CN117095160A - EL detection gray scale grading system and method - Google Patents
EL detection gray scale grading system and method Download PDFInfo
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- CN117095160A CN117095160A CN202311137369.2A CN202311137369A CN117095160A CN 117095160 A CN117095160 A CN 117095160A CN 202311137369 A CN202311137369 A CN 202311137369A CN 117095160 A CN117095160 A CN 117095160A
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- 238000002503 electroluminescence detection Methods 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000007547 defect Effects 0.000 claims abstract description 79
- 238000012545 processing Methods 0.000 claims abstract description 19
- 230000002950 deficient Effects 0.000 claims abstract description 4
- 238000011156 evaluation Methods 0.000 claims description 13
- 238000000605 extraction Methods 0.000 claims description 6
- 230000010354 integration Effects 0.000 claims description 3
- 238000003672 processing method Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 6
- 230000000694 effects Effects 0.000 abstract description 2
- 210000004027 cell Anatomy 0.000 description 15
- 238000010248 power generation Methods 0.000 description 4
- 210000003850 cellular structure Anatomy 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003333 near-infrared imaging Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
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- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
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Abstract
The application discloses an EL detection gray scale grading system and method, wherein the system comprises: the device comprises an image acquisition module, an image processing module, a defect identification module and a grading module; the image acquisition module is used for acquiring the EL image of the photovoltaic cell; the image processing module is used for processing the EL image to obtain a processed image; the defect identification module is used for identifying defects of the photovoltaic cell based on the processed images to obtain defect areas; the grading module is used for grading the defects and evaluating the defect severity of the defect area. According to the application, through parameter setting of defect types, an interception interval is set according to the size of a defective area corresponding to the gray level detected by the machine, and the defect area is evaluated, so that the optimal detection effect is achieved.
Description
Technical Field
The application belongs to the technical field of photovoltaic panel detection, and particularly relates to an EL detection gray scale grading system and method.
Background
The photovoltaic cell component is a core part of the solar power generation system, and the quality of the photovoltaic cell component directly determines the power generation performance of the solar power generation system. Therefore, the detection of photovoltaic cell modules in a solar power generation system is an indispensable procedure.
The EL detection is to design a camera bellows for shielding visible light by utilizing a near infrared imaging detection method, obtain a photovoltaic cell image connected with a constant direct current source in the camera bellows through a CCD near infrared camera, obtain a complete and clear defect detection image through computer image processing, and further find defects such as black cores, black spots, hidden cracks, broken grids and the like of the photovoltaic cell. In the conventional EL detection, only a defective piece of a normal setting parameter is detected, and therefore, an EL detection system capable of setting different interception sections has been proposed.
Disclosure of Invention
The application aims to solve the defects of the prior art, and provides an EL detection gray scale classification system and method, which utilize gray scale classification to identify and evaluate the defect area of a photovoltaic cell.
In order to achieve the above object, the present application provides the following solutions:
an EL detection gray scale gradation system comprising: the device comprises an image acquisition module, an image processing module, a defect identification module and a grading module;
the image acquisition module is used for acquiring a plurality of EL images of the photovoltaic cell;
the image processing module is used for processing a plurality of EL images to obtain processed images;
the defect identification module is used for identifying defects of the photovoltaic cell based on the processed images to obtain defect areas;
the grading module is used for grading the defects and evaluating the defect severity of the defect area.
Preferably, the image acquisition module comprises an infrared imager;
the infrared imager is used for collecting a plurality of EL images of the photovoltaic cell.
Preferably, the image processing module includes: an integrating unit and a graying unit;
the integration unit is used for splicing a plurality of EL images to obtain an integral EL image;
the graying unit is used for graying the whole EL image to obtain the processed image.
Preferably, the defect identifying module includes: a feature extraction unit and an identification unit;
the feature extraction unit is used for extracting a shadow part area from the processed image;
the identification unit is used for identifying the shadow part area to obtain a defect area.
