CN110146508B - Material shortage detection method - Google Patents
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- CN110146508B CN110146508B CN201910371307.5A CN201910371307A CN110146508B CN 110146508 B CN110146508 B CN 110146508B CN 201910371307 A CN201910371307 A CN 201910371307A CN 110146508 B CN110146508 B CN 110146508B
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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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- 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
- G01N2021/8854—Grading and classifying of flaws
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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
- G01N2021/8887—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 based on image processing techniques
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Abstract
The application provides a starved material detection method for detecting starved material areas of battery materials, the starved material detection method comprises the following steps: acquiring a current image of a battery material; identifying the type of the starved area in the current image, and acquiring the information of the starved area; judging whether a first starved area exists in the starved area or not; when the first starved area exists, judging whether the first starved area is continuous with a pre-stored second starved area; when the first starved area is continuous with the second starved area, splicing the first starved area and the second starved area to form a splicing area; and acquiring and outputting the information of the splicing area. According to the method and the device, the defect areas which are not completely displayed are spliced, so that the information of the large-size starved areas can be accurately detected.
Description
Technical Field
The application relates to the field of battery manufacturing, in particular to a material shortage detection method.
Background
The lamination of the pole piece of the battery needs to laminate the material on the PET film to the pole piece through a rolling roller, and the material to be laminated is not transferred to the pole piece from the PET film due to the tension, foreign matters on the rolling roller, gaps and the like in the lamination process, so that the phenomenon of material shortage is generated. Pole piece large tracts of land lacks the material can lead to the electric core energy density who produces uneven, influences the battery performance, consequently needs to detect and mark out the position that lacks the material before reaching follow-up station, especially the large tracts of land lacks the material. The existing detection method adopts a line scanning camera to shoot pole piece images for material shortage detection, but the detection of each image is independent, and once the size of a material shortage area exceeds that of a single image, the complete size of the material shortage area cannot be obtained.
Disclosure of Invention
The application provides a starved feed detection method for detecting a large-size starved feed area.
The application provides a starved material detection method for detecting starved material areas of battery materials, the starved material detection method comprises the following steps: acquiring a current image of a battery material; identifying the type of the starved area in the current image, and acquiring the information of the starved area; judging whether a first starved area exists in the starved area or not; when the first starved feeding area exists, judging whether the first starved feeding area is continuous with a pre-stored second starved feeding area; when the first starved area is continuous with the second starved area, splicing the first starved area and the second starved area to form a splicing area; and acquiring and outputting the information of the splicing area.
Optionally, the first material shortage region extends to a first boundary of a corresponding image, the second material shortage region extends to a second boundary of a corresponding image, the first boundary of the current image and the second boundary of the previous image are adjacently arranged, and the material shortage detection method includes: after the first starved area and the second starved area are spliced to form a spliced area, judging whether the spliced area extends to a second boundary of the current image; when the splicing area does not extend to a second boundary of the current image, outputting the information of the splicing area; and when the splicing area extends to the second boundary of the current image, storing the information of the splicing area.
Optionally, the pre-stored second material shortage region is a second material shortage region of a previous image or a splicing region formed by splicing.
Optionally, the acquiring information of the splicing area includes: and acquiring the information of the splicing area according to the information of the first starved area and the second starved area.
Optionally, the information of the starved area includes boundary position information of the starved area.
Optionally, the determining whether the first starved area and a pre-stored second starved area are continuous includes: and judging whether a first boundary of the first starved area is overlapped with a second boundary of the second starved area.
Optionally, the determining whether the first boundary of the first starved area and the second boundary of the second starved area coincide includes: and judging whether the coordinate of the first end point is the same as the coordinate of the third end point and whether the coordinate of the second end point is the same as the coordinate of the fourth end point.
Optionally, the material shortage detection method includes: when judging whether a first starved area exists in the starved areas, judging whether a second starved area and a third starved area exist; when a second starved area exists, storing information of the second starved area; and when the third starved area exists, outputting the information of the third starved area.
