CN115494078A - Appearance detection method for square aluminum-shell battery after being wrapped with blue film - Google Patents
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- CN115494078A CN115494078A CN202211043125.3A CN202211043125A CN115494078A CN 115494078 A CN115494078 A CN 115494078A CN 202211043125 A CN202211043125 A CN 202211043125A CN 115494078 A CN115494078 A CN 115494078A
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- 238000001514 detection method Methods 0.000 title claims abstract description 56
- 230000007547 defect Effects 0.000 claims abstract description 74
- 238000013135 deep learning Methods 0.000 claims abstract description 31
- 230000037303 wrinkles Effects 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 abstract description 3
- 238000000034 method Methods 0.000 description 12
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 5
- 229910052782 aluminium Inorganic materials 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000002950 deficient Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 239000012528 membrane Substances 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 208000029152 Small face Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 208000003464 asthenopia Diseases 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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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 invention discloses an appearance detection method of a square aluminum-shell battery after being wrapped with a blue film, which comprises the following steps: s1: the camera device collects an appearance image of the battery, wherein the battery is coated with a blue film; s2: inputting the appearance image of the battery into a deep learning network model for defect detection to obtain defect parameters; s3: and comparing and judging the defect parameters with preset standard parameters to obtain the appearance detection result of the battery. Through the mode, the visual detection of the appearance of the blue film of the battery is realized, compared with the existing manual detection mode, the detection efficiency can be greatly improved, the labor time is saved, and the situations of false detection and detection omission caused by artificial subjective factors are avoided.
Description
Technical Field
The invention relates to the technical field of lithium batteries, in particular to an appearance detection method of a square aluminum-shell battery after being coated with a blue film.
Background
A layer of blue film is required to be coated on the aluminum shell before the square aluminum shell battery is produced and off-line, so that the square aluminum shell battery has the effects of insulation, water resistance, attractiveness and the like. In some batteries, various appearance defects such as bubbles, pits, wrinkles, scratches, etc. may occur in the blue film, which affects the normal use of the battery, and therefore, appearance inspection needs to be performed after the blue film is wrapped in the square aluminum-casing battery.
The existing detection for the appearance of the blue film of the battery is usually a manual detection mode, the manual detection mode needs to consume more manpower time, the detection efficiency is low, and the situations of false detection and detection omission occur due to subjective factors such as visual fatigue and the like.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a method for detecting the appearance of a square aluminum-shell battery coated with a blue film, which can solve the technical problems.
(II) technical scheme
In order to solve the technical problems, the invention provides the following technical scheme: a method for detecting the appearance of a square aluminum-shell battery coated with a blue film comprises the following steps:
s1: the camera device collects an appearance image of the battery, wherein the battery is coated with a blue film;
s2: inputting the appearance image of the battery into a deep learning network model for defect detection to obtain defect parameters;
s3: and comparing and judging the defect parameters with preset standard parameters to obtain the appearance detection result of the battery.
Preferably, the appearance image of the battery includes a large-side appearance image of the battery, a small-side appearance image of the battery, a bottom-side appearance image of the battery, and a top-side appearance image of the battery.
Preferably, the top surface of the battery is provided with a pole, and the top surface appearance image comprises a pole image.
Preferably, the camera device is a line scan camera.
Preferably, the defect detection in step S2 is specifically: and performing defect feature extraction and feature classification on the appearance image of the battery by using the deep learning network model.
Preferably, the defect parameters include a classification of the defect, a specification size of the defect, and a coordinate location of the defect.
Preferably, the classification of defects includes bubbles, pits, wrinkles, and scratches.
Preferably, step S3 is to compare the specification size of the defect with a standard parameter for determining, so as to obtain an appearance detection result of the battery.
Preferably, the appearance detection method of the square aluminum-shell battery after being wrapped with the blue film further comprises the following steps: collecting a defect image sample; marking each defect feature on the defect image sample; inputting the marked defect image samples into a plurality of different deep learning network models to be selected for model training; and comparing the defect detection results output by the deep learning network models to be selected to determine one deep learning network model to be selected as the deep learning network model adopted in the step S2.
Preferably, after determining the deep learning network model, the method further includes: and continuously acquiring defect image samples to perform model iteration on the deep learning network model.
(III) advantageous effects
Compared with the prior art, the appearance detection method of the square aluminum-shell battery coated with the blue film has the following beneficial effects: according to the invention, the appearance image of the battery is acquired through the camera device, the appearance image of the battery is further input into the deep learning network model for defect detection to obtain defect parameters, and finally the defect parameters are compared with the preset standard parameters to judge to obtain the appearance detection result of the battery, so that the visual detection of the blue membrane appearance of the battery is realized.
