a specific identifier. The problem that the corresponding product model and the use instruction cannot be obtained according to the identification of the product mark in the prior art is solved.
Drawings
The present disclosure will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings:
fig. 1 is a schematic flow chart of a method for identifying a home appliance model according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a training process of a neural network model provided in an embodiment of the present disclosure;
fig. 3 is another schematic flow chart of a method for identifying a home appliance model according to an embodiment of the present disclosure;
fig. 4 is another schematic flow chart of a method for identifying a home appliance model according to an embodiment of the present disclosure;
fig. 5 is a connection block diagram of an apparatus for identifying a home appliance model according to an embodiment of the present disclosure.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
Embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and examples, so that how to apply technical means to solve technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and the features of the embodiments of the present disclosure can be combined with each other without conflict, and the formed technical solutions are all within the protection scope of the present disclosure.
The utility model provides a household appliance model identification method, a household appliance model identification device, a storage medium and an electronic device, which comprises the steps of obtaining images of household appliances, judging whether the images meet preset identification conditions, and identifying the images through a trained neural network model when the images meet the preset identification conditions so as to obtain the household appliance model corresponding to the images and display the household appliance model.
Example one
Fig. 1 is a schematic flow chart of a method for identifying a home appliance model according to an embodiment of the present disclosure. As shown in fig. 1, the method includes:
step S110: and acquiring an image of the household appliance.
Preferably, the image of the home appliance may be an image obtained by a user when the user takes a picture using an electronic device such as a mobile phone, or may be an image of the home appliance stored in the electronic device.
The images of the household appliances are divided into two types including household appliance mark images and household appliance mark images.
Preferably, the image of the home appliance is a 64 × 3 rgb (red green blue) picture.
Step S120: and judging whether the image meets a preset identification condition.
Specifically, the image is identified, and whether the image contains a preset household appliance mark is judged; when the image contains a preset household appliance mark, judging whether the image quality meets a preset requirement; and when the image quality meets the preset requirement, the image meets the preset identification condition.
Further, judging whether the image quality meets a preset requirement includes: judging whether the illumination of the image is greater than or equal to a preset illumination, and acquiring the resolution of the household appliance mark in the image when the illumination is greater than or equal to the preset illumination; judging whether the integrity of the household appliance mark meets the requirement or not by judging whether the resolution is equal to a preset value or not, when the resolution is equal to the preset value, the integrity of the household appliance mark meets the requirement, and mapping the resolution and a preset interval to obtain a fuzzy value of the household appliance mark; and judging whether the fuzzy value is less than or equal to a preset fuzzy value, and when the fuzzy value is less than or equal to the preset fuzzy value, the image meets a preset identification condition.
Preferably, the value range of the illumination of the image is [0, 255], the illumination of the image is better as the illumination of the image is closer to 255 lux, and the illumination degree of the image meets the requirement when the illumination of the image is greater than the preset illumination.
Preferably, when the resolution is equal to the preset value, that is, the integrity of the household appliance mark meets the requirement, the resolution is mapped with the preset fuzzy degree interval to obtain the fuzzy value of the household appliance mark.
Preferably, the preset fuzzy degree interval is [0,1], wherein 0 is clear and 1 is fuzzy. For example, when the resolution is mapped with the preset fuzzy degree interval to obtain that the fuzzy value of the household appliance mark is 0.6, comparing the fuzzy value 0.6 with the preset fuzzy value, and determining whether the fuzzy value 0.6 is less than or equal to the preset fuzzy value, thereby determining whether the image meets the preset identification condition.
The preset illumination, the preset value and the preset fuzzy value are manually preset according to the actual image recognition condition.
Step S130: and when the image meets the preset identification condition, identifying the image through the trained neural network model to obtain the household appliance model corresponding to the image.
Further, the training process of the neural network model comprises the following steps: acquiring an image set of the household appliance, and extracting a household appliance mark in each image in the image set to obtain a household appliance mark training set; and training the pre-trained neural network model through a household appliance sign training set to obtain the trained neural network model.
