CN109064464B - Method and device for detecting burrs of battery pole piece - Google Patents

Method and device for detecting burrs of battery pole piece Download PDF

Info

Publication number
CN109064464B
CN109064464B CN201810906761.1A CN201810906761A CN109064464B CN 109064464 B CN109064464 B CN 109064464B CN 201810906761 A CN201810906761 A CN 201810906761A CN 109064464 B CN109064464 B CN 109064464B
Authority
CN
China
Prior art keywords
pole piece
battery pole
image
area
detected
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810906761.1A
Other languages
Chinese (zh)
Other versions
CN109064464A (en
Inventor
文亚伟
冷家冰
刘明浩
郭江亮
李旭
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Baidu Netcom Science and Technology Co Ltd
Original Assignee
Beijing Baidu Netcom Science and Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Baidu Netcom Science and Technology Co Ltd filed Critical Beijing Baidu Netcom Science and Technology Co Ltd
Priority to CN201810906761.1A priority Critical patent/CN109064464B/en
Publication of CN109064464A publication Critical patent/CN109064464A/en
Application granted granted Critical
Publication of CN109064464B publication Critical patent/CN109064464B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Abstract

The embodiment of the application discloses a method and a device for detecting burrs of a battery pole piece. One embodiment of the method comprises: acquiring an image shot by a battery pole piece as an image to be detected; and in response to the fact that the image to be detected comprises the battery pole piece burr area, determining the category of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected. According to the embodiment, the classification of the battery pole piece burrs is determined through the battery pole piece burr area in the image to be detected.

