CN115063414A - Method, device and equipment for detecting lithium battery pole piece gummed paper and storage medium - Google Patents
Method, device and equipment for detecting lithium battery pole piece gummed paper and storage medium Download PDFInfo
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- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 56
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000003860 storage Methods 0.000 title claims abstract description 14
- 238000001514 detection method Methods 0.000 claims abstract description 131
- 230000009466 transformation Effects 0.000 claims abstract description 24
- 239000000853 adhesive Substances 0.000 claims abstract description 7
- 230000001070 adhesive effect Effects 0.000 claims abstract description 7
- 239000002390 adhesive tape Substances 0.000 claims description 37
- 238000006243 chemical reaction Methods 0.000 claims description 12
- 238000005520 cutting process Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 5
- 229910001416 lithium ion Inorganic materials 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 20
- 238000012545 processing Methods 0.000 description 11
- 238000004519 manufacturing process Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 239000000047 product Substances 0.000 description 6
- 238000007619 statistical method Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000004927 fusion Effects 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 230000003321 amplification Effects 0.000 description 2
- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 229910000733 Li alloy Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 239000011888 foil Substances 0.000 description 1
- 239000001989 lithium alloy Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000007773 negative electrode material Substances 0.000 description 1
- 239000011255 nonaqueous electrolyte Substances 0.000 description 1
- 239000011265 semifinished product Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G06T5/00—Image enhancement or restoration
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- G06T5/94—Dynamic range modification of images or parts thereof based on local image properties, e.g. for local contrast enhancement
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
- G06T2207/20192—Edge enhancement; Edge preservation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for detecting lithium battery pole piece gummed paper, wherein the method comprises the following steps: acquiring a target image of a pole piece to be detected, wherein adhesive paper is arranged on the pole piece to be detected, and the target image is a color image; aiming at a plurality of color channels of a target image, calculating the gray scale statistical characteristics of the target image under each color channel respectively, and determining 2 target color channels based on the calculated gray scale statistical characteristics; performing preset image transformation on images corresponding to 2 target color channels in the target image to obtain a detection gray image; and performing linear detection based on the detection gray level image, and determining the gummed paper detection result of the pole piece to be detected according to the detected linear. By adopting the invention, the linear detection rate and the accuracy of linear detection can be improved, thereby improving the detection accuracy of the gummed paper.
Description
Technical Field
The invention relates to the technical field of lithium batteries and the technical field of automatic visual detection of industrial machines, in particular to a method and a device for detecting lithium battery pole piece gummed paper, computer equipment and a computer readable storage medium.
Background
The battery is a type of battery using a nonaqueous electrolyte solution with lithium metal or a lithium alloy as a negative electrode material. Because the chemical characteristics of lithium metal are very active, the lithium battery has high requirements on the manufacturing process. Lithium batteries are broadly classified into two types: lithium metal batteries and lithium ion batteries. The lithium ion battery is used for the new energy automobile. With the strong promotion of new energy in China, the power battery industry becomes popular. At present, lithium ion batteries are widely applied to the fields of portable electronic products, electric vehicles, large power supplies, secondary charging, energy storage and the like, so that the quality problem of the lithium ion batteries is more and more important.
In the course of working of lithium cell, need go up and carry out the rubberizing on the pole piece and handle, wherein, the adhesive tape on the pole piece generally relies on rubberizing processing equipment direct once only to accomplish the rubberizing, and the production of rubberizing position and adhesive tape size are bad is inevitable, because constantly improve the requirement to security, quality uniformity, need measure the adhesive tape position and the adhesive tape size of semi-manufactured goods after the processing to confirm whether the pole piece after the rubberizing accords with the requirement of lithium cell production.
In the relevant technical scheme of the detection of the gummed paper of the lithium battery pole piece, the position and the size of the gummed paper can be measured in a manual measurement mode, but the manual measurement efficiency is low and the error is large. Or, the position and size of the gummed paper can be measured through a black-and-white image or a gray-scale image, but the black-and-white image is not good enough to represent the gummed paper, generally, the contrast is low, and the accuracy in measurement is low; the gray-scale image has low contrast and low accuracy in measurement.