As shown in fig. 2, an image set of a home appliance is obtained, a home appliance mark in the image is extracted to obtain a home appliance mark training set, a pre-trained neural network model is trained through the home appliance mark training set to obtain a trained neural network model, the recognition rate of the trained neural network model is obtained, whether the recognition rate is greater than 90% or not is judged, that is, whether the trained neural network model meets a preset use requirement or not is evaluated, and when the recognition rate is greater than 90% or not, that is, when the trained neural network model meets the preset use requirement is evaluated, the trained neural network model is obtained. And when the recognition rate is less than or equal to 90 percent, namely the trained neural network model is evaluated not to meet the preset use requirement, re-acquiring the image set of the household appliance, and training again.
Further, in step S130: when the image satisfies the preset recognition condition, the recognition is carried out through the trained neural network model, and after the household appliance model corresponding to the image is obtained, the method further comprises the following steps: and inquiring in a preset database according to the type of the household appliance to obtain the household appliance information corresponding to the type of the household appliance.
Each household appliance model in the preset database corresponds to one household appliance information.
Preferably, the storage format of the home appliance information in the database includes an image format, a text format, a video format, an audio format, and an AR (Augmented Reality) format, among others.
Preferably, the appliance information includes a profile of the appliance and a specification of the appliance.
As shown in fig. 3, an image of a home appliance is acquired, whether the image meets a preset identification condition is judged, when the image meets the preset identification condition, the image is identified through a trained neural network model to obtain a home appliance model corresponding to the image, and according to the home appliance model, the home appliance information corresponding to the home appliance model is obtained and displayed in a preset database.
For example, a user uploads a picture of a LOGO (LOGO) of an electrical appliance to a server through two modes of uploading or taking a picture at a client, and a system calls an image quality judgment module to judge whether the image can be identified, specifically, the image quality judgment module judges from the illumination degree of the LOGO, the integrity of the LOGO and the definition of the LOGO in the image. And when the judgment result is that the picture cannot be identified, the server feeds back the information of the re-uploaded picture to the user.
And when the judgment result is that the image can be identified, the system calls the identification module to identify the image to obtain the electric appliance model corresponding to the image, inquires in a preset database according to the obtained electric appliance model to obtain information such as the introduction of the electric appliance corresponding to the electric appliance model, a user instruction manual and the like, and feeds the information back to the client side for display.
The electric appliance manufacturer makes various format introductions and operation specifications of various electric appliances, and stores the introductions and the specifications of the electric appliances in a preset database. The format of the description and the introduction of the appliance includes at least one of a text format, a video format, an audio format, and an AR format.
Preferably, the user can select the introduction of the electric appliance and the format of the specification according to the preference, and the client displays the electric appliance according to the selection of the user.
Preferably, the merchant can provide the accessed user with the new version of the specification by modifying and replacing the existing specification in the database, so that the user can know the latest dynamics of the product, and the mode also saves resources and reduces the cost.
Preferably, by counting the number of times of accessing a certain electric appliance, the merchant can know the interest tendency and preference of the user, so as to adjust the marketing strategy more specifically.
Preferably, a feedback function can be added in the client to collect user opinions and improve the user satisfaction.
The convenient technology for photographing and identifying reduces the cost for users to know feedback and draws the distance between the users and the products and the merchants closer. In addition, through the photographing recognition function, the interest of the user who does not purchase is converted into the knowledge of the user about the product, and the product sale is promoted.
The embodiment provides a household appliance model identification method which comprises the steps of obtaining images of household appliances, judging whether the images meet preset identification conditions or not, and identifying the images through a trained neural network model when the images meet the preset identification conditions so as to obtain household appliance models corresponding to the images and display the household appliance models. The corresponding product model and the use instruction are obtained according to the identification of the product mark, so that the product instruction is papered, the printing cost is saved, the use instruction of the product can be updated in time, and the timeliness of product information is improved.
Example two
Fig. 4 is another schematic flow chart of a method for identifying a home appliance model according to an embodiment of the present disclosure. As shown in fig. 4, acquiring an image of the home appliance, and determining whether the image satisfies a preset identification condition; when the image meets the preset identification condition, identifying through the trained neural network model to obtain the household appliance model corresponding to the image; and inquiring in a preset database according to the type of the household appliance to obtain household appliance information corresponding to the type of the household appliance, outputting the household appliance information and displaying the household appliance information.
And when the image does not meet the preset identification condition, re-acquiring the image of the household appliance, and judging whether the acquired image meets the preset identification condition again until the acquired image of the household appliance meets the preset identification condition.