Drawings
Fig. 1 is a flowchart illustrating steps of an appearance inspection method of a square aluminum-casing battery after being coated with a blue film according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
The invention provides an appearance detection method of a square aluminum-shell battery after being wrapped with a blue film, which comprises the following steps:
s1: the camera device collects an appearance image of the battery.
The battery is specifically a square aluminum shell battery, and an aluminum shell of the battery is coated with a blue film. The cell has two large faces, two small faces (also called side faces, the area of which is smaller than that of the large faces), a bottom face and a top face. Specifically, the appearance image of the battery includes a large-area appearance image of the battery, a small-area appearance image of the battery, a bottom-area appearance image of the battery, and a top-area appearance image of the battery. In addition, the top surface of battery is equipped with utmost point post, and top surface outward appearance image can include utmost point post image, the utmost point post that is used for detecting the battery correspondingly.
Preferably, the camera Device is a line scan camera, i.e. a line scan camera, which is a camera using a line image sensor such as a linear array CCD (Charge coupled Device), and the present invention may specifically use a 4K line scan camera.
S2: and inputting the appearance image of the battery into a deep learning network model for defect detection to obtain defect parameters.
The defect detection in the step S2 is specifically: and performing defect feature extraction and feature classification on the appearance image of the battery by using the deep learning network model, wherein the feature extraction comprises the positioning of defects on the appearance image, and the feature classification comprises the determination of the specific classification to which the defects belong.
S3: and comparing and judging the defect parameters with preset standard parameters to obtain the appearance detection result of the battery.
The defect parameters may include a classification of the defect, a gauge size of the defect, and a coordinate location of the defect. The classification of defects may specifically include bubbles, pits, wrinkles, scratches, and the like. The step S3 may specifically be comparing and determining the specification size of the defect with the standard parameter to obtain an appearance detection result of the battery. For example, the length standard parameter of the wrinkle is 0.5mm, and when the length of the wrinkle is detected to be >0.5mm, the appearance of the battery is detected as a defective product.
In addition, the defect parameters may further include the number of defects, and the specification size of the defects and the number of the defects may be determined in combination, for example, if the number of the bubbles having the diameter standard parameter of 5mm and 5mm is 0, and if the diameter of the bubbles is determined to be greater than 5mm and the number of the bubbles greater than 5mm on a single surface of the battery is determined to be greater than 0, the appearance detection result of the battery corresponds to a defective product; or when the diameter of the bubbles with the diameter of 2mm or more is less than 5mm and the number of the bubbles with the specification on the single surface of the battery is more than 3, the appearance detection result of the battery is correspondingly unqualified; or the diameter standard parameter of the pits is 2mm, the number standard parameter of the pits is 3 pcs/surface, and when the diameter of the pits is larger than or equal to 2mm and the number of the pits is larger than or equal to 3 pcs/surface, the appearance detection result of the battery is correspondingly unqualified.
In addition, the defect can be classified into breakage (unqualified if being more than 0.5 mm), flanging tilting (unqualified if being provided), flatness (highest point-lowest point, unqualified if being more than 0.03 mm); when the image of the pole is detected, the defect is classified into scratches and damages of the pole, and if the scratches and/or the damages of the pole are detected, the image is determined to be unqualified.
In addition, the appearance detection method of the square aluminum-shell battery after being coated with the blue film further comprises the following steps: collecting a defect image sample; marking each defect feature on the defect image sample; inputting the marked defect image samples into a plurality of different deep learning network models to be selected for model training, so that defect characteristics can be classified; and comparing the defect detection results output by the deep learning network models to be selected to determine that one of the deep learning network models to be selected is the deep learning network model adopted in the step S2, namely comparing the truth results of the defect judgment output by the deep learning network models and selecting the optimal model by combining artificial re-judgment. In the above model training, it may be specifically noise learning to enhance the fault tolerance of the model using noisy data. In the above defect image sample collection, a dynamic sampling algorithm may be specifically adopted: in general, the accuracy of model prediction is reduced if the input defects are unevenly distributed and the quantity proportion is not adjusted in the model training process, and the purpose of the dynamic sampling algorithm is to weaken the problems. The deep learning network model may be specifically a CNN, a DBN, an RNN, an RNTN, and the like in the prior art.
In addition, in order to improve the accuracy of the identification and determination of the deep learning network model, after determining the deep learning network model, the method may further include: and continuously acquiring defect image samples to perform model iteration on the deep learning network model and perform parameter adjustment on the deep learning network model.