Disclosure of Invention
In view of the above, it is necessary to provide a method and an apparatus for detecting a lithium battery pole piece adhesive tape, a computer device, and a computer readable storage medium.
In a first aspect of the present invention, a method for detecting a lithium battery electrode tab gummed paper is provided, the method comprising:
obtaining a target image of a pole piece to be detected, wherein adhesive paper is arranged on the pole piece to be detected, and the target image is a color image;
aiming at a plurality of color channels of the target image, calculating the gray scale statistical characteristics of the target image under each color channel respectively, and determining 2 target color channels based on the calculated gray scale statistical characteristics;
performing preset image transformation on images corresponding to the 2 target color channels in the target image to obtain a detection gray image;
and performing linear detection based on the detection gray level image, and determining the gummed paper detection result of the pole piece to be detected according to the detected linear.
Optionally, the step of obtaining the target image of the pole piece to be detected further includes: the method comprises the steps of collecting an image of a pole piece to be detected through a camera device, and determining one or more ROI (region of interest) areas in the collected image as a target image of the pole piece to be detected.
Optionally, the plurality of color channels include an R color channel, a G color channel, and a B color channel; the grayscale statistical feature includes the grayscale standard deviation; the step of calculating the grayscale statistical characteristics of the target image under each color channel respectively for the plurality of color channels of the target image, and determining 2 target color channels based on the calculated grayscale statistical characteristics further includes: calculating the gray standard deviation of the channel image of the target image under each color channel; determining a color channel corresponding to the maximum value of the gray standard deviation corresponding to each color channel as a first color channel, and determining a color channel corresponding to the minimum value of the gray standard deviation corresponding to each color channel as a second color channel; wherein the 2 target color channels include a first color channel and a second color channel.
Optionally, the step of performing preset image transformation on the image corresponding to the 2 target color channels in the target image to obtain a detection gray image further includes: calculating an average gray level image corresponding to the channel image under the first color channel and the channel image under the second color channel; calculating the absolute value of the difference between the channel image under the first color channel and the average gray scale image to be used as a first average difference image, and calculating the absolute value of the difference between the channel image under the second color channel and the average gray scale value to be used as a second average difference image; and summing the first average difference image and the second average difference image, and cutting a summation result to obtain a conversion image obtained by performing preset image conversion on the images corresponding to the 2 target color channels in the target image.
Optionally, the step of performing preset image transformation on the image corresponding to the 2 target color channels in the target image to obtain a detection gray image further includes: and amplifying the converted image, and determining the detection gray image based on the amplified converted image and the channel image under the first color channel.
Optionally, the step of performing amplification processing on the transformed image, and determining the detection gray image based on the amplified transformed image and the channel image in the first color channel further includes: according to the formula
Performing an enlargement process on the transformed image, wherein,
representing the transformed image after enlargement,
represents the maximum value of the gray-scale average of the channel images for a plurality of color channels,
represents the maximum value of the gray scale standard deviation of the channel images for a plurality of color channels,
representing a transformed image; according to the formula
Determining a detection gray scale image, wherein,
a gray-scale image is represented for detection,
representing a channel image corresponding to the first color channel.
Optionally, the step of performing linear detection based on the detection gray level image and determining the gummed paper detection result of the pole piece to be detected according to the detected linear image further includes: performing line detection on the detection gray level image based on a preset line detection algorithm, and determining one or more pieces of line information in the target image; and calculating distance information between straight lines according to the detected one or more pieces of straight line information, and determining a gummed paper detection result of the pole piece to be detected according to the calculated distance information, wherein the gummed paper detection result comprises position information and size information of the gummed paper on the pole piece to be detected.