The specific embodiment process of the above method steps can be referred to as embodiment one, and the detailed description of this embodiment is not repeated herein.
EXAMPLE III
Fig. 5 is a connection block diagram of an identification apparatus 20 for a home appliance model according to an embodiment of the present disclosure. As shown in fig. 5, the apparatus 20 includes:
an acquisition module 21 configured to acquire an image of the home appliance;
a judging module 22 configured to judge whether the image satisfies a preset recognition condition;
and the processing module 23 is configured to, when the image meets a preset recognition condition, recognize the image through the trained neural network model to obtain a home appliance model corresponding to the image, and control the display module 24 to display the home appliance model.
Further, the apparatus may further include an information invoking module, which is configured to obtain the household appliance information corresponding to the household appliance model in the database according to the household appliance model, and control the display module 24 to display the household appliance information.
The present disclosure also provides another preferred embodiment of a device for identifying a home appliance model, in this embodiment, the device for identifying a home appliance model includes: a processor, wherein the processor is configured to execute the following program modules stored in the memory: an acquisition module configured to acquire an image of the home appliance; a judging module configured to judge whether the image satisfies a preset recognition condition; and the processing module is configured to perform processing on the image through the trained neural network model when the image meets a preset identification condition to obtain a household appliance model corresponding to the image, control the display module to display the household appliance model, obtain household appliance information corresponding to the household appliance model in the database according to the household appliance model and control the display module to display the household appliance information.
The specific embodiment process of the above method steps can be referred to as embodiment one, and the detailed description of this embodiment is not repeated herein.
Example four
The present embodiments also provide a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., having stored thereon a computer program that when executed by a processor, performs the following method steps:
acquiring an image of the household appliance;
judging whether the image meets a preset identification condition;
and when the image meets the preset identification condition, identifying the image through the trained neural network model to obtain the household appliance model corresponding to the image and display the household appliance model.
The specific embodiment process of the above method steps can be referred to as embodiment one, and the detailed description of this embodiment is not repeated herein.
EXAMPLE five
The embodiment of the present disclosure provides an electronic device, which may be a mobile phone, a computer, or a tablet computer, and the electronic device includes a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, implements a method for identifying a home appliance model as in the first embodiment. It is understood that the electronic device may also include multimedia components, input/output (I/O) interfaces, and communication components.
The processor is used for executing all or part of the steps in the identification method of the household appliance model in the first embodiment. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The Processor may be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the method for identifying the model of the household appliance in the first embodiment.
The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
In summary, according to the identification method and apparatus for the home appliance model, storage medium and electronic device provided by the present disclosure, an image of a home appliance is obtained, whether the image meets a preset identification condition is determined, when the image meets the preset identification condition, the image is identified through a trained neural network model to obtain the home appliance model corresponding to the image and display the home appliance model, and according to the home appliance model, the home appliance information corresponding to the home appliance model is obtained and displayed by querying in a preset database. The corresponding product model and the use instruction are obtained and displayed according to the identification of the image, so that the product instruction is demodium, paper product instruction is not needed, and product information is not needed to be identified through a specific mark, so that a user can obtain product introduction and use information more easily and conveniently when using the product, a merchant can more conveniently and quickly update the instruction, and the user can know and trust the product.
In the several embodiments provided in the embodiments of the present disclosure, it should be understood that the disclosed system and method may be implemented in other manners. The system and method embodiments described above are merely illustrative.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although the embodiments disclosed in the present disclosure are described above, the above description is only for the convenience of understanding the present disclosure, and is not intended to limit the present disclosure. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (10)
1. A method for identifying the model of a household appliance is characterized by comprising the following steps:
acquiring an image of the household appliance;
judging whether the image meets a preset identification condition;
and when the image meets a preset identification condition, identifying the image through a trained neural network model to obtain the household appliance model corresponding to the image and displaying the household appliance model.
2. The method of claim 1, wherein determining whether the home appliance image satisfies a predetermined recognition condition comprises:
identifying the image, and judging whether the image contains a preset household appliance mark or not and whether the image quality meets a preset requirement or not;
when the image contains a preset household appliance mark and the image quality meets a preset requirement, the image meets a preset identification condition.