Description

Method and device for detecting burrs of battery pole piece
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for detecting burrs of a battery pole piece.
Background
In the battery industry, the quality of a battery pole piece is closely related to the quality of a battery. Therefore, in the production process of the battery pole piece, the surface of the battery pole piece is often required to be detected, and whether burrs exist is checked. At present, the detection of the surface of a battery pole piece is mainly realized by the following two ways. Firstly, the surface of the battery pole piece is shot, then, the shot picture is analyzed by technicians, and whether burrs exist on the surface of the battery pole piece is further determined. Secondly, storing the previous detection results of technicians, and then analyzing the shot images according to the stored detection results to further determine whether burrs exist on the surface of the battery pole piece.
Disclosure of Invention
The embodiment of the application provides a method and a device for detecting burrs of a battery pole piece.
In a first aspect, an embodiment of the present application provides a method for detecting burrs of a battery pole piece, where the method includes: acquiring an image shot by a battery pole piece as an image to be detected; and in response to the fact that the image to be detected comprises the battery pole piece burr area, determining the category of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected.
In some embodiments, determining the category of the burr indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected includes: inputting an image to be detected into a pre-trained burr detection model to obtain the class information of burrs indicated by a battery pole piece burr region and the position information of the position of the battery pole piece burr region in the image to be detected, wherein the burr detection model is used for representing the corresponding relation between the class of the burrs indicated by the image to be detected and the battery pole piece burr region and the position of the battery pole piece burr region in the image to be detected.
In some embodiments, the spur detection model is trained by: obtaining a sample set, wherein the sample comprises a sample image and sample marking information, the sample image comprises a battery pole piece burr area, and the sample marking information is used for indicating the type of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the sample image; and taking the sample image of the sample in the sample set as the input of the initial model, taking the sample marking information corresponding to the input sample image as the expected output of the initial model, and training to obtain the burr detection model.
In some embodiments, the initial model includes a candidate area network and a convolutional neural network.
In some embodiments, the method further comprises: category information of the category and location information of the location are stored and/or pushed.
In a second aspect, an embodiment of the present application provides an apparatus for detecting battery pole piece burrs, the apparatus including: an acquisition unit configured to acquire an image captured for a battery pole piece as an image to be detected; the determination unit is configured to respond to the fact that the image to be detected comprises the battery pole piece burr area, and determine the category of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected.
In some embodiments, the determining unit is further configured to: inputting an image to be detected into a pre-trained burr detection model to obtain category information of burrs indicated by a battery pole piece burr area and position information of positions of the battery pole piece burr area in the image to be detected, wherein the burr detection model is used for representing the corresponding relation between the category of the burrs indicated by the image to be detected and the battery pole piece burr area and the positions of the battery pole piece burr area in the image to be detected.
In some embodiments, the spur detection model is trained by: obtaining a sample set, wherein the sample comprises a sample image and sample marking information, the sample image comprises a battery pole piece burr area, and the sample marking information is used for indicating the type of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the sample image; and taking the sample image of the sample in the sample set as the input of the initial model, taking the sample marking information corresponding to the input sample image as the expected output of the initial model, and training to obtain the burr detection model. .
In some embodiments, the initial model includes a candidate area network and a convolutional neural network.
In some embodiments, the apparatus further comprises: the storage unit and/or the pushing unit are/is configured to store and/or push category information of categories and position information of positions.
In a third aspect, an embodiment of the present application provides a server, where the server includes: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any implementation of the first aspect.
In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, which when executed by a processor implements the method described in any implementation manner of the first aspect.
The method and the device for detecting the burrs of the battery pole piece can acquire the image shot by the battery pole piece. If the shot image is further determined to comprise a battery pole piece burr area, the category of burrs of the battery pole piece can be further determined through the battery pole piece burr area.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for detecting battery pole piece burring according to the present application;
FIG. 3 is a schematic diagram of one application scenario of a method for detecting battery pole piece burrs according to the present application;
FIG. 4 is a flow chart of yet another embodiment of a method for detecting battery pole piece burring according to the present application;
FIG. 5 is a schematic diagram of the structure of one embodiment of an apparatus for detecting battery pole piece flash in accordance with the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing a server according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 illustrates an exemplary architecture 100 to which the method for detecting battery pole piece burring or the apparatus for detecting battery pole piece burring of the present application may be applied.
As shown in fig. 1, system architecture 100 may include server 101, database server 102, network 103, and terminal devices 104, 105. The network 103 is a medium used to provide a communication link between the server device 101 and the terminal devices 104, 105, or a medium used to provide a communication link between the server device 101 and the database server 102. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The terminal devices 104, 105 interact with the server 101 through the network 103 to receive or transmit information or the like. Various communication client applications, such as an information interaction application, an image detection application, and the like, may be installed on the terminal devices 104 and 105.