In the second part of the invention, a detection device for lithium battery pole piece gummed paper is provided, which comprises:
the device comprises an image acquisition module, a detection module and a control module, wherein the image acquisition module is used for acquiring a target image of a pole piece to be detected, the pole piece to be detected is provided with adhesive paper, and the target image is a color image;
the characteristic statistical module is used for respectively calculating the gray scale statistical characteristics of the target image under each color channel aiming at a plurality of color channels of the target image, and determining 2 target color channels based on the calculated gray scale statistical characteristics;
the image transformation module is used for carrying out preset image transformation on the images corresponding to the 2 target color channels in the target image to obtain a detection gray image;
and the linear detection module is used for carrying out linear detection based on the detection gray level image and determining the gummed paper detection result of the pole piece to be detected according to the detected linear.
In a third aspect of the present invention, there is provided a computer device, where the computer device includes a memory and a processor, and the memory has an executable code, and when the executable code runs on the processor, the method for detecting the lithium battery pole piece gummed paper is implemented.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, where the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the foregoing method for detecting the lithium battery pole piece gummed paper.
By adopting the embodiment of the invention, the following beneficial effects are achieved:
after the detection method, the detection device, the computer equipment and the computer-readable storage medium of the lithium battery pole piece adhesive tape are adopted, a target image of a pole piece to be detected is obtained, wherein the adhesive tape is arranged on the pole piece to be detected, and the target image is a color image; aiming at a plurality of color channels of the target image, calculating the gray scale statistical characteristics of the target image under each color channel respectively, and determining 2 target color channels based on the calculated gray scale statistical characteristics; performing preset image transformation on images corresponding to the 2 target color channels in the target image to obtain a detection gray image; and performing linear detection based on the detection gray level image, and determining the gummed paper detection result of the pole piece to be detected according to the detected linear. That is to say, in the embodiment of the present invention, the images under each color channel are subjected to statistical analysis, and the images are subjected to processing such as conversion according to the analysis result, so that the contrast of the edges of the adhesive tape can be improved, and thus the linear detection rate and the accuracy of linear detection are improved, and the detection accuracy of the adhesive tape is improved; moreover, the image fusion with the maximum difference between the transformed image and the original image enables the detection method of the lithium battery pole piece adhesive tape to be applicable to more adhesive tape processes, and the compatibility and the application range of the detection method of the lithium battery pole piece adhesive tape are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a schematic flow chart illustrating a method for detecting adhesive tape of a lithium battery electrode tab in one embodiment;
FIG. 2 is a schematic view of an image of a pole piece to be sensed in one embodiment;
FIG. 3 is a diagram of an image corresponding to an ROI area in an embodiment
FIG. 4 shows a first color channel in one embodiment
A gray scale schematic diagram corresponding to the lower channel image;
FIG. 5 shows a second color channel in one embodiment
A gray scale schematic diagram corresponding to the lower channel image;
FIG. 6 is an average gray scale map of an embodiment
A schematic diagram of (a);
FIG. 7 is a diagram of transforming an image in one embodiment
A schematic diagram of (a);
FIG. 8 is a diagram of a transformed image after magnification in one embodiment;
FIG. 9 is a diagram illustrating detection of a grayscale image according to one embodiment;
FIG. 10 is a diagram illustrating a target image corresponding to a ROI area in another embodiment;
FIG. 11 is a diagram illustrating detection of a gray scale image according to another embodiment;
FIG. 12 is a schematic structural diagram of a detection apparatus for a lithium battery electrode tab gummed paper in an embodiment;
fig. 13 is a schematic structural diagram of a computer device for operating the detection method for the gummed paper of the lithium battery pole piece in one embodiment.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this embodiment, a method for detecting a lithium battery pole piece adhesive tape is provided, where the method may be performed in an industrial scene, for example, in a production line of a lithium battery pole piece or an adhesive tape product line of a lithium battery pole piece, a camera is arranged on the production line to acquire an image of the lithium battery pole piece on the production line, and then a position and a size of the adhesive tape on the corresponding lithium battery pole piece are detected based on image recognition to determine whether a semi-finished product of the pole piece adhesive tape in a lithium battery generation process has a problem, so as to distinguish a good product from an inferior product, thereby improving a yield of the pole piece entering a subsequent process of lithium battery production, and improving a yield of lithium battery generation.
