CN116012330B - Pole piece defect detection method, device, equipment and computer storage medium - Google Patents
Pole piece defect detection method, device, equipment and computer storage medium Download PDFInfo
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- 230000007547 defect Effects 0.000 title claims abstract description 113
- 238000001514 detection method Methods 0.000 title claims abstract description 38
- 238000003860 storage Methods 0.000 title claims abstract description 17
- 229910052751 metal Inorganic materials 0.000 claims abstract description 15
- 239000002184 metal Substances 0.000 claims abstract description 15
- 238000009795 derivation Methods 0.000 claims abstract description 12
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- 230000008859 change Effects 0.000 description 12
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 6
- 230000002950 deficient Effects 0.000 description 6
- 229910052744 lithium Inorganic materials 0.000 description 6
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Abstract
The application discloses a defect detection method, a device, equipment and a computer storage medium for a pole piece, wherein the defect detection method for the pole piece comprises the following steps: acquiring an image of a pole piece to be detected; carrying out gray processing on the image to obtain a target image; performing horizontal gray level projection processing on the target image to obtain horizontal gray level of the target image; performing second-order derivation on the horizontal gray level of the target image to obtain a gray level difference curve graph of the target image; determining a region to be detected of the target image by using the gray level difference curve graph; judging defects of the pole piece to be detected by utilizing at least one parameter of the maximum gray value, the minimum gray value and the standard gray difference of the pixel units in the area to be detected, wherein the defects comprise at least one of dark mark defects, bright mark defects and metal leakage defects, and the gray difference between the defect area and the non-defect area in the target image can be highlighted by conducting second-order derivation on the target image, so that the accuracy of detecting the defects of the pole piece to be detected is improved.
Description
Technical Field
The present application relates to the field of defect detection technologies for pole pieces, and in particular, to a method, an apparatus, a device, and a computer storage medium for detecting a defect of a pole piece.
Background
In the lithium battery production process, the quality of the pole piece determines the safety and the endurance of the lithium battery, for example, when the anode piece has a metal leakage defect, the cathode and the anode of the lithium battery are easily shorted, and the safety of the lithium battery in use is affected, so that the pole piece is subjected to important steps in the lithium battery production process when the defect detection is carried out.
At present, a gray threshold method and an AI detection technology are mainly adopted to detect defects of a pole piece of a lithium battery, wherein: (1) The gray threshold method is to compare the coating gray value of the pole piece with the gray threshold value to determine a defect area, but the method can not stably detect the defect of the pole piece and has low detection accuracy; (2) The AI detection technique often adopts a high-resolution camera to detect the defects of the pole piece, but the high-resolution camera has high cost, and can not detect the pole piece in real time, thereby influencing the detection efficiency of the pole piece.
Disclosure of Invention
Based on the above, the application aims to provide a defect detection method, device, equipment and computer storage medium for pole pieces, which have the advantage of high detection accuracy.
The technical scheme adopted by the application comprises the following specific contents:
a defect detection method of a pole piece, comprising:
s1: acquiring an image of a pole piece to be detected;
s2: carrying out gray processing on the image to obtain a target image;
s3: performing horizontal gray level projection processing on the target image to obtain horizontal gray level of the target image;
s4: performing second-order derivation on the horizontal gray level of the target image to obtain a gray level difference curve graph of the target image;
s5: determining a region to be detected of the target image by using a gray scale difference graph, including S51: acquiring the gray level differenceMaximum and minimum values of the graph; s52: determining the number of lines i of the maximum value and the minimum value of the gray scale difference curve graph in the target image respectively max And i min The method comprises the steps of carrying out a first treatment on the surface of the S53: by using the ith in the target image max Line and i min The pixel units between the two pixel units form the region to be detected;
s6: judging the defects of the pole piece to be detected by utilizing at least one parameter of the maximum gray value, the minimum gray value and the standard gray difference of the pixel units in the area to be detected, wherein the defects comprise at least one of dark mark defects, bright mark defects and metal leakage defects, and the method comprises the following steps:
acquiring a gray maximum value B of a pixel unit in the region to be detected and a gray average value M of the pixel unit in the region to be detected mean When the maximum gray value B of the pixel units in the region to be detected is greater than the average gray value M of the pixel units in the region to be detected mean Judging that the pole piece to be detected has a bright mark defect;
or acquiring the minimum gray value C of the pixel units in the to-be-detected area, and when the minimum gray value C of the pixel units in the to-be-detected area is smaller than the average gray value M of the pixel units in the to-be-detected area mean Judging that the pole piece to be detected has a dark mark defect;
or, acquiring the gray standard deviation D of the pixel units in the region to be detected, and when the gray maximum value B of the pixel units in the region to be detected is larger than the gray average value M of the pixel units in the region to be detected mean When the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean And judging that the electrode plate to be detected has metal leakage defects when the gray standard deviation D of the pixel units in the region to be detected is smaller than a threshold value and simultaneously meets the threshold value.