Optionally, the material shortage detection method includes: and when the first material shortage region extends to a second boundary of the current image, storing the information of the current image and the first material shortage region.
Optionally, the material shortage detection method includes: and classifying the starved areas after judging whether a first starved area, a second starved area and a third starved area exist.
According to the method and the device, the defect regions which are not completely displayed are spliced, so that the information of the large-size material shortage region can be accurately detected.
Drawings
FIG. 1 is a schematic flow chart diagram of one embodiment of the starved feed detection method of the present application;
FIG. 2 is a schematic view of a current image obtained by the starvation detection method of the present application;
FIG. 3 is a schematic diagram of a current image and a previous image obtained by the starved feed detection method of the present application;
FIG. 4 is a schematic view of a spliced area formed by splicing the first starved area and the second starved area shown in FIG. 3;
fig. 5 is a schematic view of another embodiment of the splicing area of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. Where the following description refers to the accompanying drawings, corresponding numbers in different drawings indicate corresponding or analogous elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of devices consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like, as used in the description and in the claims, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Also, the use of the terms a, an, etc. do not denote a limitation of quantity, but rather denote the presence of at least one. "plurality" or "a number" means two or more. The word "comprising" or "comprises", and the like, means that the element or item listed after "comprises" or "comprising" is inclusive of the element or item listed after "comprising" or "comprises", and the equivalent thereof, and does not exclude additional elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. As used in this specification 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 and all possible combinations of one or more of the associated listed items.
The application provides a material shortage detection method which is used for detecting the size and the position of a material shortage area of a battery material. In this embodiment, the battery material 1 is exemplified by a pole piece.
Referring to fig. 1, the method for detecting material shortage includes:
step S1: a current image of the battery material is obtained.
Optionally, the detection is performed by a CCD detection system. The CCD detection system comprises a linear array camera and a processing unit electrically connected with the linear array camera, wherein the linear array camera is used for acquiring a current image of a battery material, and the processing unit is used for identifying, processing and calculating the current image. Since the line camera scans continuously line by line, the current image is continuous with the previous image and the next image, and thus, a plurality of images are connected in sequence.
Step S2: and identifying the material shortage region in the current image and acquiring the information of the material shortage region.
The gray scale of the current image can be identified through the processing unit of the CCD detection system, the material shortage region can be identified according to the difference between the gray scale of the material shortage region and the gray scale of the battery material, and the information of each material shortage region can be acquired simultaneously, wherein the information of the material shortage region can be boundary position information of the material shortage region, size information of the material shortage region, and the combination of the boundary position information and the size information. The size information may be obtained by boundary position information, for example, by determining the size information of the starved area from position information of each point on the boundary of the starved area.
It should be noted that the number of starved areas may be zero, one, or multiple. Of course, the detection is ended if the number of starved areas is zero.
Step S3: and judging whether a first starved area exists in the starved area or not.
Referring to fig. 2, in one embodiment, it is determined whether there is a second starved area 13 and a third starved area 14 while determining whether there is a first starved area 11 in the starved area. When the second starved area 13 exists, the current image and information of the second starved area 13 are stored. When a third starved area 13 is present, it is optional to output the information of the third starved area 13 at this time.
Based on the determination result, the starved area may be classified into three types, i.e., a first starved area 11, a second starved area 13, and a third starved area 14, and the number of types of starved areas may be one, two, or three. The first starved area 11, 12 extends to a first boundary 101 of the corresponding image, the second starved area 13 extends to a second boundary 102 of the corresponding image, and the third starved area 14 is located between the first boundary 101 and the second boundary 102 of the corresponding image, where the corresponding image of the first starved area 11, 12, the second starved area 13, and the third starved area 14 is the current image. Similarly, the previous image and the subsequent image also have a first starved area, a second starved area, and a third starved area. It will be appreciated that an image may not fully display the starved areas, such as the first starved areas 11, 12 and the second starved area 13.
In this embodiment, the first boundary is a boundary of an upward image, the second boundary is a boundary of a downward image, and the first boundary of the current image and the second boundary of the previous image are adjacently disposed. In the direction of the image of fig. 2, the first boundary is the upper boundary and the second boundary is the lower boundary.