In this embodiment, the detection method based on the lithium battery pole piece gummed paper may be implemented based on a computer device connected to a camera for collecting an image of the lithium battery pole piece to be detected, where the computer device may be a control device of a product line or a server connected to the product line, and is not limited in this embodiment.
The detection method of the lithium battery pole piece gummed paper can detect the gummed paper position and the gummed paper size of the lithium battery pole piece gummed paper so as to determine whether the position and the size of the gummed paper on the lithium battery pole piece are accurate or not, and therefore the possibility of poor gumming position and size of the gummed paper is reduced.
In a specific embodiment, as shown in fig. 1, a schematic flow chart of the method for detecting the lithium battery pole piece gummed paper is given, where the method for detecting the lithium battery pole piece gummed paper includes steps S101 to S104 shown in fig. 1:
step S101: the method comprises the steps of obtaining a target image of a pole piece to be detected, wherein adhesive paper is arranged on the pole piece to be detected, and the target image is a color image.
In this embodiment, for a pole piece to be detected that needs to be subjected to adhesive tape detection, an image of the pole piece to be detected, that is, a target image in this embodiment, needs to be obtained through a camera device (for example, a CCD device) disposed above the pole piece to be detected. The target image is a color image and includes images of a plurality of color channels, that is, a channel image of each color channel. In a particular embodiment, the plurality of color channels includes an R color channel, a G color channel, and a B color channel; in other embodiments, the plurality of color channels may also include a Y color channel, a U color channel, and a V color channel, which are not limited herein.
After acquiring a target image of a pole piece to be detected, ROI (region of interest) identification needs to be further performed on the target image to determine one or more ROI regions in the target image, and then the following steps S101 to S104 are respectively performed with each ROI region as an image, that is, the ROI region extracted from the image corresponding to the pole piece to be detected is used as the target image of the pole piece to be detected, and then, the gummed paper detection results such as gummed paper position and gummed paper size are respectively acquired for the target image corresponding to each ROI region.
In a specific embodiment, as shown in fig. 2, a schematic diagram of an image of a pole piece to be detected is given (shown in the figure by a gray image, the image is a color image actually), wherein a black area corresponds to the pole piece to be detected, and a middle gray portion corresponds to an area where the gummed paper is located, as can be known from fig. 2, in the gray image, the gray contrast of the upper, lower, left and right edge positions of the area where the gummed paper is located is not high, and the accuracy of directly detecting the gummed paper position and the gummed paper size based on the gray image is not sufficient.
Further, the detection of the ROI area of the image of the pole piece to be detected may be based on a preset ROI area, and the translation following is performed by detecting the position of the pole piece to be detected and the position of the empty foil therein, so as to determine one or more ROI areas in the image of the pole piece to be detected, and determine a target image corresponding to the one or more ROI areas of the pole piece to be detected. Fig. 3 shows a schematic diagram of a map corresponding to a ROI region.
The analysis and processing of the target image in steps S102-S104 below is based on a single ROI region (or a single target image), and is set forth below directly as an example for the processing of the target image.
Step S102: and aiming at a plurality of color channels of the target image, calculating the gray scale statistical characteristics of the target image under each color channel respectively, and determining 2 target color channels based on the calculated gray scale statistical characteristics.
In this step, each target image needs to be processed separately, and the channel images under the multiple color channels of the target image need to be calculated separately for statistical analysis.
The color channels are illustrated here as comprising R, G, B3 color channels.
For the channel images corresponding to the target image under R, G, B3 color channels, the gray level mean value corresponding to each channel image needs to be calculated respectively
That is, the mean value of the gradations under the R color channel is calculated
Calculating the mean value of gray levels in G color channels
Calculating the mean value of gray levels in B color channel
。
Then, based on the calculated gray level mean value, the corresponding gray level standard deviation is further calculated
(ii) a I.e. calculating the standard deviation of the gray scale in the R color channel
Calculating the standard deviation of gray scale in G color channel
Calculating the standard deviation of gray scale in B color channel
。
The grayscale statistical characteristics of the channel image include one or more of corresponding grayscale mean and grayscale standard deviation.