The terminal devices 104, 105 may be hardware or software. When the terminal devices 104, 105 are hardware, they may be various electronic devices having a display screen and supporting an image display function, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like. When the terminal devices 104, 105 are software, they can be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 101 may be a server that provides various services. For example, the server 101 may acquire an image from the database server 102 or an image capture device (not shown in the figure), process the acquired image, and generate a processing result. For example, the server 101 may store the generated processing result in the database server 102, or push the generated processing result to the terminal apparatuses 104 and 105.
The server 101 and the database server 102 may be hardware or software. When the hardware is used, the hardware can be implemented as a distributed server cluster consisting of a plurality of servers, or can be implemented as a single server. When software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the server 101 may directly obtain an image from an image capture device (not shown in the figure), or may store the generated processing result locally. In this case, the database server 102 may not be present, or the terminal apparatuses 104 and 105 may not be present.
It should be noted that the method for detecting battery pole piece burrs provided in the embodiments of the present application is generally performed by the server 101, and accordingly, the apparatus for detecting battery pole piece burrs is generally disposed in the server 101.
It should be understood that the number of servers, networks, and terminal devices in fig. 1 is merely illustrative. There may be any number of servers, networks, and terminal devices, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method for detecting battery pole piece flash in accordance with the present application is shown. The method for detecting the burrs of the battery pole piece comprises the following steps:
step 201, acquiring an image shot by the battery pole piece as an image to be detected.
In the present embodiment, an execution main body (such as the server 101 shown in fig. 1) of the method for detecting the battery pole piece burr may acquire an image captured for the battery pole piece by various methods, and further, the acquired image is used as an image to be detected.
As an example, the image capturing apparatus may capture the surface of the battery pole piece, and then the execution body may acquire the captured image from the image capturing apparatus as an image to be detected. It should be noted that the image capturing device may be any electronic device having an image capturing function.
As an example, a database server (e.g., database server 102) may have images captured in advance for the battery pole pieces. Further, the execution subject may acquire these images from the database server as the images to be detected.
Step 202, in response to determining that the image to be detected includes a battery pole piece burr area, determining the category of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected.
In this embodiment, the battery pole piece burr area is used to represent the existence of burrs on the surface of the battery pole piece. In practice, if burrs exist on the surface of the battery pole piece and the image acquisition equipment shoots the burrs on the surface of the battery pole piece, the area corresponding to the burrs on the surface of the battery pole piece in the shot image is the battery pole piece burr area.
In this embodiment, in response to determining that the image to be detected includes the battery pole piece burr region, the execution main body may further determine the category of the battery pole piece burr and the position of the battery pole piece burr region in the image to be detected.
As an example, in practice, a technician may process a large number of images including a battery pole piece burr region, and then obtain a correspondence table between a feature vector representing the battery pole piece burr region and a category of battery pole piece burrs through statistics. And the characteristic vector of the battery pole piece burr area is correlated with the category of the battery pole piece burrs. Here, the category of the battery pole piece burr may be represented by any one of: numbers, letters, images, etc.
The execution body may extract several regions from the image to be detected, and the regions may overlap. For example, several regions may be extracted from the image to be detected by a Selective Search algorithm, an Edge Boxes algorithm, or the like. Then, the execution body can perform feature extraction on the regions respectively and generate corresponding feature vectors. Furthermore, the execution body may match the feature vectors of the respective regions to the correspondence table. If there is a feature vector whose similarity satisfies a preset target (for example, the similarity is greater than 90%), the execution main body may determine that the image to be detected includes a battery pole piece burr region. That is to say, for an area in the area extracted from the image to be detected, if the similarity between the area and the feature vector in the corresponding relation table meets the feature vector of the preset target, the execution main body can determine the area as a battery pole piece burr area; it can be understood that the execution body can determine the position of the region as the position of the battery pole piece burr region in the image to be detected. Further, the execution main body may determine the category of the burr of the battery pole piece corresponding to the feature vector satisfying the preset target as the category of the burr indicated by the battery pole piece burr region.
In some optional implementations of this embodiment, the method further includes: category information of the category and location information of the location are stored and/or pushed.
In these implementations, after determining the category of the battery pole piece burr and the position of the battery pole piece burr area in the image to be detected, the execution main body may store the obtained category information and position information of the position. For example, it may be stored locally in the execution entity or in a communicatively connected database server (e.g., database server 102).
In these implementations, the execution body may further push the obtained category information of the category and the position information of the position to the terminal device (e.g., terminal devices 104 and 105) connected to the communication.