Compared with the prior art, the invention provides the appearance detection method of the square aluminum-shell battery after being wrapped with the blue film, which has the following beneficial effects: according to the invention, the appearance image of the battery is acquired through the camera device, the appearance image of the battery is further input into the deep learning network model for defect detection to obtain defect parameters, and finally the defect parameters are compared and judged with the preset standard parameters to obtain the appearance detection result of the battery, so that the visual detection of the blue membrane appearance of the battery is realized.
It is to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The appearance detection method of the square aluminum-shell battery after being wrapped with the blue film is characterized by comprising the following steps of:
s1: a camera device collects an appearance image of a battery, wherein the battery is coated with a blue film;
s2: inputting the appearance image of the battery into a deep learning network model for defect detection to obtain defect parameters;
s3: and comparing and judging the defect parameters with preset standard parameters to obtain an appearance detection result of the battery.
2. The appearance detection method of the square aluminum-shell battery after being wrapped with the blue film according to claim 1, characterized in that: the appearance image of the battery includes a large-side appearance image of the battery, a small-side appearance image of the battery, a bottom-side appearance image of the battery, and a top-side appearance image of the battery.
3. The appearance detection method of the square aluminum-shell battery after being wrapped with the blue film according to claim 2, characterized in that: the top surface of battery is equipped with utmost point post, top surface outward appearance image includes utmost point post image.
4. The appearance detection method of the square aluminum-shell battery after being coated with the blue film according to claim 1, characterized in that: the camera device is a line scan camera.
5. The appearance detection method of the square aluminum-shell battery after being wrapped with the blue film according to claim 1, wherein the defect detection in the step S2 is specifically as follows: and performing defect feature extraction and feature classification on the appearance image of the battery by using the deep learning network model.
6. The appearance detection method of the square aluminum-shell battery after being coated with the blue film according to claim 5, characterized in that: the defect parameters include classification of the defect, specification size of the defect, and coordinate location of the defect.
7. The appearance detection method of the square aluminum-shell battery after being wrapped with the blue film according to claim 6, characterized in that: the classification of defects includes bubbles, pits, wrinkles, and scratches.
8. The appearance detection method of the square aluminum-shell battery after being wrapped with the blue film according to claim 6, characterized in that: the step S3 is specifically to compare and determine the specification size of the defect with the standard parameter to obtain an appearance detection result of the battery.
9. The appearance detection method of the square aluminum-shell battery after being wrapped with the blue film according to claim 1, further comprising the steps of: collecting a defect image sample; marking each defect feature on the defect image sample; inputting the marked defect image samples into a plurality of different deep learning network models to be selected for model training; comparing the defect detection results output by the deep learning network models to be selected to determine that one of the deep learning network models to be selected is the deep learning network model adopted in the step S2.
10. The appearance detection method of the square aluminum-shell battery after being wrapped with the blue film according to claim 9, characterized in that: after determining the deep learning network model, further comprising: and continuously acquiring the defect image samples to perform model iteration on the deep learning network model.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116363125A (en) * | 2023-05-30 | 2023-06-30 | 厦门微图软件科技有限公司 | Deep learning-based battery module appearance defect detection method and system |
| CN116385444A (en) * | 2023-06-06 | 2023-07-04 | 厦门微图软件科技有限公司 | Blue film appearance defect detection network for lithium battery and defect detection method thereof |
| CN116542980A (en) * | 2023-07-06 | 2023-08-04 | 宁德时代新能源科技股份有限公司 | Defect detection method, defect detection apparatus, defect detection program, storage medium, and defect detection program |
| CN117409007A (en) * | 2023-12-15 | 2024-01-16 | 深圳市什方智造科技有限公司 | Method, device, equipment and medium for determining laminating degree of battery heating film |
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| CN116363125A (en) * | 2023-05-30 | 2023-06-30 | 厦门微图软件科技有限公司 | Deep learning-based battery module appearance defect detection method and system |
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| CN116542980A (en) * | 2023-07-06 | 2023-08-04 | 宁德时代新能源科技股份有限公司 | Defect detection method, defect detection apparatus, defect detection program, storage medium, and defect detection program |
| CN116542980B (en) * | 2023-07-06 | 2023-11-03 | 宁德时代新能源科技股份有限公司 | Defect detection method, defect detection apparatus, defect detection program, storage medium, and defect detection program |
| CN117409007A (en) * | 2023-12-15 | 2024-01-16 | 深圳市什方智造科技有限公司 | Method, device, equipment and medium for determining laminating degree of battery heating film |
| CN117409007B (en) * | 2023-12-15 | 2024-04-12 | 深圳市什方智造科技有限公司 | Method, device, equipment and medium for determining laminating degree of battery heating film |
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