Further, step S2 is: and carrying out gray threshold processing on the image to obtain the target image, wherein the gray value of the pixel unit of the target image is [10,90].
Further, step S3 includes:
s31: acquiring a gray average value G of pixel units of each row of the target image along the width direction of the target image i Wherein: i is the number of rows of pixel units of the target image, and i=1, 2, n;
s32: using the gray average value G of the pixel units of each row of the target image i Calculating the horizontal gray G of the target image, and
the application also provides a defect detection device of the pole piece, which comprises:
the first acquisition module is used for acquiring an image of the pole piece to be detected;
the second acquisition module is used for carrying out gray processing on the image to obtain a target image;
the third acquisition module is used for carrying out horizontal gray level projection processing on the target image to obtain the horizontal gray level of the target image;
the fourth acquisition module is used for carrying out second order derivation on the horizontal gray level of the target image to obtain a gray level difference curve graph of the target image;
a determining module, configured to determine a region to be detected of the target image using a gray scale difference graph, including S51: obtaining the maximum value and the minimum value of the gray scale difference curve graph; s52: determining the number of lines i of the maximum value and the minimum value of the gray scale difference curve graph in the target image respectively max And i min The method comprises the steps of carrying out a first treatment on the surface of the S53: by using the ith in the target image max Line and i min The pixel units between the two pixel units form the region to be detected;
the judging module is configured to judge a defect of the to-be-detected pole piece by using at least one parameter of a gray maximum value, a gray minimum value and a gray standard deviation of a pixel unit in the to-be-detected area, where the defect includes at least one of a dark mark defect, a bright mark defect and a metal leakage defect, and includes:
acquiring pixel units in the region to be detectedAnd the average value M of the gray level of the pixel units in the region to be detected mean When the maximum gray value B of the pixel units in the region to be detected is greater than the average gray value M of the pixel units in the region to be detected mean Judging that the pole piece to be detected has a bright mark defect;
or acquiring the minimum gray value C of the pixel units in the to-be-detected area, and when the minimum gray value C of the pixel units in the to-be-detected area is smaller than the average gray value M of the pixel units in the to-be-detected area mean Judging that the pole piece to be detected has a dark mark defect;
or, acquiring the gray standard deviation D of the pixel units in the region to be detected, and when the gray maximum value B of the pixel units in the region to be detected is larger than the gray average value M of the pixel units in the region to be detected mean When the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean And judging that the electrode plate to be detected has metal leakage defects when the gray standard deviation D of the pixel units in the region to be detected is smaller than a threshold value and simultaneously meets the threshold value.
The application also provides a defect detection device of the pole piece, which comprises:
a processor;
a memory for storing a computer program for execution by the processor;
the defect detection method of the pole piece is realized when the processor executes the computer program.
The application also provides a computer readable storage medium, on which a computer program is stored, which when executed implements the defect detection method of the pole piece of the application.
Compared with the prior art, the application has the beneficial effects that:
according to the defect detection method for the pole piece, disclosed by the application, the gray level difference between the defect area and the non-defect area in the target image can be highlighted by conducting second-order derivation on the target image, so that the accuracy of determining the area to be detected is improved, and the accuracy of detecting the defect of the pole piece to be detected is further improved.
For a better understanding and implementation, the present application is described in detail below with reference to the drawings.
Drawings
FIG. 1 is a flow chart of a method for detecting defects of a pole piece according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a defect detecting device for pole pieces according to an embodiment of the present application;
1. a first acquisition module; 2. a second acquisition module; 3. a third acquisition module; 4. a fourth acquisition module; 5. a determining module; 6. and a judging module.