Step S4: and when the first starved area exists, judging whether the first starved area is continuous with a pre-stored second starved area.
Optionally, the pre-stored second material shortage region is a second material shortage region of a previous image or a splicing region formed by splicing.
Taking a second material shortage region with a pre-stored second material shortage region as a previous image as an example, because the current image is continuous with the previous image, if the first material shortage region is continuous with the second material shortage region, it is indicated that the first material shortage region and the second material shortage region are two parts of a complete material shortage region, and the previous image refers to a previous image directly acquired by the linear array camera. Referring to fig. 3, in one embodiment, whether the first boundary 111 of the first starved area 11 and the second boundary 131a of the second starved area 13a are overlapped can be determined.
In one embodiment, whether the first boundary 111 of the first starved area 11 coincides with the second boundary 131a of the second starved area 13a may be determined by whether the coordinates are the same. The first boundary 111 of the first starved area 11 includes a first end point a and a second end point B, and the second boundary 131a of the second starved area 13a includes a third end point C and a fourth end point D. If the coordinate of the first endpoint a is the same as the coordinate of the third endpoint C, and the coordinate of the second endpoint B is the same as the coordinate of the fourth endpoint D, it is determined that the first boundary 111 of the first starved area 11 coincides with the second boundary 131a of the second starved area 13 a; otherwise, the two are not coincident. The coordinates here may refer to coordinates in one direction, for example, coordinates in the extending direction of the first boundary 101 of the current image.
Step S5: and when the first starved area is continuous with the second starved area, splicing the first starved area and the second starved area to form a splicing area.
When the first starved area 11 is continuous with the second starved area 13a, since the first starved area 11 does not extend to the second boundary of the corresponding image, it indicates that the spliced area formed by splicing the first starved area 11 and the second starved area 13a is a fully displayed starved area, and at this time, it may be selected to directly acquire and output information of the spliced area, such as boundary position information and size information.
Referring to fig. 2, when the first starved area 12 extends to the second boundary 102 of the current image, the information of the first starved area 12 is stored, and the first starved area 12 at this time may be called as the second starved area pre-stored in step S4. When the first starved area does not extend to the second boundary of the current image, the first starved area is known to be a completely displayed starved area, and information of the first starved area, such as boundary position information and size, can be directly output.
Step S6: and judging whether the splicing area extends to a second boundary of the current image.
Referring to fig. 3 and 4, after the first starved area 11 and the second starved area 13a are spliced, the formed spliced area 100 does not extend to the second boundary (i.e., the second boundary 102 of the current image) of the current image, and the information of the spliced area is obtained and output.
Referring to fig. 5, the first material shortage region and the second material shortage region are spliced to form a spliced region 100a, and the spliced region 100a extends to the second boundary 102b of the current image.
Step S7: and outputting the information of the splicing area.
When the splicing area does not extend to the second boundary of the current image, the information of the splicing area 100 can be directly obtained according to the information of the first starved area 11 and the information of the second starved area 13a, and the information of the splicing area 100, such as the boundary position information and the size information of the splicing area 100, is output.
When the formed splicing area 100a where the first run-out area is spliced with the second run-out area extends to the second boundary 102b of the current image, information of the splicing area 100a is stored for recall in step S4.
Of course, the first starved area 12 may also extend to the second boundary of the corresponding image, there is no starved area in the next image that is continuous with the first starved area 12, the first starved area 12 is actually a fully displayed starved area, and at this time, information accumulated in the first starved area 12 is output.
According to the method and the device, the defect areas which are not completely displayed are spliced, so that the information of the large-size starved areas can be accurately detected.
The above description is only a preferred embodiment of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (8)
1. A starved charge detection method is used for detecting starved charge areas of battery materials, and is characterized in that: the material shortage detection method comprises the following steps:
acquiring a current image of a battery material;
identifying the type of the starved area in the current image, and acquiring the information of the starved area;
judging whether a first starved area exists in the starved area or not;
when the first starved feeding area exists, judging whether the first starved feeding area is continuous with a pre-stored second starved feeding area; the first starved area extends to a first boundary of a corresponding image, the second starved area extends to a second boundary of a corresponding image, and the first boundary of the current image and the second boundary of the previous image are arranged adjacently; the current image is consecutive to the previous image;
when the first starved area is continuous with the pre-stored second starved area, splicing the first starved area with the pre-stored second starved area to form a spliced area;
acquiring and outputting information of the splicing area;
the judging whether the first material shortage region is continuous with a pre-stored second material shortage region comprises the following steps:
judging whether a part of the first starved area, which is positioned on the first boundary, is overlapped with a pre-stored part of the second starved area, which is positioned on the second boundary;
the determining whether the part of the first starved area located on the first boundary and the part of the second starved area located on the second boundary are overlapped includes: and judging whether the coordinate of the first endpoint is the same as the coordinate of the third endpoint and whether the coordinate of the second endpoint is the same as the coordinate of the fourth endpoint.
2. The starved feed detection method of claim 1, wherein: the material shortage detection method comprises the following steps: after the first starved area and the second starved area are spliced to form a splicing area,
judging whether the splicing area extends to a second boundary of the current image or not;
when the splicing area does not extend to a second boundary of the current image, outputting the information of the splicing area; and when the splicing area extends to the second boundary of the current image, storing the information of the splicing area.
3. The starved charge detection method of claim 2, wherein: the pre-stored second material shortage region is a second material shortage region of a previous image or a splicing region formed by splicing.
4. A starvation detection method according to any of claims 1 to 3, wherein: the acquiring information of the splicing area includes:
and acquiring the information of the splicing area according to the information of the first starved area and the second starved area.
5. A starvation detection method according to any of claims 1 to 3, wherein: the information of the starved area includes boundary position information of the starved area.
6. A starvation detection method according to any of claims 1 to 3, wherein: the material shortage detection method comprises the following steps:
when judging whether a first starved area exists in the starved areas, judging whether a second starved area and a third starved area exist, wherein the third starved area is positioned between a first boundary and a second boundary of the corresponding images;
when a second starved area exists, storing information of the second starved area;
and when the third starved area exists, outputting the information of the third starved area.
7. The starved charge detection method of claim 6, wherein: the material shortage detection method comprises the following steps:
and when the first material shortage region extends to a second boundary of the current image, storing the information of the current image and the first material shortage region.
8. The starved charge detection method of claim 6, wherein: the material shortage detection method comprises the following steps: and classifying the starved areas after judging whether a first starved area, a second starved area and a third starved area exist.
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| CN201910371307.5A CN110146508B (en) | 2019-05-06 | 2019-05-06 | Material shortage detection method |
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| CN201910371307.5A CN110146508B (en) | 2019-05-06 | 2019-05-06 | Material shortage detection method |
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Citations (3)
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| CN202075719U (en) * | 2011-05-19 | 2011-12-14 | 上海科睿展览展示工程科技有限公司 | Multi-channel multi-point touch system |
| CN105044126A (en) * | 2015-07-22 | 2015-11-11 | 瑞安市质量技术监督检测院 | Visual detection system for large-width continuous surface defects |
| CN106570935A (en) * | 2016-09-09 | 2017-04-19 | 北京林业大学 | Vegetation distribution simulation method and apparatus |
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| CN202075719U (en) * | 2011-05-19 | 2011-12-14 | 上海科睿展览展示工程科技有限公司 | Multi-channel multi-point touch system |
| CN105044126A (en) * | 2015-07-22 | 2015-11-11 | 瑞安市质量技术监督检测院 | Visual detection system for large-width continuous surface defects |
| CN106570935A (en) * | 2016-09-09 | 2017-04-19 | 北京林业大学 | Vegetation distribution simulation method and apparatus |
Non-Patent Citations (1)
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| "桥梁拉索表面缺陷图像检测关键技术的研究";李新科;《中国博士学位论文全文数据库 信息科技辑》;20141115;论文摘要、论文正文第71-85页 * |
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