Then, in the process of determining 2 target color channels based on the calculated gray scale statistical characteristics, the maximum value and the minimum value in the gray scale standard deviation are determined according to the size of the gray scale standard deviation of the channel image, and the maximum value is used for determining the maximum value
And minimum value
Corresponding colorThe color channels are respectively determined as first color channels
And a second color channel
That is, for a channel image of the target image under each color channel, calculating a gray standard deviation of the channel image; determining the maximum value of the corresponding gray standard deviation of each color channel
The corresponding color channel is used as the first color channel
Determining the minimum value of the corresponding gray standard deviation of each color channel
The corresponding color channel is used as the second color channel
(ii) a Wherein the 2 target color channels include a first color channel
And a second color channel
。
Referring specifically to fig. 4 and 5, fig. 4 and 5 show the first color channel, respectively
And a second color channel
The lower channel image corresponds to a gray scale diagram.
In this step, statistical analysis is performed on the channel images of the multiple color channels, and confirmation is performed based on the grayscale statistical characteristics, so as to determine 2 color channels corresponding to the maximum value and the minimum value of the grayscale standard deviation, and further perform further calculation in subsequent steps.
Step S103: and carrying out preset image transformation on the images corresponding to the 2 target color channels in the target image to obtain a detection gray image.
In this step, for the determined first color channel and second color channel, the original images (channel images of the target image in the first color channel and the second color channel) in the corresponding color channels need to be subjected to transformation calculation to obtain corresponding transformation images, and then processing is performed based on the transformation images to obtain grayscale images (detection grayscale images) finally used for line detection.
Specifically, an average gray level image corresponding to a channel image under a first color channel and a channel image under a second color channel is calculated; calculating the absolute value of the difference between the channel image under the first color channel and the average gray level image to be used as a first average difference image, and calculating the absolute value of the difference between the channel image under the second color channel and the average gray level value to be used as a second average difference image; and summing the first average difference image and the second average difference image, and cutting off a summation result to obtain a conversion image obtained by performing preset image conversion on the images corresponding to the 2 target color channels in the target image.
That is, for the first color channel
And a second color channel
By the formula
Calculating the average gray level image corresponding to the channel images under the two color channels
That is, the average gray scale map corresponds to the gray scale map corresponding to the average gray scale value of the channel image in the 2 color channels. See in particular the schematic diagram given in fig. 6.
Then, the first color channels are calculated respectively
And a second color channel
Obtaining the absolute value of the difference between the lower channel image and the average gray level image to obtain a first average difference image
And a second average difference image
Namely:
for the first average difference image
And a second average difference image
Performing summation to obtain
Then, the result of the summation is further processed for cutting (also as a cut-off)Physical) to reduce the effects of noise, namely:
wherein,
the result after the cutting process is the converted image obtained by performing the preset image conversion on the image corresponding to the 2 target color channels in the target image in the step
. See in particular the schematic illustration of fig. 7.
In obtaining a transformed image
After that, further processing is required.
Specifically, the converted image is amplified, and the detection grayscale image is determined based on the amplified converted image and the channel image in the first color channel. Wherein according to the formula
Performing an enlargement process on the transformed image, wherein,
showing the transformed image after enlargement (see in particular the schematic diagram of figure 8),
represents the maximum value of the gray-scale average of the channel images for a plurality of color channels,
represents the maximum value of the gray scale standard deviation of the channel images in a plurality of color channels,
representing a transformed image; according to the formula
Determining a detection gray scale image, wherein,
representing a detected gray scale image (see in particular the schematic diagram of fig. 9).
The expressed detection gray level image can be directly used for linear detection, so that the linear position between the gummed paper and the pole piece in the target image of the pole piece to be detected is determined.
Step S104: and performing linear detection based on the detection gray level image, and determining the gummed paper detection result of the pole piece to be detected according to the detected linear.