Detailed Description
It should be understood that the described embodiments are merely some, but not all embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the application, are intended to be within the scope of the embodiments of the present application.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of embodiments of the application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application as detailed in the accompanying claims. In the description of the present application, it should be understood that the terms "first," "second," "third," and the like are used merely to distinguish between similar objects and are not necessarily used to describe a particular order or sequence, nor should they be construed to indicate or imply relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
It is to be understood that the embodiments of the application are not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be made without departing from the scope thereof. The scope of embodiments of the application is limited only by the appended claims.
Referring to fig. 1, the present embodiment provides a defect detection method for a pole piece, including:
s1: and acquiring an image of the pole piece to be detected.
In the embodiment, the line scanning camera is adopted to collect the image of the pole piece to be detected, the image is connected through the camera link of the line scanning camera, and then the collecting card is provided with the encoder, so that the line scanning camera can be triggered to collect the image of the pole piece to be detected through the encoder. In addition, in practical application, the pole piece to be detected moves, so that light sources are required to be arranged in front of and behind the moving direction of the pole piece to be detected, and the line scanning camera can acquire clearer images.
S2: and carrying out gray processing on the image to obtain a target image.
In this embodiment, the gray-scale processing of the image is a process of converting the color image acquired by the line scanning camera into a gray-scale image, so that each pixel in the target image has a corresponding gray-scale value; further, in this embodiment, the image is subjected to gray-scale threshold processing to obtain the target image, and the gray-scale value of the pixel unit of the target image is [10,90].
S3: and carrying out horizontal gray level projection processing on the target image to obtain the horizontal gray level of the target image.
In this embodiment, in order to improve the detection efficiency of the defect of the pole piece to be detected, the possible defect area of the pole piece to be detected needs to be marked, so that only the possible defect area can be detected later, so as to improve the defect detection efficiency of the pole piece to be detected, therefore, in this embodiment, step S3 includes:
s31: acquiring a gray average value G of pixel units of each row of the target image along the width direction of the target image i Wherein: i is the number of rows of pixel units of the target image, and i=1, 2. In this embodiment, the gray level average value of the pixel units of each line of the target imageWherein: g ji And the gray value of the pixel units in the ith row and the jth column is m, and the column number of the pixel units in the target image is m.
S32: using the gray average value G of the pixel units of each row of the target image i Calculating the horizontal gray G of the target image, and
s4: and performing second-order derivation on the horizontal gray level of the target image to obtain a gray level difference curve graph of the target image.
In this embodiment, the formula for performing second order derivative on the horizontal gray scale of the target image is: wherein: />
In this embodiment, a first-order derivative is performed on the horizontal gray scale of the target image, so that a gray scale change rate distribution map of the target image can be obtained; then, the gray level difference curve graph can be obtained by deriving the gray level change rate distribution graph of the target image, and the gray level difference curve graph is a step curve graph with a concave-convex structure, compared with a mode of directly determining the region to be detected by using the horizontal gray level of the target image, the gray level difference between the defect region and the non-defect region in the target image can be highlighted by performing second-order derivation on the horizontal gray level of the target image, the region to be detected can be rapidly determined, the accuracy of determining the region to be detected can be improved, and the gray levels of pixel units in different regions of the target image can be averaged by the horizontal gray level of the target image, so that the difference between the defect region and the non-defect region of the target image is reduced, and the accuracy and the efficiency of determining the region to be detected are further reduced.
S5: and determining a region to be detected of the target image by using a gray level difference curve chart.
In the present embodiment, step S5 includes the steps of:
s51: and obtaining the maximum value and the minimum value of the gray scale difference curve graph.
S52: determining the number of lines i of the maximum value and the minimum value of the gray scale difference curve graph in the target image respectively max And i min 。
S53: by using the ith in the target image max Line and i min The pixel units between the two pixel units form the region to be detected.
