CN113838043A - Machine vision-based quality analysis method in metal foil manufacturing - Google Patents
Machine vision-based quality analysis method in metal foil manufacturing Download PDFInfo
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- 229910052751 metal Inorganic materials 0.000 title claims abstract description 81
- 239000002184 metal Substances 0.000 title claims abstract description 81
- 239000011888 foil Substances 0.000 title claims abstract description 68
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 27
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- 238000007689 inspection Methods 0.000 claims description 2
- 238000004949 mass spectrometry Methods 0.000 claims 2
- 238000001514 detection method Methods 0.000 abstract description 8
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
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Abstract
The invention relates to a quality analysis method based on machine vision in metal foil manufacturing. The method is suitable for the field of product quality detection. The technical scheme adopted by the invention is as follows: a quality analysis method based on machine vision in metal foil manufacturing is characterized in that: s1, acquiring front and back images of the product; s2, calculating the maximum fitting linear equation of the edges of the metal foil areas on the front and back images of the product; s3, comparing each pixel in the metal foil area with the average gray scale value of the whole metal area, and marking the pixel with the difference larger than a set threshold as a defective pixel; s4, calculating the size of the area of the connected region of each defect based on the defect point pixels; s5, inputting each detected flaw picture and the corresponding area size into a CNN intelligent classifier to identify the category of each flaw; and S6, marking the quality judgment of the metal foil according to the type and the number of the defects and the like.
Description
Technical Field
The invention relates to a quality analysis method based on machine vision in metal foil manufacturing. The method is suitable for the field of product quality detection.
Background
The metal foil is widely applied to industries such as packaging, lithium battery substrates and the like, the quality of products is expected to be increased day by day, and the quality control relates to the surface leveling uniformity and the detection control of defects such as pinholes, imprints, wrinkles, dirt, dark spots and the like.
At present, the traditional measuring mode needs a production line to run at a low speed, various defects are detected in a human eye mode, the detection precision is low, and the speed is low; moreover, because the metal foil is high in brightness and reflects light, the detection omission and the false detection are caused by the fatigue of human eyes, and the product quality is influenced.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in view of the problems, a quality analysis method based on machine vision in the metal foil manufacturing is provided.
The technical scheme adopted by the invention is as follows: a quality analysis method based on machine vision in metal foil manufacturing is characterized in that:
s1, acquiring front and back images of the product;
s2, calculating the maximum fitting linear equation of the edges of the metal foil areas on the front and back images of the product;
s3, comparing each pixel in the metal foil area with the average gray scale value of the whole metal area, and marking the pixel with the difference larger than a set threshold as a defective pixel;
s4, calculating the size of the area of the connected region of each defect based on the defect point pixels;
s5, inputting each detected flaw picture and the corresponding area size into a CNN intelligent classifier to identify the category of each flaw;
and S6, marking the quality judgment of the metal foil according to the type and the number of the defects and the like.
Step S2 includes:
s21, finding approximate position areas on two sides of the metal foil area on the front and back images of the product through an image pyramid algorithm;
s22, performing primary median filtering in the approximate position area to remove noise signals;
s23, calculating edge images of the metal area in the X direction and the Y direction by using a Prewitt operator;
s24, calculating the gradient direction and derivative of each pixel point by using the X and Y direction edge values;
s25, eliminating pixels with small edge response according to the gradient direction and the derivative, and leaving continuous skeleton lines with maximum edge response;
and S26, calculating the maximum fitting linear equation of the edge points by Hough transformation based on the skeleton line.
Further comprising:
and calculating the width of the product according to linear equations at two sides of the metal foil area, comparing the width with a standard value, and giving results such as size judgment.
A machine vision based mass analysis device in metal foil manufacturing, comprising:
the image acquisition module is used for acquiring front and back images of a product;
the edge fitting module is used for calculating a maximum fitting linear equation of the edges of the metal foil areas on the front and back images of the product;
the defect point identification module is used for comparing each pixel in the metal foil area with the average gray level value of the whole metal area and marking the pixel with the difference larger than a set threshold as a defect point pixel;
the flaw size calculation module is used for calculating the size of the area of the connected region of each flaw based on the flaw point pixels;
the flaw classification module is used for inputting each detected flaw picture and the corresponding area size into the CNN intelligent classifier to identify the category of each flaw;
and the quality judging module is used for marking the quality judgment and the like of the metal foil according to the types and the number of the flaws.