Performing line detection on the detection gray level image based on a preset line detection algorithm, and determining one or more pieces of line information in the target image; and calculating distance information between straight lines according to the detected one or more pieces of straight line information, and determining a gummed paper detection result of the pole piece to be detected according to the calculated distance information, wherein the gummed paper detection result comprises position information and size information of the gummed paper on the pole piece to be detected.
That is to say, in this embodiment, the processing and the line detection in the above steps are respectively performed on the target image corresponding to each ROI region, so that the line position in each ROI region can be determined, where the line position is a line corresponding to the edge position between the gummed adhesive tape and the pole piece, and the position information and the size information of the adhesive tape can be determined according to the detected line position, so as to determine the adhesive tape detection result.
Fig. 10-11 show another embodiment, in the case of the target image corresponding to the ROI shown in fig. 10, which corresponds to the obtained detection gray image, wherein the edge between the adhesive tape and the pole piece is clearer than the edge in fig. 10, and the accuracy of detecting the position information and the size information of the adhesive tape is improved.
In this embodiment, in the above steps S101 to S104, the statistical analysis of the gray scale features is performed on the image of the pasted pole piece to be detected in each color channel to determine the color channel used in the image transformation later, and then the contrast of the edge of the gummed paper is maximized by the average gray scale image calculation, the difference calculation, the absolute value calculation, and the summation calculation in the image transformation, and the offset cutting is performed on the gray scale image after the summation, so that the influence of noise is reduced, and the accuracy of the edge detection of the gummed paper is improved. And the converted image is summed with the channel image under the original color channel by the method, and the maximum difference value in the original image and the converted image is fused, so that the compatibility of the adhesive tape detection scheme to various processes can be improved, and the application range of the adhesive tape detection method is widened.
After the detection method of the lithium battery pole piece gummed paper is adopted, the images under each color channel are subjected to statistical analysis, the images are subjected to conversion and other processing according to the analysis result, the contrast of the gummed paper edge can be improved, the linear detection rate and the accuracy of linear detection are improved, and the detection accuracy of the gummed paper is improved; moreover, the image fusion with the maximum difference between the transformed image and the original image enables the detection method of the lithium battery pole piece adhesive tape to be applicable to more adhesive tape processes, and the compatibility and the application range of the detection method of the lithium battery pole piece adhesive tape are improved.
In another embodiment, a device for detecting the gummed paper of the lithium battery pole piece is provided, as shown in fig. 12,
the image acquisition module 101 is configured to acquire a target image of a pole piece to be detected, wherein the pole piece to be detected is provided with adhesive paper, and the target image is a color image;
a feature statistics module 102, configured to calculate, for multiple color channels of the target image, a grayscale statistics feature of the target image under each color channel, and determine 2 target color channels based on the calculated grayscale statistics feature;
the image transformation module 103 is configured to perform preset image transformation on images corresponding to the 2 target color channels in the target image to obtain a detection gray image;
and the linear detection module 104 is used for performing linear detection based on the detection gray level image and determining a gummed paper detection result of the pole piece to be detected according to the detected linear.
In an optional embodiment, the image acquisition module 101 is further configured to acquire an image of the pole piece to be detected by using a camera, and determine one or more ROI regions in the acquired image as a target image of the pole piece to be detected.
In an alternative embodiment, the plurality of color channels includes an R color channel, a G color channel, and a B color channel; the grayscale statistical feature includes the grayscale standard deviation; the feature statistics module 102 is further configured to calculate, for a channel image of the target image under each color channel, a gray standard deviation of the channel image; determining a color channel corresponding to the maximum value of the gray standard deviation corresponding to each color channel as a first color channel, and determining a color channel corresponding to the minimum value of the gray standard deviation corresponding to each color channel as a second color channel; wherein the 2 target color channels include a first color channel and a second color channel.
In an optional embodiment, the image transformation module 103 is further configured to calculate an average grayscale map corresponding to the channel image in the first color channel and the channel image in the second color channel; calculating the absolute value of the difference between the channel image under the first color channel and the average gray level image to be used as a first average difference image, and calculating the absolute value of the difference between the channel image under the second color channel and the average gray level value to be used as a second average difference image; and summing the first average difference image and the second average difference image, and cutting off a summation result to obtain a conversion image obtained by performing preset image conversion on the images corresponding to the 2 target color channels in the target image.