In this embodiment, since the gray scale difference graph is a step graph of a concave-convex structure, which reflects the change speed of the gray scale change rate of the pixel units of different rows in the target image, the maximum value and the minimum value of the gray scale difference graph respectively reflect the position where the change speed of the gray scale change rate of the pixel units of different rows in the target image is the fastest and the position where the change speed of the gray scale change rate of the pixel units of different rows in the target image is the slowest; moreover, since the gray scales of the defective area and the non-defective area in the target image are obviously different, the position with the fastest change speed and the position with the smallest change speed of the gray scale change rate of the pixel units of different rows of the target image are the transition positions between the defective area and the non-defective area of the target image, and therefore the transition positions between the defective area and the non-defective area of the target image can be determined through the maximum value and the minimum value of the gray scale difference graph, and the purpose of quickly determining the area to be detected is achieved.
S6: and judging the defects of the pole piece to be detected by utilizing at least one parameter of the maximum gray value, the minimum gray value and the standard gray difference of the pixel units in the area to be detected, wherein the defects comprise at least one of dark mark defects, bright mark defects and metal leakage defects.
In this embodiment, first, a maximum gray value B, a minimum gray value C, a standard gray difference D, and an average gray value M of pixel units in the region to be detected are obtained mean The method comprises the steps of carrying out a first treatment on the surface of the Then, when the maximum gray value B of the pixel units in the region to be detected is larger than the average gray value M of the pixel units in the region to be detected mean Judging that the pole piece to be detected has a bright mark defect; when the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean Judging that the pole piece to be detected has a dark mark defect; in addition, when the maximum gray value B of the pixel units in the region to be detected is greater than the average gray value M of the pixel units in the region to be detected mean When the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean And judging that the pole piece to be detected has metal leakage defects when three conditions that the gray standard deviation D of the pixel units in the area to be detected is smaller than a threshold value are simultaneously met.
In this embodiment, the average gray level M of the pixel units in the region to be detected mean By bringing the whole of the region to be detectedAnd dividing the gray values of the partial pixel units by the number of the pixel units in the region to be detected after adding.
In summary, according to the defect detection method for the pole piece provided by the embodiment, the transition position between the defect area and the non-defect area of the target image can be highlighted by performing second-order derivation on the horizontal gray level of the target image, so that the to-be-detected area can be quickly and accurately determined; moreover, the defect detection method for the pole piece provided in this embodiment is implemented by using the maximum gray value B, the minimum gray value C, the standard gray difference D and the average gray value M of the pixel units in the to-be-detected area mean Whether the area to be detected has a bright mark defect, a dark mark defect, a metal leakage defect and the like can be judged, and the accuracy of detecting the defect of the pole piece to be detected is improved.
Referring to fig. 2, the present embodiment further provides a defect detection device for a pole piece, including:
the first acquisition module 1 is used for acquiring an image of the pole piece to be detected.
And the second acquisition module 2 is used for carrying out gray processing on the image to obtain a target image.
In this embodiment, the gray-scale processing of the image by the second acquisition module 2 is a process of converting the color image acquired by the line scanning camera into a gray-scale image, so each pixel in the target image has a corresponding gray-scale value; further, in the present embodiment, the image is subjected to gray-scale threshold processing, so that the target image is obtained, and the gray-scale value of the pixel unit of the target image is [10,90].
And the third acquisition module 3 is used for carrying out horizontal gray level projection processing on the target image to obtain the horizontal gray level of the target image.
In this embodiment, the third obtaining module 3 performs a horizontal gray scale projection process on the target image, and obtains the horizontal gray scale of the target image, which includes the following steps:
(1) Along the width of the target imageThe direction, the gray average value G of the pixel units of each row of the target image is obtained i Wherein: i is the number of rows of pixel units of the target image, and i=1, 2. In this embodiment, the gray level average value of the pixel units of each line of the target imageWherein: g ji And the gray value of the pixel units in the ith row and the jth column is m, and the column number of the pixel units in the target image is m.
(2) Using the gray average value G of the pixel units of each row of the target image i Calculating the horizontal gray G of the target image, and
(3) And performing second-order derivation on the horizontal gray level G of the target image to obtain the gray level difference curve graph. In this embodiment, the formula for performing second order derivative on the horizontal gray scale of the target image is:wherein:
and the fourth acquisition module 4 is used for carrying out second order derivation on the horizontal gray level of the target image to obtain a gray level difference curve graph of the target image.
In this embodiment, the formula for performing second order derivative on the horizontal gray scale of the target image is: wherein: />
And the determining module 5 is used for determining the region to be detected of the target image by using the gray level difference curve graph.
In this embodiment, the determining module 5 determines the region to be detected of the target image using a gray scale difference graph includes the following steps:
(1) And obtaining the maximum value and the minimum value of the gray scale difference curve graph.
(2) Determining the number of lines i of the maximum value and the minimum value of the gray scale difference curve graph in the target image respectively max And i min 。
(3) By using the ith in the target image max Line and i min The pixel units between the two pixel units form the region to be detected.
And the judging module 6 is used for judging the defects of the pole piece to be detected by utilizing at least one parameter of the maximum gray value, the minimum gray value and the standard gray difference of the pixel units in the area to be detected, wherein the defects comprise at least one of dark mark defects, bright mark defects and metal leakage defects.
In this embodiment, first, the determining module 5 takes a gray maximum value B, a gray minimum value C, a gray standard deviation D, and a gray average value M of the pixel units in the region to be detected mean The method comprises the steps of carrying out a first treatment on the surface of the Then, the judging module 5 judges the gray level maximum value B, the gray level minimum value C, the gray level standard deviation D and the gray level average value M of the pixel units in the region to be detected mean The type of defect of the pole piece to be detected is determined, in particular:
(1) When the maximum gray value B of the pixel units in the region to be detected is greater than the average gray value M of the pixel units in the region to be detected mean And judging that the pole piece to be detected has a bright mark defect.
(2) When the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean And judging that the pole piece to be detected has a dark mark defect.
(3) When the gray maximum value of the pixel unit in the region to be detectedB is larger than the average gray level M of the pixel units in the region to be detected mean When the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean And judging that the pole piece to be detected has metal leakage defects when three conditions that the gray standard deviation D of the pixel units in the area to be detected is smaller than a threshold value are simultaneously met.
Based on the defect detection method of the pole piece provided in the present embodiment, the present embodiment further provides a defect detection device of the pole piece, where the defect detection device of the pole piece may be a terminal device such as a server, a desktop computing device or a mobile computing device (for example, a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.), and the defect detection device includes:
a processor;
a memory for storing a computer program for execution by the processor;
the defect detection method of the pole piece is realized when the processor executes the computer program.
Based on the defect detection method of the pole piece provided in the embodiment, the embodiment also provides a computer readable storage medium, on which a computer program is stored, and the defect detection method of the pole piece is implemented when the computer program is executed.
The present application may take the form of a computer program product embodied on one or more storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) having program code embodied therein. Computer-usable storage media include both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to: phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, may be used to store information that may be accessed by the computing device.
The foregoing examples have shown only the preferred embodiments of the application, which are described in more detail and are not to be construed as limiting the scope of the application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the spirit of the application, and the application is intended to encompass such modifications and improvements.
Claims (6)
1. The defect detection method of the pole piece is characterized by comprising the following steps of:
s1: acquiring an image of a pole piece to be detected;
s2: carrying out gray processing on the image to obtain a target image;
s3: performing horizontal gray level projection processing on the target image to obtain horizontal gray level of the target image;
s4: performing second-order derivation on the horizontal gray level of the target image to obtain a gray level difference curve graph of the target image;
s5: determining a region to be detected of the target image by using a gray scale difference graph, including S51: obtaining the maximum value and the minimum value of the gray scale difference curve graph; s52: determining the number of lines i of the maximum value and the minimum value of the gray scale difference curve graph in the target image respectively max And i min The method comprises the steps of carrying out a first treatment on the surface of the S53: by using the ith in the target image max Line and i min The pixel units between the two pixel units form the region to be detected;
s6: judging the defects of the pole piece to be detected by utilizing at least one parameter of the maximum gray value, the minimum gray value and the standard gray difference of the pixel units in the area to be detected, wherein the defects comprise at least one of dark mark defects, bright mark defects and metal leakage defects, and the method comprises the following steps:
acquisition ofThe maximum gray value B of the pixel units in the region to be detected and the average gray value M of the pixel units in the region to be detected mean When the maximum gray value B of the pixel units in the region to be detected is greater than the average gray value M of the pixel units in the region to be detected mean Judging that the pole piece to be detected has a bright mark defect;
or acquiring the minimum gray value C of the pixel units in the to-be-detected area, and when the minimum gray value C of the pixel units in the to-be-detected area is smaller than the average gray value M of the pixel units in the to-be-detected area mean Judging that the pole piece to be detected has a dark mark defect;
or, acquiring the gray standard deviation D of the pixel units in the region to be detected, and when the gray maximum value B of the pixel units in the region to be detected is larger than the gray average value M of the pixel units in the region to be detected mean When the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean And judging that the electrode plate to be detected has metal leakage defects when the gray standard deviation D of the pixel units in the region to be detected is smaller than a threshold value and simultaneously meets the threshold value.
2. The defect detection method of the pole piece according to claim 1, wherein step S2 is: and carrying out gray threshold processing on the image to obtain the target image, wherein the gray value of the pixel unit of the target image is [10,90].
3. The defect detection method of the pole piece according to claim 1, wherein step S3 comprises:
s31: acquiring a gray average value G of pixel units of each row of the target image along the width direction of the target image i Wherein: i is the number of rows of pixel units of the target image, and i=1, 2, …, n;
s32: using the gray average value G of the pixel units of each row of the target image i Calculating the horizontal gray G of the target image, and
4. a defect detection device for a pole piece, comprising:
the first acquisition module is used for acquiring an image of the pole piece to be detected;
the second acquisition module is used for carrying out gray processing on the image to obtain a target image;
the third acquisition module is used for carrying out horizontal gray level projection processing on the target image to obtain the horizontal gray level of the target image;
the fourth acquisition module is used for carrying out second order derivation on the horizontal gray level of the target image to obtain a gray level difference curve graph of the target image;
a determining module, configured to determine a region to be detected of the target image using a gray scale difference graph, including S51: obtaining the maximum value and the minimum value of the gray scale difference curve graph; s52: determining the number of lines i of the maximum value and the minimum value of the gray scale difference curve graph in the target image respectively max And i min The method comprises the steps of carrying out a first treatment on the surface of the S53: by using the ith in the target image max Line and i min The pixel units between the two pixel units form the region to be detected;
the judging module is configured to judge a defect of the to-be-detected pole piece by using at least one parameter of a gray maximum value, a gray minimum value and a gray standard deviation of a pixel unit in the to-be-detected area, where the defect includes at least one of a dark mark defect, a bright mark defect and a metal leakage defect, and includes:
acquiring a gray maximum value B of a pixel unit in the region to be detected and a gray average value M of the pixel unit in the region to be detected mean When the maximum gray value B of the pixel units in the region to be detected is greater than the average gray value M of the pixel units in the region to be detected mean Judging that the pole piece to be detected has a bright mark defect;
or, acquiring the gray scale of the pixel unit in the region to be detectedA minimum value C, and when the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean Judging that the pole piece to be detected has a dark mark defect;
or, acquiring the gray standard deviation D of the pixel units in the region to be detected, and when the gray maximum value B of the pixel units in the region to be detected is larger than the gray average value M of the pixel units in the region to be detected mean When the gray minimum value C of the pixel units in the region to be detected is smaller than the gray average value M of the pixel units in the region to be detected mean And judging that the electrode plate to be detected has metal leakage defects when the gray standard deviation D of the pixel units in the region to be detected is smaller than a threshold value and simultaneously meets the threshold value.
5. A defect detection apparatus for a pole piece, comprising:
a processor;
a memory for storing a computer program for execution by the processor;
wherein the processor, when executing the computer program, implements the method for defect detection of pole pieces of any one of claims 1-3.
6. A computer readable storage medium, characterized in that a computer program is stored thereon, which computer program, when executed, implements the defect detection method of a pole piece according to any of claims 1-3.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016168823A1 (en) * | 2015-04-16 | 2016-10-20 | Jabil Circuit, Inc. | Defect detection using a first derivative of an image |
| CN106780465A (en) * | 2016-08-15 | 2017-05-31 | 哈尔滨工业大学 | Retinal images aneurysms automatic detection and recognition methods based on gradient vector analysis |
| CN106934803A (en) * | 2017-03-13 | 2017-07-07 | 珠海格力智能装备有限公司 | The detection method and device of electronic device surface defect |
| CN108230321A (en) * | 2018-01-19 | 2018-06-29 | 深圳市亿图视觉自动化技术有限公司 | Defect inspection method and device |
| CN108346138A (en) * | 2017-12-04 | 2018-07-31 | 广东嘉铭智能科技有限公司 | A kind of detection method of surface flaw and system based on image procossing |
| CN109483887A (en) * | 2018-11-14 | 2019-03-19 | 华中科技大学 | Shaping layer contour accuracy online test method in the fusion process of selective laser |
| CN109598721A (en) * | 2018-12-10 | 2019-04-09 | 广州市易鸿智能装备有限公司 | Defect inspection method, device, detection device and the storage medium of battery pole piece |
| CN112529876A (en) * | 2020-12-15 | 2021-03-19 | 天津大学 | Method for detecting edge defects of contact lenses |
| CN113298803A (en) * | 2021-06-15 | 2021-08-24 | 歌尔光学科技有限公司 | Magnet piece detection method, system, equipment and computer readable storage medium |
| CN113538430A (en) * | 2021-09-16 | 2021-10-22 | 深圳新视智科技术有限公司 | Pole piece defect detection method, device, equipment and medium based on difference |
| CN114235758A (en) * | 2021-12-10 | 2022-03-25 | 苏州凌云视界智能设备有限责任公司 | Defect detection method, device, equipment and storage medium |
| CN114764804A (en) * | 2022-06-16 | 2022-07-19 | 深圳新视智科技术有限公司 | Lithium battery pole piece defect detection method, device, equipment and storage medium |
| CN114972147A (en) * | 2021-02-19 | 2022-08-30 | 中国科学院微电子研究所 | Method and device for detecting wafer defects |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006200972A (en) * | 2005-01-19 | 2006-08-03 | Tokyo Seimitsu Co Ltd | Image defect inspection method, image defect inspection device, and external appearance inspection device |
| CN108387587B (en) * | 2018-01-22 | 2020-07-31 | 京东方科技集团股份有限公司 | Defect detection method and defect detection equipment |
-
2022
- 2022-12-28 CN CN202211696446.3A patent/CN116012330B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016168823A1 (en) * | 2015-04-16 | 2016-10-20 | Jabil Circuit, Inc. | Defect detection using a first derivative of an image |
| CN106780465A (en) * | 2016-08-15 | 2017-05-31 | 哈尔滨工业大学 | Retinal images aneurysms automatic detection and recognition methods based on gradient vector analysis |
| CN106934803A (en) * | 2017-03-13 | 2017-07-07 | 珠海格力智能装备有限公司 | The detection method and device of electronic device surface defect |
| CN108346138A (en) * | 2017-12-04 | 2018-07-31 | 广东嘉铭智能科技有限公司 | A kind of detection method of surface flaw and system based on image procossing |
| CN108230321A (en) * | 2018-01-19 | 2018-06-29 | 深圳市亿图视觉自动化技术有限公司 | Defect inspection method and device |
| CN109483887A (en) * | 2018-11-14 | 2019-03-19 | 华中科技大学 | Shaping layer contour accuracy online test method in the fusion process of selective laser |
| CN109598721A (en) * | 2018-12-10 | 2019-04-09 | 广州市易鸿智能装备有限公司 | Defect inspection method, device, detection device and the storage medium of battery pole piece |
| CN112529876A (en) * | 2020-12-15 | 2021-03-19 | 天津大学 | Method for detecting edge defects of contact lenses |
| CN114972147A (en) * | 2021-02-19 | 2022-08-30 | 中国科学院微电子研究所 | Method and device for detecting wafer defects |
| CN113298803A (en) * | 2021-06-15 | 2021-08-24 | 歌尔光学科技有限公司 | Magnet piece detection method, system, equipment and computer readable storage medium |
| CN113538430A (en) * | 2021-09-16 | 2021-10-22 | 深圳新视智科技术有限公司 | Pole piece defect detection method, device, equipment and medium based on difference |
| CN114235758A (en) * | 2021-12-10 | 2022-03-25 | 苏州凌云视界智能设备有限责任公司 | Defect detection method, device, equipment and storage medium |
| CN114764804A (en) * | 2022-06-16 | 2022-07-19 | 深圳新视智科技术有限公司 | Lithium battery pole piece defect detection method, device, equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 基于区域灰度特征比较和人眼睑曲率的状态识别;赵恒炜;;电子科技(第06期);全文 * |
| 基于机器视觉的图像处理技术识别锂电池极片的缺陷;邬博等;《机械研究与应用》;全文 * |
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