3. The method of claim 2, wherein determining whether the image quality meets a predetermined requirement comprises:
judging whether the integrity of the household appliance marks in the image meets a preset condition or not;
when the integrity of the household appliance mark in the image meets a preset condition, acquiring a fuzzy value of the household appliance mark;
and judging whether the fuzzy value is less than or equal to a preset fuzzy value or not, wherein when the fuzzy value is less than or equal to the preset fuzzy value, the image quality meets the preset requirement.
4. The method of claim 3, wherein determining whether the integrity of the home appliance flag in the image satisfies a predetermined condition comprises:
acquiring the illumination of the image;
when the illumination of the image is determined to be greater than or equal to the preset illumination, acquiring the resolution of the household appliance mark in the image;
judging whether the resolution of the household appliance mark in the image is greater than or equal to a preset value or not;
and when the resolution ratio is greater than or equal to a preset value, the integrity of the household appliance mark in the image meets a preset condition.
5. The method of claim 1, wherein the training process of the neural network model comprises:
acquiring an image set of the household appliance;
extracting the household appliance marks in the image to obtain a household appliance mark training set;
and training the neural network model to be trained through the household appliance sign training set to obtain the trained neural network model.
6. The method of claim 1, wherein after obtaining the appliance model corresponding to the image, the method further comprises:
according to the type of the household appliance, inquiring in a preset database to obtain household appliance information corresponding to the type of the household appliance and displaying the household appliance information;
and each household appliance model in the database corresponds to one household appliance information respectively.
7. The method of claim 5, further comprising: the storage format of the household appliance information in the preset database comprises at least one of an image format, a text format, a video format, an audio format and an AR format.
8. An apparatus for recognizing a model of an electric home appliance, the apparatus comprising:
an acquisition module configured to acquire an image of the home appliance;
a judging module configured to judge whether the image satisfies a preset recognition condition;
the processing module is configured to recognize the image through a trained neural network model when the image meets a preset recognition condition so as to obtain a household appliance model corresponding to the image;
a display module configured to display the appliance model.
9. A storage medium storing a computer program executable by one or more processors to perform the method of identifying a model of an appliance according to any one of claims 1 to 7.
10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, performs the method for identifying a model of an appliance according to any one of claims 1 to 7.
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| CN202010001748.9A CN111144514A (en) | 2020-01-02 | 2020-01-02 | Household appliance model identification method and device, storage medium and electronic equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112287174A (en) * | 2020-09-22 | 2021-01-29 | 长沙市到家悠享网络科技有限公司 | Information viewing and processing method, device and storage medium |
| EP3937430A1 (en) * | 2020-07-07 | 2022-01-12 | Deutsche Telekom AG | Method and system for detecting network devices in a home network |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108345907A (en) * | 2018-02-09 | 2018-07-31 | 广东美的制冷设备有限公司 | Recognition methods, augmented reality equipment and storage medium |
| CN109993023A (en) * | 2017-12-29 | 2019-07-09 | 佛山市顺德区美的电热电器制造有限公司 | A kind of household electrical appliances networking indicating means, AR equipment and computer storage medium |
| CN110020039A (en) * | 2017-08-15 | 2019-07-16 | 芜湖美的厨卫电器制造有限公司 | Methods of exhibiting, device, system and the server of specification |
-
2020
- 2020-01-02 CN CN202010001748.9A patent/CN111144514A/en not_active Withdrawn
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020039A (en) * | 2017-08-15 | 2019-07-16 | 芜湖美的厨卫电器制造有限公司 | Methods of exhibiting, device, system and the server of specification |
| CN109993023A (en) * | 2017-12-29 | 2019-07-09 | 佛山市顺德区美的电热电器制造有限公司 | A kind of household electrical appliances networking indicating means, AR equipment and computer storage medium |
| CN108345907A (en) * | 2018-02-09 | 2018-07-31 | 广东美的制冷设备有限公司 | Recognition methods, augmented reality equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3937430A1 (en) * | 2020-07-07 | 2022-01-12 | Deutsche Telekom AG | Method and system for detecting network devices in a home network |
| CN112287174A (en) * | 2020-09-22 | 2021-01-29 | 长沙市到家悠享网络科技有限公司 | Information viewing and processing method, device and storage medium |
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