The type information may be any information describing the type of the battery pole piece burr, for example, "type 1", "type a", and the like. The position information can be a marking frame representing the position of the battery pole piece burr area in the image to be detected, and can also be the coordinate of any vertex of the marking frame.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for detecting battery pole piece burrs according to the present embodiment. In the application scenario of fig. 3, when an image 301 captured for the battery pole piece M is acquired, the server 300 may extract regions 302, 303, 304, 305, and 306 from the image 301. Then, the server 300 may perform feature extraction on the regions 302, 303, 304, 305, and 306 respectively to obtain corresponding feature vectors.
Technicians can select a large number of battery pole pieces with burrs on the surfaces, predetermine the category of the burrs of the battery pole pieces and acquire images of the burrs of the battery pole pieces. And further, extracting the characteristics of the burr area of the battery pole piece included in the acquired image to obtain a corresponding characteristic vector. Furthermore, a technician can work out a corresponding relation table between the characteristic vector representing the battery pole piece burr area and the category of the battery pole piece burrs according to the statistical result.
Here, the region 306 is taken as an example. The server 300 extracts features from the region 306, resulting in feature vectors a1, a2, and A3. And then matching the extracted feature vectors A1, A2 and A3 in the corresponding relation. If there are eigenvectors B1, B2, and B3 whose similarity satisfies a preset target, the server 300 may determine the region 306 as a battery pole piece burr region. Further, the server 300 may determine the location where the region 306 is located as the location of the battery pole piece burr region, and determine the category (i.e., "category 1") in the correspondence table corresponding to the feature vectors B1, B2, and B3 as the category of the burr indicated by the region 306.
The method provided by the above embodiment of the present application first extracts a certain number of regions from the image to be detected. Then, for the regions in the extracted regions, the following steps are respectively performed: extracting the features of the region to obtain a feature vector; matching the generated characteristic vectors to a corresponding relation table obtained in advance; if the similarity meets the characteristic vector of the preset target, determining the area as a battery pole piece burr area, determining the position of the area as the position of the battery pole piece burr area, and determining the category corresponding to the characteristic vector meeting the preset target as the category of the burr indicated by the area.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for detecting battery pole piece flash is shown. The process 400 of the method for detecting battery pole piece burrs comprises the following steps:
step 401, acquiring an image shot for a battery pole piece as an image to be detected.
The detailed processing of step 401 and the technical effects thereof may refer to step 201 in the embodiment corresponding to fig. 2, and are not described herein again.
Step 402, responding to the situation that the image to be detected comprises the battery pole piece burr area, inputting the image to be detected into a burr detection model trained in advance, and obtaining the class information of burrs indicated by the battery pole piece burr area and the position information of the position of the battery pole piece burr area in the image to be detected.
In this embodiment, in response to determining that the image to be detected includes the battery pole piece burr region, an execution main body (for example, the server 101 shown in fig. 1) of the method for detecting battery pole piece burr may input the image to be detected to a pre-trained burr detection model, so as to obtain category information of a category of burr indicated by the battery pole piece burr region and position information of a position of the battery pole piece burr region in the image to be detected. The burr detection model is used for representing the category of burrs indicated by the image to be detected and the burr area of the battery pole piece and the corresponding relation between the positions of the burr area of the battery pole piece in the image to be detected.
It should be noted that the execution main body of the training burr detection model and the execution main body of the method for detecting the burr of the battery pole piece may be the same or different.
In some optional implementations of this embodiment, the spur detection model may be trained by:
in a first step, a sample set is obtained.
The sample comprises a sample image and sample marking information, the sample image comprises a battery pole piece burr area, and the sample marking information is used for indicating the type of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the sample image
An executive who trains the glitch detection model may obtain a sample set by various methods. For example, when the training samples are stored locally, the performing agent that trains the spur detection model may directly obtain the samples from locally as a sample set. For example, when training samples are stored at a communicatively connected database server (e.g., database server 102), the executing agent of the training spike detection model may also obtain the samples from the communicatively connected database server as a sample set.
And secondly, taking the sample image of the sample in the sample set as the input of the initial model, taking the sample marking information corresponding to the input sample image as the expected output of the initial model, and training to obtain the burr detection model.
Further, an executive who trains the spur detection model may select samples from a sample set. And then, taking the sample image of the selected sample as the input of the initial model, taking the sample marking information corresponding to the input sample image as the expected output of the initial model, and training by a machine learning method to obtain the burr detection model.
In these implementations, the initial model described above may include a candidate area network and a convolutional neural network.
Specifically, the training step is as follows.
In step S1, the executing entity training the spike detection model may input the sample image into the convolutional neural network to obtain a corresponding feature map. The convolutional neural network may include various structural layers with special functions, such as convolutional layers, pooling layers, full-link layers, and the like. The convolutional layer may extract features (e.g., texture features, edge features) of the sample image. The pooling layer can perform dimension reduction on the features extracted from the convolutional layer, and further retain main features. The full connection layer can synthesize the extracted main features to obtain a feature map.
Step S2, after obtaining the feature map, the executing agent of the training spike detection model may input the feature map into the candidate area network, and then slide the sliding window in the feature map, thereby extracting a certain number of candidate windows in the feature map. In practice, the relevant parameters may be preset to determine n candidate regions (e.g. rectangular regions) in the sample image according to the positions of the candidate windows in the feature map, where n is a positive integer. Thus, each time the sliding window is slid, n candidate regions can be determined in the sample image. In practice, the sliding step of the sliding window can be set according to actual needs.