Preferably, the grading module includes: a classification unit and an evaluation unit;
the grading unit grades the defects according to the gray scale size and the gray scale area to obtain grading conditions;
the evaluation unit is used for evaluating the defect area based on the grading condition to obtain an evaluation result.
The application also provides an EL detection gray scale grading method, which comprises the following steps:
collecting a plurality of EL images of the photovoltaic cell;
processing a plurality of EL images to obtain processed images;
identifying defects of the photovoltaic cell slice based on the processed image to obtain a defect area;
and grading the defects and evaluating the defect severity of the defect area.
Preferably, the method of treatment comprises:
splicing a plurality of the EL images to obtain an integral EL image;
and graying the whole EL image to obtain the processed image.
Preferably, the method for obtaining the defect area includes:
extracting a shadow part region based on the processed image;
and identifying the shadow part area based on the defect level to obtain a defect area.
Preferably, the method of evaluating comprises:
grading the defects according to the gray scale size and the gray scale area to obtain grading conditions;
and evaluating the defect area based on the grading condition to obtain an evaluation result.
Compared with the prior art, the application has the beneficial effects that:
according to the application, through parameter setting of defect types, an interception interval is set according to the size of a defective area corresponding to the gray level detected by the machine, and the defect area is evaluated, so that the optimal detection effect is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present application;
FIG. 2 is a flow chart of a method according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
Example 1
In this embodiment, as shown in fig. 1, an EL detection gradation system includes: the device comprises an image acquisition module, an image processing module, a defect identification module and a grading module.
The image acquisition module is used for acquiring a plurality of EL images of the photovoltaic cell, and comprises an infrared imager.
The image processing module is used for processing the image information to obtain a processed image. The image processing module includes: an integrating unit and a graying unit; the integration unit is used for splicing the plurality of EL images to obtain an integral EL image; the graying unit is used for graying the whole EL image to obtain a processed image.
Each pixel value of the gray-scale image is obtained by summing three channel values of red, green and blue of the original image under a certain weight. In this embodiment, the graying formula used is:
Gray＝0.299×R+0.587×G+0.114×B
where Gray is the pixel value after graying, R is the pixel value of the red pixel, G is the pixel value of the green pixel, and B is the pixel value of the blue pixel.
The defect identification module is used for identifying defects of the photovoltaic cell based on the processed images to obtain defect areas. The defect recognition module comprises: a feature extraction unit and an identification unit; the feature extraction unit is used for extracting a shadow part area based on the processed image; the identification unit is used for identifying the shadow part area to obtain a defect area.
The grading module is used for grading the defects and evaluating the severity of the defects of the defect area. The grading module comprises: a classification unit and an evaluation unit; the grading unit grades the defects according to the gray scale size and the gray scale area to obtain grading conditions; the evaluation unit is used for evaluating the defect area based on the grading condition to obtain an evaluation result.
In this embodiment, gray values are classified according to 28, 35, 60, 78, 255, corresponding interception area intervals are set, the gray values have little difference, and the severity of defects of the photovoltaic cell can be distinguished and judged by setting area parameters.
Example two
In a second embodiment, as shown in fig. 2, the present application further provides an EL detection gray scale grading method, which includes the following steps:
s1, collecting a plurality of EL images of the photovoltaic cell.
S2, processing the EL images to obtain processed images.
The processing method comprises the following steps: splicing a plurality of EL images to obtain an integral EL image; and graying the whole EL image to obtain a processed image.
Each pixel value of the gray-scale image is obtained by summing three channel values of red, green and blue of the original image under a certain weight. In this embodiment, the graying formula used is:
Gray＝0.299×R+0.587×G+0.114×B
where Gray is the pixel value after graying, R is the pixel value of the red pixel, G is the pixel value of the green pixel, and B is the pixel value of the blue pixel.
S3, identifying defects of the photovoltaic cell based on the processed images to obtain defect areas.
The method for obtaining the defect area comprises the following steps: extracting a shadow part region based on the processed image; and identifying the shadow part area based on the defect level to obtain a defect area.
S4, grading the defects, and evaluating the severity of the defects in the defect area.
The method for evaluating comprises the following steps: grading the defects according to the gray scale size and the gray scale area to obtain grading conditions; and evaluating the defect area based on the grading condition to obtain an evaluation result.
In this embodiment, gray values are classified according to 28, 35, 60, 78, 255, corresponding interception area intervals are set, the gray values have little difference, and the severity of defects of the photovoltaic cell can be distinguished and judged by setting area parameters.
The above embodiments are merely illustrative of the preferred embodiments of the present application, and the scope of the present application is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present application pertains are made without departing from the spirit of the present application, and all modifications and improvements fall within the scope of the present application as defined in the appended claims.
Claims (9)
1. An EL detection gray scale gradation system, characterized by comprising: the device comprises an image acquisition module, an image processing module, a defect identification module and a grading module;
the image acquisition module is used for acquiring a plurality of EL images of the photovoltaic cell;
the image processing module is used for processing a plurality of EL images to obtain processed images;
the defect identification module is used for identifying defects of the photovoltaic cell based on the processed images to obtain defect areas;
the grading module is used for grading the defects and evaluating the defect severity of the defect area.
2. The EL detection gray scale grading system of claim 1, wherein the image acquisition module comprises an infrared imager;
the infrared imager is used for collecting a plurality of EL images of the photovoltaic cell.
3. The EL detection gray scale grading system according to claim 1, wherein the image processing module comprises: an integrating unit and a graying unit;
the integration unit is used for splicing a plurality of EL images to obtain an integral EL image;
the graying unit is used for graying the whole EL image to obtain the processed image.
4. The EL detection gray scale grading system according to claim 3, wherein the defect recognition module comprises: a feature extraction unit and an identification unit;
the feature extraction unit is used for extracting a shadow part area from the processed image;
the identification unit is used for identifying the shadow part area to obtain a defect area.
5. The EL detection gray scale grading system according to claim 4, wherein the grading module comprises: a classification unit and an evaluation unit;
the grading unit grades the defects according to the gray scale size and the gray scale area to obtain grading conditions;
the evaluation unit is used for evaluating the defect area based on the grading condition to obtain an evaluation result.
6. An EL detection gradation method characterized by comprising the steps of:
collecting a plurality of EL images of the photovoltaic cell;
processing a plurality of EL images to obtain processed images;
identifying defects of the photovoltaic cell slice based on the processed image to obtain a defect area;
and grading the defects and evaluating the defect severity of the defect area.
7. The EL detection gray scale gradation method according to claim 6, wherein said processing method comprises:
splicing a plurality of the EL images to obtain an integral EL image;
and graying the whole EL image to obtain the processed image.
8. The EL detection gray scale gradation method according to claim 7, wherein the method of obtaining the defective region comprises:
extracting a shadow part region based on the processed image;
and identifying the shadow part area based on the defect level to obtain a defect area.
9. The EL detection gray scale gradation method according to claim 8, wherein the evaluation method comprises:
grading the defects according to the gray scale size and the gray scale area to obtain grading conditions;
and evaluating the defect area based on the grading condition to obtain an evaluation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311137369.2A CN117095160A (en) | 2023-09-05 | 2023-09-05 | EL detection gray scale grading system and method |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311137369.2A CN117095160A (en) | 2023-09-05 | 2023-09-05 | EL detection gray scale grading system and method |
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| Publication Number | Publication Date |
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| CN117095160A true CN117095160A (en) | 2023-11-21 |
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| CN202311137369.2A Pending CN117095160A (en) | 2023-09-05 | 2023-09-05 | EL detection gray scale grading system and method |
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- 2023-09-05 CN CN202311137369.2A patent/CN117095160A/en active Pending
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