In an optional embodiment, the image transformation module 103 is further configured to perform an amplification process on the transformed image, and determine the detection grayscale image based on the amplified transformed image and the channel image in the first color channel.
In an alternative embodiment, the image transformation module 103 is further configured to generate the image according to a formula
Performing an enlargement process on the transformed image, wherein,
representing the transformed image after enlargement,
represents the maximum value of the gray-scale average of the channel images for a plurality of color channels,
represents the maximum value of the gray scale standard deviation of the channel images for a plurality of color channels,
representing a transformed image; according to the formula
Determining a detection gray scale image, wherein,
a gray-scale image is represented for detection,
representing a channel image corresponding to the first color channel.
In an optional embodiment, the line detection module 104 is further configured to perform line detection on the detected grayscale image based on a preset line detection algorithm, and determine one or more pieces of line information in the target image; and calculating distance information between straight lines according to the detected one or more pieces of straight line information, and determining a gummed paper detection result of the pole piece to be detected according to the calculated distance information, wherein the gummed paper detection result comprises position information and size information of the gummed paper on the pole piece to be detected.
Fig. 13 shows an internal structure diagram of a computer device for implementing the detection method of the lithium battery pole piece gummed paper in one embodiment. The computer device may specifically be a terminal, and may also be a server. As shown in fig. 13, the computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program which, when executed by the processor, causes the processor to carry out the above-mentioned method. The internal memory may also have stored therein a computer program which, when executed by the processor, causes the processor to perform the method described above. Those skilled in the art will appreciate that the architecture shown in fig. 13 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
After the detection method, the detection device, the computer equipment and the computer-readable storage medium of the lithium battery pole piece adhesive tape are adopted, a target image of a pole piece to be detected is obtained, wherein the adhesive tape is arranged on the pole piece to be detected, and the target image is a color image; aiming at a plurality of color channels of the target image, calculating the gray scale statistical characteristics of the target image under each color channel respectively, and determining 2 target color channels based on the calculated gray scale statistical characteristics; performing preset image transformation on images corresponding to the 2 target color channels in the target image to obtain a detection gray image; and performing linear detection based on the detection gray level image, and determining the gummed paper detection result of the pole piece to be detected according to the detected linear. That is to say, in the embodiment of the present invention, the images under each color channel are subjected to statistical analysis, and the images are subjected to processing such as conversion according to the analysis result, so that the contrast of the edges of the adhesive tape can be improved, and thus the linear detection rate and the accuracy of linear detection are improved, and the detection accuracy of the adhesive tape is improved; moreover, the image fusion with the maximum difference between the transformed image and the original image enables the detection method of the lithium battery pole piece adhesive tape to be applicable to more adhesive tape processes, and the compatibility and the application range of the detection method of the lithium battery pole piece adhesive tape are improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A detection method for lithium battery pole piece gummed paper is characterized by comprising the following steps:
obtaining a target image of a pole piece to be detected, wherein adhesive paper is arranged on the pole piece to be detected, and the target image is a color image;
aiming at a plurality of color channels of the target image, calculating the gray scale statistical characteristics of the target image under each color channel respectively, and determining 2 target color channels based on the calculated gray scale statistical characteristics;
performing preset image transformation on images corresponding to the 2 target color channels in the target image to obtain a detection gray image;
and performing linear detection based on the detection gray level image, and determining the gummed paper detection result of the pole piece to be detected according to the detected linear.
2. The method for detecting the gummed paper of the lithium battery pole piece according to claim 1, wherein the step of obtaining the target image of the pole piece to be detected further comprises the following steps:
the method comprises the steps of collecting an image of a pole piece to be detected through a camera device, and determining one or more ROI (region of interest) areas in the collected image to serve as target images of the pole piece to be detected.