In step S3, after determining the candidate region in the sample map, the executive body of the training spike detection model may map the extracted candidate region into the feature map. Therefore, the executive body of the training burr detection model can classify the object (such as battery pole piece burrs) indicated by the candidate region according to the corresponding features of the candidate region in the feature map. Then, the classification result, the position of the candidate region in the sample image and the sample labeling information are compared. Furthermore, a battery pole piece burr area can be determined from the at least one candidate area to serve as a target candidate area.
In step S4, the executive agent training the burr detection model may determine, according to the comparison result, an error between the category of the burr indicated by the target candidate region and the category of the burr indicated by the sample labeling information, and an error between the position of the target candidate region in the sample image and the position of the battery pole piece burr region indicated by the sample labeling information in the sample image. Further, an executive who trains the spur detection model may determine the sum of the two errors as a total error. If the total error is less than or equal to the preset error, it can be determined that the training of the burr detection model is completed.
If the total error is greater than the preset error, the executive body of the training burr detection model can adjust the relevant parameters of the initial model. And then taking the adjusted model as an initial model, and continuing to execute the training steps until a condition for finishing training is met. Wherein the condition for ending the training comprises at least one of the following: the training time reaches the preset time length; the training times reach the preset times; the total error is less than the preset error.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the method for detecting battery pole piece glitch in this embodiment highlights the step of training the glitch detection model. Therefore, the scheme described in this embodiment can input the image to be detected to the pre-trained burr detection model, so as to determine the category of burrs indicated by the battery pole piece burr region and the position of the battery pole piece burr region in the image to be detected.
With further reference to fig. 5, as an implementation of the methods shown in the above figures, the present application provides an embodiment of an apparatus for detecting battery pole piece burrs, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied to various electronic devices.
As shown in fig. 5, the apparatus 500 for detecting battery pole piece burrs provided by the present embodiment includes an acquisition unit 501 and a determination unit 502. The acquiring unit 501 is configured to acquire an image shot for a battery pole piece as an image to be detected; a determining unit 502 configured to determine a category of a burr indicated by the battery pole piece burr region and a position of the battery pole piece burr region in the image to be detected in response to determining that the image to be detected includes the battery pole piece burr region.
In the present embodiment, in the apparatus 500 for detecting battery pole piece burr: the specific processing of the obtaining unit 501 and the determining unit 502 and the technical effects thereof can refer to the related descriptions of step 201 and step 202 in the corresponding embodiment of fig. 2, which are not repeated herein.
In some optional implementations of this embodiment, the determining unit 501 may be further configured to: inputting an image to be detected into a pre-trained burr detection model to obtain category information of burrs indicated by a battery pole piece burr area and position information of positions of the battery pole piece burr area in the image to be detected, wherein the burr detection model is used for representing the corresponding relation between the category of the burrs indicated by the image to be detected and the battery pole piece burr area and the positions of the battery pole piece burr area in the image to be detected.
In some optional implementations of this embodiment, the spur detection model is trained by: obtaining a sample set, wherein the sample comprises a sample image and sample marking information, the sample image comprises a battery pole piece burr area, and the sample marking information is used for indicating the type of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the sample image; and taking the sample image of the sample in the sample set as the input of the initial model, taking the sample marking information corresponding to the input sample image as the expected output of the initial model, and training to obtain the burr detection model.
In some optional implementations of the present embodiment, the initial model includes a candidate area network and a convolutional neural network.
In some optional implementations of the present embodiment, the apparatus 500 may further include a storage unit (not shown in the figure) and/or a pushing unit (not shown in the figure). Wherein the storage unit may be configured to: storing category information of the category and location information of the location; the pushing unit may be configured to: category information of the push category and location information of the location.
The apparatus provided in the above embodiment of the present application first acquires an image captured for a battery pole piece through the acquisition unit 501. If the determination unit 502 determines that the captured image includes a battery pole piece burr area, the type of burrs indicated by the battery pole piece burr area can be further determined, so as to determine the type of burrs existing in the battery pole piece.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a server according to embodiments of the present application. The server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit and a determination unit. Here, the names of the units do not constitute a limitation to the unit itself in some cases, and for example, the acquisition unit may also be described as a "unit that acquires an image captured for a battery pole piece as an image to be detected".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the server described in the above embodiments; or may exist separately and not be assembled into the server. The computer readable medium carries one or more programs which, when executed by the server, cause the server to: acquiring an image shot by a battery pole piece as an image to be detected; and in response to the fact that the image to be detected comprises the battery pole piece burr area, determining the category of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims (12)