The edge fitting module includes:
the area searching module is used for searching approximate position areas on two sides of the metal foil area on the front and back side images of the product through an image pyramid algorithm;
the denoising module is used for performing primary median filtering in the approximate position area to remove noise signals;
the edge image calculation module is used for calculating edge images of the metal area in the X direction and the Y direction by using a Prewitt operator;
the gradient and derivative calculation module is used for calculating the gradient direction and derivative of each pixel point by utilizing the X and Y direction edge values;
the pixel eliminating module is used for eliminating pixels with small edge response according to the gradient direction and the derivative, and leaving continuous skeleton lines with maximum edge response;
and the edge point fitting module is used for calculating a maximum fitting linear equation of the edge point by adopting Hough transformation based on the skeleton line.
Further comprising:
and the width judging module is used for calculating the width of the product according to the linear equations at two sides of the metal foil area, comparing the width with a standard value and giving a size judging result and the like.
A storage medium having stored thereon a computer program executable by a processor, the computer program comprising: the computer program when executed implements the steps of the machine vision based quality analysis method in the manufacture of metal foil.
A data processing apparatus having a memory and a processor, the memory having stored thereon a computer program executable by the processor, characterized by: the computer program when executed implements the steps of the machine vision based quality analysis method in the manufacture of metal foil.
A machine vision based product quality inspection system, comprising:
the industrial camera I is used for acquiring a front image of a product;
the industrial camera II is used for acquiring a reverse image of the product;
the encoder is in contact with the product and used for acquiring the conveying distance of the product and providing image acquisition trigger signals for the cameras I and II after the product is conveyed for a certain distance;
a light source for illuminating a metal foil product lowered by the industrial camera;
a data processing device having a memory and a processor, the memory having stored thereon a computer program executable by the processor, the computer program when executed performing the steps of the machine vision based quality analysis method in the manufacture of metal foil.
The invention has the beneficial effects that: according to the invention, the pixel points in the metal foil area are compared with the average gray level value of the whole metal area, the pixel points with the difference larger than the set threshold value are marked as defective pixels, the defective area is calculated through the defective pixels, the defect classification is carried out based on the defective images and the defective areas, and the marking is carried out on the quality judgment and the like of the metal foil according to the type and the number of the defects, so that the product quality detection can be automatically realized, the overall efficiency is improved, and the product quality is ensured.
Drawings
Fig. 1 is a schematic flowchart illustrating steps of a machine vision-based quality analysis method for manufacturing a metal foil according to an embodiment of the present invention.
Fig. 2 is a block diagram of a mass analysis apparatus based on machine vision in manufacturing a metal foil according to an embodiment.
Detailed Description
The embodiment is a quality analysis method based on machine vision in metal foil manufacturing, which specifically comprises the following steps:
s1, acquiring front and back images of the product;
s2, calculating the maximum fitting linear equation of the edges of the metal foil areas on the front and back images of the product;
s3, comparing each pixel of the metal foil area image with the average gray scale value of the whole metal area, and marking the pixel with the difference larger than a set threshold as a defective pixel;
and S4, calculating the area size of the connected region of each defect based on the blob for the defect pixel.
S5, inputting each detected flaw picture and the corresponding area size into a CNN intelligent classifier to identify the category of each flaw;
and S6, marking the quality judgment of the metal foil according to the type and the number of the defects and the like.
In this embodiment, the maximum fitting linear equation of the edges of the metal foil regions on the front and back images of the product is calculated, which includes:
s21, finding approximate position areas on two sides of the metal foil on the front and back images of the product through an image pyramid algorithm;
s22, performing primary median filtering in the approximate position area to remove noise signals;
s23, calculating edge images of the metal foil area in the X direction and the Y direction by using a Prewitt operator;
s24, calculating the gradient direction and derivative of each pixel point by using the X and Y direction edge values;
s25, eliminating pixels with small edge response according to the gradient direction and the derivative, and leaving continuous skeleton lines with maximum edge response;
and S26, calculating the maximum fitting linear equation of the edge points by Hough transformation based on the skeleton line.
In this embodiment, the quality analysis method based on machine vision in the metal foil manufacturing further includes: and calculating the width of the product according to the linear equations of the edges at two sides of the metal foil, comparing the width with a standard value, and giving results such as size judgment.
The present embodiment also provides a quality analysis device based on machine vision in metal foil manufacturing, including: the device comprises an image acquisition module, an edge fitting module, a width judging module, a flaw point identification module, a flaw size calculation module, a flaw classification module, a quality judging module and the like.
The image acquisition module is used for acquiring front and back images of the product; the edge fitting module is used for calculating a maximum fitting linear equation of the edges of the metal foil regions on the front and back images of the product; the width judging module is used for calculating the width of the product according to linear equations at two sides of the metal foil area, comparing the width with a standard value and giving a size judging result; the defect point identification module is used for comparing each pixel in the metal foil area with the average gray level value of the whole metal area and marking the pixel with the difference larger than a set threshold as a defect point pixel; the flaw size calculation module is used for calculating the size of the area of the connected region of each flaw based on the flaw point pixels; the flaw classification module is used for inputting each detected flaw picture and the corresponding area size into the CNN intelligent classifier to identify the category of each flaw; and the quality judging module is used for marking the quality judgment and the like of the metal foil according to the types and the number of the flaws.
The edge fitting module in this embodiment includes: the device comprises a region searching module, a denoising module, an edge image calculating module, a gradient and derivative calculating module, a pixel eliminating module and an edge point fitting module.
The region searching module in the embodiment is used for searching approximate position regions on two sides of the metal foil region on the front and back images of the product through an image pyramid algorithm; the denoising module is used for performing primary median filtering in the approximate position area to remove noise signals; the edge image calculation module is used for calculating edge images of the metal area in the X direction and the Y direction by using a Prewitt operator; the gradient and derivative calculation module is used for calculating the gradient direction and derivative of each pixel point by utilizing the X and Y direction edge values; the pixel eliminating module is used for eliminating pixels with small edge response according to the gradient direction and the derivative, and leaving continuous skeleton lines with maximum edge response; the edge point fitting module is used for calculating a maximum fitting linear equation of the edge point by adopting Hough transformation based on the skeleton line.
The present embodiment also provides a storage medium having stored thereon a computer program executable by a processor, the computer program, when executed, implementing the steps of the machine vision based quality analysis method in the manufacture of a metal foil according to the present embodiment.
The present embodiment also provides a product quality detection system based on machine vision, including: the system comprises an industrial camera I, an industrial camera II, an encoder, a light source, data processing equipment and the like.
In the embodiment, the industrial camera I is used for acquiring a front image of a product; the industrial camera II is used for acquiring a reverse image of the product; the encoder is in contact with the product and is used for acquiring the conveying distance of the product and providing image acquisition trigger signals for the cameras I and II after the product is conveyed for a certain distance; the light source is used for illuminating the metal foil product placed under the industrial camera; the data processing device has a memory and a processor, the memory having stored thereon a computer program executable by the processor, the computer program when executed implementing the steps of the machine vision based quality analysis method in the manufacture of metal foil in the present embodiment.
Claims (9)
1. A quality analysis method based on machine vision in metal foil manufacturing is characterized in that:
s1, acquiring front and back images of the product;
s2, calculating the maximum fitting linear equation of the edges of the metal foil areas on the front and back images of the product;
s3, comparing each pixel in the metal foil area with the average gray scale value of the whole metal area, and marking the pixel with the difference larger than a set threshold as a defective pixel;
s4, calculating the size of the area of the connected region of each defect based on the defect point pixels;
s5, inputting each detected flaw picture and the corresponding area size into a CNN intelligent classifier to identify the category of each flaw;
and S6, marking the quality judgment of the metal foil according to the type and the number of the defects and the like.
2. The method for machine vision based quality analysis in the manufacture of metal foils according to claim 1, wherein step S2 includes:
s21, finding approximate position areas on two sides of the metal foil area on the front and back images of the product through an image pyramid algorithm;
s22, performing primary median filtering in the approximate position area to remove noise signals;
s23, calculating edge images of the metal area in the X direction and the Y direction by using a Prewitt operator;
s24, calculating the gradient direction and derivative of each pixel point by using the X and Y direction edge values;
s25, eliminating pixels with small edge response according to the gradient direction and the derivative, and leaving continuous skeleton lines with maximum edge response;
and S26, calculating the maximum fitting linear equation of the edge points by Hough transformation based on the skeleton line.
3. The machine vision-based quality analysis method in metal foil manufacturing according to claim 1 or 2, further comprising:
and calculating the width of the product according to linear equations at two sides of the metal foil area, comparing the width with a standard value, and giving results such as size judgment.
4. A machine vision based mass analysis device in metal foil manufacturing, comprising:
the image acquisition module is used for acquiring front and back images of a product;
the edge fitting module is used for calculating a maximum fitting linear equation of the edges of the metal foil areas on the front and back images of the product;
the defect point identification module is used for comparing each pixel in the metal foil area with the average gray level value of the whole metal area and marking the pixel with the difference larger than a set threshold as a defect point pixel;
the flaw size calculation module is used for calculating the size of the area of the connected region of each flaw based on the flaw point pixels;
the flaw classification module is used for inputting each detected flaw picture and the corresponding area size into the CNN intelligent classifier to identify the category of each flaw;
and the quality judging module is used for marking the quality judgment and the like of the metal foil according to the types and the number of the flaws.
5. The machine-vision-based mass spectrometry apparatus of claim 4, wherein the edge fitting module comprises:
the area searching module is used for searching approximate position areas on two sides of the metal foil area on the front and back side images of the product through an image pyramid algorithm;
the denoising module is used for performing primary median filtering in the approximate position area to remove noise signals;
the edge image calculation module is used for calculating edge images of the metal area in the X direction and the Y direction by using a Prewitt operator;
the gradient and derivative calculation module is used for calculating the gradient direction and derivative of each pixel point by utilizing the X and Y direction edge values;
the pixel eliminating module is used for eliminating pixels with small edge response according to the gradient direction and the derivative, and leaving continuous skeleton lines with maximum edge response;
and the edge point fitting module is used for calculating a maximum fitting linear equation of the edge point by adopting Hough transformation based on the skeleton line.
6. The machine vision-based mass spectrometry apparatus for use in the manufacture of metal foil according to claim 4 or 5, further comprising:
and the width judging module is used for calculating the width of the product according to the linear equations at two sides of the metal foil area, comparing the width with a standard value and giving a size judging result and the like.
7. A storage medium having stored thereon a computer program executable by a processor, the computer program comprising: the computer program when executed performs the steps of a machine vision based quality analysis method in the manufacture of a metal foil according to any one of claims 1 to 3.
8. A data processing apparatus having a memory and a processor, the memory having stored thereon a computer program executable by the processor, characterized by: the computer program when executed performs the steps of a machine vision based quality analysis method in the manufacture of a metal foil according to any one of claims 1 to 3.
9. A machine vision based product quality inspection system, comprising:
the industrial camera I is used for acquiring a front image of a product;
the industrial camera II is used for acquiring a reverse image of the product;
the encoder is in contact with the product and used for acquiring the conveying distance of the product and providing image acquisition trigger signals for the cameras I and II after the product is conveyed for a certain distance;
a light source for illuminating a metal foil product lowered by the industrial camera;
data processing device having a memory and a processor, the memory having stored thereon a computer program executable by the processor, the computer program when executed performing the steps of the machine vision based quality analysis method of manufacturing of metal foil according to any one of claims 1 to 3.
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| CN115049644A (en) * | 2022-08-12 | 2022-09-13 | 山东三微新材料有限公司 | Temperature control method and system based on aluminum pipe surface flaw identification |
| CN117351001A (en) * | 2023-11-16 | 2024-01-05 | 肇庆市大正铝业有限公司 | Surface defect identification method for regenerated aluminum alloy template |
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