WO2021082284A1

WO2021082284A1 - Baking mold specification detection method and apparatus, and kitchen appliance

Info

Publication number: WO2021082284A1
Application number: PCT/CN2020/071670
Authority: WO
Inventors: 刘彦甲; 苏明月; 高进宝; 李玉强; 冯浩; 王华伟; 王京华
Original assignee: 青岛海尔智能技术研发有限公司; 海尔智家股份有限公司
Priority date: 2019-10-30
Filing date: 2020-01-13
Publication date: 2021-05-06
Also published as: CN112749587A

Abstract

A baking mold specification detection method and apparatus, and a kitchen appliance. The method comprises: obtaining an image of a working area of a kitchen appliance where a baking mold is placed; training the image by means of a convolutional neural network model, and determining location image information of the baking mold in the kitchen appliance, and segmentation image information of a set surface of the baking mold; according to the location image information and the segmentation image information, determining area information of the set surface of the baking mold; and according to stored correspondence between mold areas and mold specifications, determining a mold specification corresponding to the area information. Therefore, the accuracy in determining baking mold specification information is improved.

Description

Method and device for detecting baking mold specifications, and kitchen electrical equipment

This application is filed based on a Chinese patent application with an application number of 201911045086.9 and an application date of October 30, 2019, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby incorporated into this application by reference.

Technical field

This application relates to the technical field of smart equipment, for example, it relates to a method and device for detecting the specifications of a baking mold, and kitchen appliances.

Background technique

At present, kitchen electrical equipment, such as ovens, microwave ovens, air fryer, etc., have the function of grilling food. Generally, the size and weight of the food can be determined by identifying the type of food and the weight detection device, thereby determining the kitchen The set temperature and the set time of the electric equipment to roast the food to achieve the purpose of roasting the food.

However, adding weight detection devices to kitchen appliances, such as gravity sensors, will increase the manufacturing cost of the kitchen appliances. Moreover, the performance and service life of the weight detection devices will affect the size and weight of the ingredients, resulting in the size and weight of the ingredients. The weight is not accurate enough.

Summary of the invention

In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not a general comment, nor is it intended to determine key/important components or describe the scope of protection of these embodiments, but serves as a preface to the detailed description that follows.

The embodiments of the present disclosure provide a method, device and kitchen electrical equipment for detecting the specifications of a baking mold, so as to solve the technical problem that the kitchen electrical equipment determines that the specification information of the baking mold is not accurate enough.

In some embodiments, the method includes:

Obtain an image of the working area of the kitchen electrical equipment with the baking mold;

Training the image through a convolutional neural network model to determine the image information of the position of the baking mold in the kitchen appliance and the segmented image information of the setting surface of the baking mold;

Determining the area information of the set surface of the baking mold according to the position image information and the segmented image information;

According to the stored corresponding relationship between the mold area and the mold specification, the mold specification corresponding to the area information is determined.

In some embodiments, the device includes:

The image acquisition module is configured to acquire an image of the working area of the kitchen appliance on which the baking mold is placed;

The model training module is configured to train the image through a convolutional neural network model, determine the image information of the position of the baking mold in the kitchen appliance, and divide the set surface of the baking mold Image information

An area determination module configured to determine area information of a set surface of the baking mold according to the position image information and the segmented image information;

The specification determination module is configured to determine the mold specification corresponding to the area information according to the stored correspondence relationship between the mold area and the mold specification.

In some embodiments, the baking mold specification detection device includes a processor and a memory storing program instructions, and the processor is configured to execute the above baking mold specification detection method when the program instructions are executed.

In some embodiments, the kitchen electrical equipment includes: the aforementioned baking mold specification detection device.

The method, device and kitchen electrical equipment for baking mold specification detection provided by the embodiments of the present disclosure can achieve the following technical effects:

For the image of the working area of the kitchen electrical equipment, deep learning is carried out through the convolutional neural network model to obtain the specification information of the baking mold in which the ingredients are placed in the kitchen electrical equipment. In this way, the specification information of the ingredients in the baking mold can be determined. It is necessary to add any hardware equipment to determine the specifications of the mold, which reduces the manufacturing resources of kitchen appliances, and does not rely on the performance of the weight measurement hardware equipment, which improves the accuracy of determining the mold specification information.

The above general description and the following description are only exemplary and explanatory, and are not used to limit the application.

Description of the drawings

One or more embodiments are exemplified by the accompanying drawings. These exemplified descriptions and drawings do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation, and among them:

FIG. 1 is a schematic flowchart of a method for detecting specifications of a baking mold provided by an embodiment of the present disclosure;

Figure 2 is a schematic diagram of a MobileNet model training provided by an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a binary mask matrix provided by an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a bit-wise multiplication provided by an embodiment of the present disclosure;

FIG. 5 is a schematic flowchart of a method for detecting specifications of a baking mold provided by an embodiment of the present disclosure;

6 is a schematic structural diagram of a baking mold specification detection device provided by an embodiment of the present disclosure;

FIG. 7 is a schematic structural diagram of a baking mold specification detection device provided by an embodiment of the present disclosure;

Fig. 8 is a schematic structural diagram of a baking mold specification detection device provided by an embodiment of the present disclosure.

Detailed ways

In order to have a more detailed understanding of the features and technical content of the embodiments of the present disclosure, the implementation of the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a number of details are used to provide a sufficient understanding of the disclosed embodiments. However, without these details, one or more embodiments can still be implemented. In other cases, in order to simplify the drawings, well-known structures and devices may be simplified for display.

In the embodiments of the present disclosure, the image of the working area of the kitchen appliance is deep learning through the convolutional neural network model to obtain the specification information of the baking mold in which the food is placed in the kitchen appliance. In this way, the content of the food in the baking mold can be determined Specification information, and further control the baking of ingredients.

FIG. 1 is a schematic flowchart of a method for detecting specifications of a baking mold provided by an embodiment of the present disclosure. As shown in Figure 1, the process of baking mold specification inspection includes:

Step 101: Obtain an image of the working area of the kitchen electrical equipment on which the baking mold is placed.

In the embodiments of the present disclosure, the kitchen electrical equipment includes: ovens, microwave ovens, air fryer and other equipment with baking function. These kitchen appliances can bake or roast the ingredients put in the baking mold. Baking molds are objects that can only place ingredients and have a certain shape, such as: baking molds corresponding to cakes, baking molds corresponding to bread, etc. Therefore, generally, the specification information of the baking mold can be determined to determine the ingredients in the baking mold Specification information.

The kitchen appliance may be equipped with an image acquisition device, so that the image of the working area of the kitchen appliance on which the baking mold is placed can be acquired through the image acquisition device.

Step 102: Train the image through the convolutional neural network model to determine the image information of the position of the baking mold in the kitchen appliance and the segmented image information of the set surface of the baking mold.

Convolutional Neural Network (CNN) has been widely used in the field of computer image processing, and has achieved good results. At present, in order to pursue classification accuracy, the depth of the model is getting deeper and deeper, and the complexity of the model is getting higher and higher. In this embodiment, different neural network models can be used to train the images respectively to obtain the position image information of the baking mold in the kitchen appliance and the segmented image information of the set surface of the baking mold. Among them, the neural compression network model can be used to train the image to obtain the grid mapping matrix corresponding to the hierarchical position of the baking mold in the kitchen appliance; the network model can also be segmented by examples to train the image to obtain the design of the baking mold. The binarization mask matrix of a given surface.

Kitchen appliances, such as ovens, air fryer, etc., may have one, two or more grills. The baking mold can be placed on different grills. Therefore, the image can be trained through the neural compression network model , The level position of the baking mold in the kitchen electrical equipment is obtained, and then the baking mold can be determined on which layer of the grill.

Among them, MobileNet is a lightweight neural compression network model, which has the advantages of small size and high accuracy. It can reduce response delay while ensuring accuracy, and can achieve rapid convergence. Use this lightweight Network training can also reduce the amount of calculation, thereby reducing the requirements for hardware equipment.

Optionally, the image is trained through the MobileNet model to obtain the grid mapping matrix corresponding to the hierarchical position of the baking mold in the kitchen appliance. Of course, in some other embodiments, other types of neural compression network models can also train images to obtain image information of the position of the baking mold in the kitchen appliance.

Fig. 2 is a schematic diagram of a MobileNet model training provided by an embodiment of the present disclosure. In this embodiment, after acquiring the image of the working area of the oven, it is input into the MobileNet model for training, and the level position of the baking mold in the oven can be determined. As shown in Figure 2, the first level can be determined and the results can be obtained. Corresponds to the grid mapping matrix. The grid mapping matrix is a two-dimensional matrix with the same size as the picture taken by the camera, and the value of each element represents the actual area corresponding to the corresponding position.

Mask-RCNN is an instance segmentation network model. Through the Mask-RCNN algorithm model, segmentation tasks, positioning and classification tasks can be completed at the same time, and the mask image corresponding to the image can be obtained. In this embodiment, when training Mask-RCNN, only the mold setting surface is marked, instead of marking the entire mold, because the upper surface of the mold is more helpful for judging the specifications of the container. In some embodiments, the setting surface may be on the mold. surface. The Mask-RCNN obtained by training judges the type of mold and obtains the corresponding binarized mask matrix. Among them, the types of molds may include: sponge cake molds, toast molds, and the like.

Optionally, through the Mask-RCNN model, the image is trained to obtain the binarization mask matrix of the setting surface of the baking mold and the type information of the mold.

FIG. 3 is a schematic diagram of a binary mask matrix provided by an embodiment of the present disclosure. In this embodiment, the obtained image is input into the Mask-RCNN model, and the instance segmentation is performed according to each pixel in the image, and the information is accurately labeled, so that the type judgment and precise position segmentation of the baking mold can be realized, and the result is shown in Figure 3. The binarization mask matrix shown.

Of course, in some other embodiments, other types of instance segmentation network models can also be trained on the image to obtain segmented image information of the set surface of the baking mold.

Step 103: Determine the area information of the set surface of the baking mold according to the position image information and the segmented image information.

The position image information and segmented image information of the baking mold have been known, so that the area information of the set surface of the baking mold can be determined, which can include: position image information and segmented image information, the position image information and segmented image corresponding to the same pixel Information is processed by calculation, such as: addition, multiplication, or weighted addition, etc. Then, the information that meets the set conditions after processing is retained, and based on the retained information, the area information of the set surface of the baking mold is obtained. For example: retain the information that is not equal to 0, or the information greater than the set value, and add the retained information, calculate the difference, or weighted addition, etc., to obtain the area information of the set surface of the baking mold. That is, the position image information and segmented image information corresponding to the same pixel are subjected to the first calculation process, and the information that meets the set conditions after the processing is retained, and the second calculation process is performed according to the retained information to obtain the setting surface area of the baking mold information.

In some embodiments, the grid mapping matrix and the binarized mask matrix can be multiplied by bit to obtain the area matrix information of the setting surface of the baking mold; and the element values in the area matrix information can be summed. , Get the area information of the setting surface of the baking mold.

Fig. 4 is a schematic diagram of a bitwise multiplication provided by an embodiment of the present disclosure. As shown in Figure 4, by multiplying the grid mapping matrix and the binarization mask matrix, the background area in the image can be masked and only the corresponding area of the mold setting surface is retained, that is, the result matrix in Figure 4 is baked The area matrix information of the setting surface of the mold, and the binarization matrix is the binarization mask moment in this embodiment.

Then, the element values in the area matrix information are summed to obtain the area information of the set surface of the baking mold. As shown in Fig. 4, the area matrix information, namely the value of each element in the result matrix, is summed to be 0.4+1.5+0.3+1.1+0.6+0.4=4.3 respectively, so that the area of the set surface of the baking mold is obtained.

Step 104: Determine the mold specification corresponding to the area information according to the stored corresponding relationship between the mold area and the mold specification.

Generally, the mold specifications are standard, such as 6 inches, 7 inches, and 8 inches. In this way, the corresponding relationship between the mold area and the mold specifications can be configured in advance, for example, the corresponding relationship between the upper surface area of the mold and the mold specifications can be configured according to Configure the saved correspondence to determine the mold specifications corresponding to the area information in the above process.

In some embodiments, there are different types of corresponding baking molds for different types of food, that is, the types of molds may include: sponge cake molds, toast molds, and the like. In this way, for different types of molds, you can pre-configure and save the correspondence between different mold areas and mold specifications. Therefore, when the mold type information is determined through the Mask-RCNN model, the correspondence relationship that can be saved is determined with The mold type information matches the corresponding relationship, and then, in the matched corresponding relationship, the mold specification corresponding to the area information is determined. Of course, the type information of the baking mold can also be obtained in other ways, for example, according to the instruction information input by the user, the type information of the baking mold is obtained.

It can be seen that, in the embodiment of the present disclosure, the image of the working area of the kitchen appliance is deep learning through the convolutional neural network model to obtain the specification information of the baking mold in which the food is placed in the kitchen appliance. In this way, the inside of the baking mold can be determined The specification information of the ingredients is used to further control the baking of the ingredients. The mold specifications can be identified without adding any hardware equipment, which reduces the manufacturing resources of kitchen appliances, and does not rely on the performance of the weight measurement hardware equipment, which improves the accuracy of determining mold specifications.

The mold is put in the kitchen appliance to bake or bake the food. Therefore, after determining the mold specification corresponding to the area information, it also includes: according to the mold specification of the baking mold, the baking control of the food placed in the baking mold is performed.

Optionally, when the mold type information is determined through the Mask-RCNN model, the baking process corresponding to the mold type information can be determined, and the mold specifications of the baking mold can determine the specific parameters of the baking process, such as: baking time, baking Temperature and so on, thus, by running the baking process with determined parameters, the baking control of the ingredients put into the baking mold can be realized. Of course, it is also possible to directly control the baking according to the baking time, baking temperature and other baking parameters corresponding to the mold specifications of the baking mold.

It can be seen that the specification information of the ingredients in the baking mold is determined, and the baking control of the ingredients is further carried out. It can be seen that there is no need to add any hardware equipment. In the process of identifying the types of ingredients, the specifications of the ingredients can also be determined, which reduces the manufacturing resources of kitchen electrical equipment, and does not rely on the performance of the weight measurement hardware equipment to improve the determination of the specifications of the ingredients. The accuracy of the information.

The operation procedures are assembled into specific embodiments below to illustrate the baking mold specification detection process provided by the embodiments of the present invention.

In an embodiment of the present disclosure, the corresponding relationship between the mold area and the mold specification that matches the mold type is stored in the oven.

FIG. 5 is a schematic flowchart of a method for detecting specifications of a baking mold provided by an embodiment of the present disclosure. As shown in Figure 5, the baking mold specification inspection process includes:

Step 501: Obtain an image of the working area of the oven where the baking mold is placed.

A camera is built into the oven, so that an image of the working area of the oven can be obtained through the camera.

Step 502: Train the image through the MobileNet model to obtain the grid mapping matrix corresponding to the hierarchical position of the baking mold in the kitchen appliance.

Step 503: Train the image through the Mask-RCNN model to obtain the binarized mask matrix of the set surface of the baking mold and the type information of the mold.

The sequence of step 502 and step 503 is not limited, and can be performed simultaneously or sequentially.

Step 504: Perform bitwise multiplication processing on the grid mapping matrix and the binarized mask matrix to obtain the area matrix information of the set surface of the baking mold.

Step 505: Perform a summation process on the element values in the area matrix information to obtain the area information of the set surface of the baking mold.

Step 506: Determine the corresponding relationship between the mold area and the mold specification that matches the type information of the mold, and determine the mold specification corresponding to the area information according to the matched correspondence.

It can be seen that in this embodiment, the image of the working area in the oven can be deep-learned through the convolutional neural network model to obtain the specification information of the baking mold where the ingredients are placed. In this way, the specification information of the ingredients in the baking mold can be determined, so that: The specifications of the mold can be determined without adding any hardware equipment, which reduces the manufacturing resources of the oven, and does not rely on the performance of the weight measurement hardware equipment, which improves the accuracy of determining the mold specification information.

According to the above-mentioned baking mold specification detection process, a baking mold specification detection device can be constructed.

Fig. 6 is a schematic structural diagram of a baking mold specification detection device provided by an embodiment of the present disclosure. As shown in FIG. 6, the baking mold specification detection device includes: an image acquisition module 610, a model training module 620, an area determination module 630, and a specification determination module 640.

The image acquisition module 610 is configured to acquire an image of the working area of the kitchen appliance on which the baking mold is placed.

The model training module 620 is configured to train the image through the convolutional neural network model, determine the position image information of the baking mold in the kitchen appliance, and the segmented image information of the set surface of the baking mold.

The area determination module 630 is configured to determine the area information of the set surface of the baking mold according to the position image information and the segmented image information.

The specification determining module 640 is configured to determine the mold specification corresponding to the area information according to the stored correspondence relationship between the mold area and the mold specification.

In some embodiments, the model training module 620 is specifically configured to train the image through the neural compression network model to obtain the grid mapping matrix corresponding to the hierarchical position of the baking mold in the kitchen appliance; segment the network model by example, The image is trained to obtain the binarized mask matrix of the setting surface of the baking mold.

In some embodiments, the area determining module 630 is specifically configured to perform a bitwise multiplication process on the grid mapping matrix and the binarized mask matrix to obtain the area matrix information of the setting surface of the baking mold; and the area matrix information The element values in are summed to obtain the area information of the setting surface of the baking mold.

In some embodiments, it further includes: a baking control module configured to perform baking control on the ingredients put into the baking mold according to the mold specifications of the baking mold.

The following is an example to illustrate the baking mold specification detection process performed by the baking mold specification detection device provided by the embodiment of the present disclosure.

Fig. 7 is a schematic structural diagram of a baking mold specification detection device provided by an embodiment of the present disclosure. As shown in FIG. 7, the baking mold specification detection device includes: an image acquisition module 610, a model training module 620, an area determination module 630, and a specification determination module 640, and may also include a baking control module 650.

Wherein, the image acquisition module 610 can acquire an image of the working area of the kitchen appliance on which the baking mold is placed.

In this way, the model training module 620 can train the image through the MobileNet model to obtain the grid mapping matrix corresponding to the hierarchical position of the baking mold in the kitchen appliance, and train the image through the Mask-RCNN model to obtain the baking mold Set the surface binarization mask matrix and mold type information.

Therefore, the area determination module 630 can perform a bitwise multiplication process on the grid mapping matrix and the binarization mask matrix to obtain the area matrix information of the setting surface of the baking mold, and sum the element values in the area matrix information After processing, the area information of the set surface of the baking mold is obtained.

The corresponding relationship between the mold area and the mold specifications that matches the mold type has been saved in advance. Therefore, the specification determination module 640 can determine the correspondence between the mold area and the mold specification that matches the type information of the mold, and determine the mold specification corresponding to the area information according to the matched correspondence.

Since the mold type information is determined through the Mask-RCNN model, the baking process corresponding to the mold type information can be determined, and the mold specifications of the baking mold can determine the specific parameters of the baking process, such as: baking time, baking temperature, etc. Therefore, the baking control module 650 can perform baking control on the food material put into the baking mold according to the baking process with the determined parameters.

It can be seen that, in this embodiment, after the baking mold specification detection device acquires the image of the working area in the kitchen appliance, it performs deep learning through the convolutional neural network model to obtain the specification information of the baking mold in which the food is placed in the kitchen appliance. , Can determine the specification information of the ingredients in the baking mold, and further control the baking of the ingredients. There is no need to add any hardware equipment. In the process of identifying the type of food, the specifications of the baking mold and the food can also be determined, which reduces the manufacturing resources of the kitchen electrical equipment, and does not rely on the performance of the weight measurement hardware equipment to improve the determination of the food Accuracy of specification information.

The embodiment of the present disclosure provides a device for detecting the specification of a baking mold, the structure of which is shown in FIG. 8 and includes:

A processor (processor) 100 and a memory (memory) 101 may also include a communication interface (Communication Interface) 102 and a bus 103. Among them, the processor 100, the communication interface 102, and the memory 101 can communicate with each other through the bus 103. The communication interface 102 can be used for information transmission. The processor 100 may call the logic instructions in the memory 101 to execute the method for detecting the specifications of the baking mold in the foregoing embodiment.

In addition, the above-mentioned logic instructions in the memory 101 can be implemented in the form of software functional units and when sold or used as independent products, they can be stored in a computer readable storage medium.

As a computer-readable storage medium, the memory 101 can be used to store software programs and computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes functional applications and data processing by running the program instructions/modules stored in the memory 101, that is, implements the baking mold specification detection method in the foregoing method embodiment.

The memory 101 may include a program storage area and a data storage area. The program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of a terminal device, and the like. In addition, the memory 101 may include a high-speed random access memory, and may also include a non-volatile memory.

The embodiment of the present disclosure provides a kitchen appliance, which includes the above-mentioned baking mold specification detection device.

The embodiment of the present disclosure provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are configured to execute the above-mentioned baking mold specification detection method.

The embodiments of the present disclosure provide a computer program product, the computer program product includes a computer program stored on a computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer program The computer executes the above-mentioned baking mold specification detection method.

The aforementioned computer-readable storage medium may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.

The technical solutions of the embodiments of the present disclosure can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which can be a personal computer, a server, or a network). Equipment, etc.) execute all or part of the steps of the method described in the embodiments of the present disclosure. The aforementioned storage medium may be a non-transitory storage medium, including: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks, etc. A medium that can store program codes, or it can be a transient storage medium.

The above description and drawings fully illustrate the embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The examples only represent possible changes. Unless explicitly required, the individual components and functions are optional, and the order of operations can be changed. Parts and features of some embodiments may be included in or substituted for parts and features of other embodiments. The scope of the embodiments of the present disclosure includes the entire scope of the claims and all available equivalents of the claims. When used in this application, although the terms "first", "second", etc. may be used in this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, without changing the meaning of the description, the first element can be called the second element, and likewise, the second element can be called the first element, as long as all occurrences of the "first element" are renamed consistently and all occurrences "Second component" can be renamed consistently. The first element and the second element are both elements, but they may not be the same element. Moreover, the terms used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates, the singular forms "a" (a), "an" (an) and "the" (the) are intended to also include plural forms . Similarly, the term "and/or" as used in this application refers to any and all possible combinations of one or more of the associated lists. In addition, when used in this application, the term "comprise" (comprise) and its variants "comprises" and/or including (comprising) and the like refer to the stated features, wholes, steps, operations, elements, and/or The existence of components does not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components, and/or groups of these. If there are no more restrictions, the element defined by the sentence "including one..." does not exclude the existence of other same elements in the process, method, or device that includes the element. In this article, each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method parts disclosed in the embodiments, then the related parts can be referred to the description of the method parts.