1. A method for detecting battery pole piece flash, comprising:
acquiring an image shot by a battery pole piece as an image to be detected;
extracting a plurality of areas from the image to be detected, and performing feature extraction on the areas to generate feature vectors corresponding to the areas;
determining the similarity between the feature vectors corresponding to the regions and the feature vectors in a preset corresponding relation table;
determining the area with the similarity meeting a preset target as a battery pole piece burr area;
in response to the fact that the image to be detected comprises the battery pole piece burr area, determining the type of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected; the feature vector of the battery pole piece burr area is correlated with the category of burrs indicated by the battery pole piece burr area.
2. The method of claim 1, wherein the determining the class of the burr indicated by the battery pole piece burr region and the position of the battery pole piece burr region in the image to be detected comprises:
inputting the image to be detected into a pre-trained burr detection model to obtain the category information of burrs indicated by a battery pole piece burr area and the position information of the position of the battery pole piece burr area in the image to be detected, wherein the burr detection model is used for representing the corresponding relation between the category of the burrs indicated by the image to be detected and the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected.
3. The method of claim 2, wherein the spur detection model is trained by:
obtaining a sample set, wherein the sample comprises a sample image and sample marking information, the sample image comprises a battery pole piece burr area, and the sample marking information is used for indicating the type of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the sample image;
and taking the sample image of the sample in the sample set as the input of the initial model, taking the sample marking information corresponding to the input sample image as the expected output of the initial model, and training to obtain the burr detection model.
4. The method of claim 3, wherein the initial model comprises a candidate area network and a convolutional neural network.
5. The method according to any one of claims 1-4, wherein the method further comprises:
and storing and/or pushing the category information of the category and the position information of the position.
6. An apparatus for detecting battery pole piece flash, comprising:
an acquisition unit configured to acquire an image captured for a battery pole piece as an image to be detected;
the determining unit is configured to extract a plurality of areas from the image to be detected, perform feature extraction on the areas and generate feature vectors corresponding to the areas; determining the similarity between the feature vectors corresponding to the regions and the feature vectors in a preset corresponding relation table; determining the area with the similarity meeting a preset target as a battery pole piece burr area; in response to the fact that the image to be detected comprises the battery pole piece burr area, determining the type of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected; the feature vector of the battery pole piece burr area is correlated with the category of burrs indicated by the battery pole piece burr area.
7. The apparatus of claim 6, wherein the determining unit is further configured to:
inputting the image to be detected into a pre-trained burr detection model to obtain the category information of burrs indicated by a battery pole piece burr area and the position information of the position of the battery pole piece burr area in the image to be detected, wherein the burr detection model is used for representing the corresponding relation between the category of the burrs indicated by the image to be detected and the battery pole piece burr area and the position of the battery pole piece burr area in the image to be detected.
8. The apparatus of claim 7, wherein the spur detection model is trained by:
obtaining a sample set, wherein the sample comprises a sample image and sample marking information, the sample image comprises a battery pole piece burr area, and the sample marking information is used for indicating the type of burrs indicated by the battery pole piece burr area and the position of the battery pole piece burr area in the sample image;
and taking the sample image of the sample in the sample set as the input of the initial model, taking the sample marking information corresponding to the input sample image as the expected output of the initial model, and training to obtain the burr detection model.
9. The apparatus of claim 8, wherein the initial model comprises a candidate area network and a convolutional neural network.
10. The apparatus of any of claims 6-9, wherein the apparatus further comprises:
a storage unit and/or a push unit configured to store and/or push category information of the category and location information of the location.
11. A server, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-5.
CN201810906761.1A 2018-08-10 2018-08-10 Method and device for detecting burrs of battery pole piece Active CN109064464B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810906761.1A CN109064464B (en) 2018-08-10 2018-08-10 Method and device for detecting burrs of battery pole piece