3. The method for detecting the gummed paper of the lithium battery pole piece according to claim 1, wherein the plurality of color channels comprise an R color channel, a G color channel and a B color channel;
the grayscale statistical feature includes the grayscale standard deviation;
the step of calculating the grayscale statistical characteristics of the target image under each color channel respectively for the plurality of color channels of the target image, and determining 2 target color channels based on the calculated grayscale statistical characteristics further includes:
calculating the gray standard deviation of the channel image of the target image under each color channel; determining a color channel corresponding to the maximum value of the gray standard deviation corresponding to each color channel as a first color channel, and determining a color channel corresponding to the minimum value of the gray standard deviation corresponding to each color channel as a second color channel;
wherein the 2 target color channels include a first color channel and a second color channel.
4. The method for detecting the gummed paper of the lithium battery pole piece according to claim 3, wherein the step of performing preset image transformation on the images corresponding to the 2 target color channels in the target image to obtain a detection gray image further comprises:
calculating an average gray level image corresponding to the channel image under the first color channel and the channel image under the second color channel;
calculating the absolute value of the difference between the channel image under the first color channel and the average gray level image to be used as a first average difference image, and calculating the absolute value of the difference between the channel image under the second color channel and the average gray level value to be used as a second average difference image;
and summing the first average difference image and the second average difference image, and cutting a summation result to obtain a conversion image obtained by performing preset image conversion on the images corresponding to the 2 target color channels in the target image.
5. The method for detecting the gummed paper of the lithium battery pole piece according to claim 4, wherein the step of performing preset image transformation on the images corresponding to the 2 target color channels in the target image to obtain a detection gray image further comprises:
and amplifying the converted image, and determining the detection gray image based on the amplified converted image and the channel image under the first color channel.
6. The method for detecting the gummed paper of the lithium battery pole piece as claimed in claim 5, wherein the step of amplifying the converted image and determining the detection gray image based on the amplified converted image and the channel image under the first color channel further comprises:
according to the formula
Performing an enlargement process on the transformed image, wherein,
representing the transformed image after enlargement,
represents the maximum value of the grayscale averages of the channel images in a plurality of color channels,
represents the maximum value of the gray scale standard deviation of the channel images for a plurality of color channels,
representing a transformed image;
according to the formula
Determining a detection gray scale image, wherein,
a gray-scale image is represented for detection,
representing a channel image corresponding to the first color channel.
7. The method for detecting the gummed paper of the lithium battery pole piece according to claim 1, wherein the step of performing the line detection based on the detection gray image and determining the gummed paper detection result of the pole piece to be detected according to the detected line further comprises the following steps:
performing line detection on the detection gray level image based on a preset line detection algorithm, and determining one or more pieces of line information in the target image;
and calculating distance information between straight lines according to the detected one or more pieces of straight line information, and determining a gummed paper detection result of the pole piece to be detected according to the calculated distance information, wherein the gummed paper detection result comprises position information and size information of the gummed paper on the pole piece to be detected.
8. The utility model provides a detection apparatus for lithium-ion battery pole piece adhesive tape which characterized in that, the device includes:
the device comprises an image acquisition module, a detection module and a control module, wherein the image acquisition module is used for acquiring a target image of a pole piece to be detected, the pole piece to be detected is provided with adhesive paper, and the target image is a color image;
the characteristic statistical module is used for respectively calculating the gray scale statistical characteristics of the target image under each color channel aiming at a plurality of color channels of the target image, and determining 2 target color channels based on the calculated gray scale statistical characteristics;
the image transformation module is used for carrying out preset image transformation on the images corresponding to the 2 target color channels in the target image to obtain a detection gray image;
and the linear detection module is used for carrying out linear detection based on the detection gray level image and determining the gummed paper detection result of the pole piece to be detected according to the detected linear.
9. A computer device, characterized in that the computer device comprises a memory and a processor, the memory has an executable code, and when the executable code runs on the processor, the method for detecting the lithium battery pole piece gummed paper is realized according to any one of claims 1 to 7.
10. A computer-readable storage medium for storing a computer program for executing the method for detecting the lithium battery pole piece adhesive tape according to any one of claims 1 to 7.
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