Those skilled in the art may realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to realize the described functions, but such realization should not be considered as going beyond the scope of the embodiments of the present disclosure. The technicians can clearly understand that, for the convenience and conciseness of the description, the specific working process of the system, device, and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units may only be a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined. Or it can be integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.

The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions, and operations of the system, method, and computer program product according to the embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of the code, and the module, program segment, or part of the code contains one or more functions for realizing the specified logic function. Executable instructions. In some alternative implementations, the functions marked in the block may also occur in a different order than the order marked in the drawings. For example, two consecutive blocks can actually be executed in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the drawings, the operations or steps corresponding to different blocks can also occur in a different order than disclosed in the description, and sometimes there is no specific operation or step between different operations or steps. order. For example, two consecutive operations or steps can actually be performed substantially in parallel, and they can sometimes be performed in the reverse order, depending on the functions involved. Each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by dedicated hardware Realized in combination with computer instructions.

Claims

A method for detecting the specification of a baking mold, which is characterized in that it comprises:

Obtain an image of the working area of the kitchen electrical equipment with the baking mold;

Training the image through a convolutional neural network model to determine the image information of the position of the baking mold in the kitchen appliance and the segmented image information of the setting surface of the baking mold;

Determining the area information of the set surface of the baking mold according to the position image information and the segmented image information;

According to the stored corresponding relationship between the mold area and the mold specification, the mold specification corresponding to the area information is determined.
The method according to claim 1, wherein said determining the image information of the position of the baking mold in the kitchen appliance, and the segmented image information of the setting surface of the baking mold comprises:

Training the image through a neural compression network model to obtain a grid mapping matrix corresponding to the hierarchical position of the baking mold in the kitchen appliance;

Through the instance segmentation network model, the image is trained to obtain the binarized mask matrix of the setting surface of the baking mold.
The method according to claim 2, wherein the determining the area information of the setting surface of the baking mold comprises:

Performing bitwise multiplication processing on the grid mapping matrix and the binarized mask matrix to obtain area matrix information of the setting surface of the baking mold;

A summation process is performed on the element values in the area matrix information to obtain the area information of the set surface of the baking mold.
The method according to claim 1, wherein after the determining the mold specifications corresponding to the area information, the method further comprises:

According to the mold specifications of the baking mold, baking control is performed on the food materials put into the baking mold.
A device for detecting the specification of a baking mold, which is characterized in that it comprises:

The image acquisition module is configured to acquire an image of the working area of the kitchen appliance on which the baking mold is placed;

The model training module is configured to train the image through a convolutional neural network model, determine the image information of the position of the baking mold in the kitchen appliance, and divide the set surface of the baking mold Image information

An area determination module configured to determine area information of a set surface of the baking mold according to the position image information and the segmented image information;

The specification determination module is configured to determine the mold specification corresponding to the area information according to the stored correspondence relationship between the mold area and the mold specification.
The device of claim 5, wherein:

The model training module is specifically configured to train the image through a neural compression network model to obtain a grid mapping matrix corresponding to the hierarchical position of the baking mold in the kitchen appliance; segment the network model through examples , Training the image to obtain a binarized mask matrix of the setting surface of the baking mold.
The device according to claim 6, wherein:

The area determination module is specifically configured to perform a bitwise multiplication process on the grid mapping matrix and the binarization mask matrix to obtain the area matrix information of the setting surface of the baking mold; The element values in the area matrix information are summed to obtain the area information of the setting surface of the baking mold.
The device according to claim 5, further comprising:

The baking control module is configured to perform baking control on the ingredients put into the baking mold according to the mold specifications of the baking mold.
A device for detecting the specification of a baking mold, comprising a processor and a memory storing program instructions, wherein the processor is configured to execute the program instructions as described in any one of claims 1 to 4 when the program instructions are executed. The method described.
A kitchen appliance, characterized by comprising the device according to claim 5 or 9.