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810906761.1A CN109064464B (en) 2018-08-10 2018-08-10 Method and device for detecting burrs of battery pole piece

Publications (2)

Publication Number Publication Date
CN109064464A CN109064464A (en) 2018-12-21
CN109064464B true CN109064464B (en) 2022-02-11

Family

ID=64683305

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810906761.1A Active CN109064464B (en) 2018-08-10 2018-08-10 Method and device for detecting burrs of battery pole piece

Country Status (1)

Country Link
CN (1) CN109064464B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109886918A (en) * 2019-01-14 2019-06-14 广州量子激光智能装备有限公司 Wire drawing burr detection method, electronic equipment and the storage medium of lithium battery pole slice
CN109754401B (en) * 2019-03-15 2020-09-08 苏州杰锐思智能科技股份有限公司 Online measuring device and method for burrs at edge of lithium battery pole piece
CN110276156B (en) * 2019-06-28 2022-05-03 蜂巢能源科技有限公司 Method and device for detecting moisture content of negative plate

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202824250U (en) * 2012-09-26 2013-03-27 万向电动汽车有限公司 Automatic burr detection device for battery pole piece
CN107133629A (en) * 2016-02-29 2017-09-05 百度在线网络技术（北京）有限公司 Picture classification method, device and mobile terminal
CN107742128A (en) * 2017-10-20 2018-02-27 百度在线网络技术（北京）有限公司 Method and apparatus for output information
CN108229364A (en) * 2017-12-28 2018-06-29 百度在线网络技术（北京）有限公司 Contour of building generation method, device, computer equipment and storage medium
CN108230317A (en) * 2018-01-09 2018-06-29 北京百度网讯科技有限公司 Steel plate defect detection sorting technique, device, equipment and computer-readable medium

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5544256A (en) * 1993-10-22 1996-08-06 International Business Machines Corporation Automated defect classification system
TWI797699B (en) * 2015-12-22 2023-04-01 以色列商應用材料以色列公司 Method of deep learning - based examination of a semiconductor specimen and system thereof
CN107966447B (en) * 2017-11-14 2019-12-17 浙江大学 workpiece surface defect detection method based on convolutional neural network
CN108132054A (en) * 2017-12-20 2018-06-08 百度在线网络技术（北京）有限公司 For generating the method and apparatus of information

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202824250U (en) * 2012-09-26 2013-03-27 万向电动汽车有限公司 Automatic burr detection device for battery pole piece
CN107133629A (en) * 2016-02-29 2017-09-05 百度在线网络技术（北京）有限公司 Picture classification method, device and mobile terminal
CN107742128A (en) * 2017-10-20 2018-02-27 百度在线网络技术（北京）有限公司 Method and apparatus for output information
CN108229364A (en) * 2017-12-28 2018-06-29 百度在线网络技术（北京）有限公司 Contour of building generation method, device, computer equipment and storage medium
CN108230317A (en) * 2018-01-09 2018-06-29 北京百度网讯科技有限公司 Steel plate defect detection sorting technique, device, equipment and computer-readable medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于机器视觉的加热片表面缺陷检测技术研究;严梁;《中国优秀硕士学位论文全文数据库 信息科技辑》;20180215;摘要、第四章、第五章 *

Also Published As

Publication number Publication date
CN109064464A (en) 2018-12-21

Similar Documents

Publication Publication Date Title
CN108830235B (en) Method and apparatus for generating information
CN108509915B (en) Method and device for generating face recognition model
US10762387B2 (en) Method and apparatus for processing image
US11244435B2 (en) Method and apparatus for generating vehicle damage information
US11392792B2 (en) Method and apparatus for generating vehicle damage information
CN108280477B (en) Method and apparatus for clustering images
CN108960090B (en) Video image processing method and device, computer readable medium and electronic equipment
CN109034069B (en) Method and apparatus for generating information
CN107622240B (en) Face detection method and device
CN109376267B (en) Method and apparatus for generating a model
CN109447156B (en) Method and apparatus for generating a model
KR102002024B1 (en) Method for processing labeling of object and object management server
CN107507153B (en) Image denoising method and device
CN110941978B (en) Face clustering method and device for unidentified personnel and storage medium
CN109064464B (en) Method and device for detecting burrs of battery pole piece
CN109214501B (en) Method and apparatus for identifying information
CN108509921B (en) Method and apparatus for generating information
CN108510084B (en) Method and apparatus for generating information
CN110070076B (en) Method and device for selecting training samples
CN112949767A (en) Sample image increment, image detection model training and image detection method
CN109345460B (en) Method and apparatus for rectifying image
CN108615006B (en) Method and apparatus for outputting information
CN112766284A (en) Image recognition method and device, storage medium and electronic equipment
CN108399401B (en) Method and device for detecting face image
CN110110666A (en) Object detection method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant