CN114659453A - Method and system for detecting paint film thickness of enameled wire - Google Patents
Method and system for detecting paint film thickness of enameled wire Download PDFInfo
- Publication number
- CN114659453A CN114659453A CN202210301127.1A CN202210301127A CN114659453A CN 114659453 A CN114659453 A CN 114659453A CN 202210301127 A CN202210301127 A CN 202210301127A CN 114659453 A CN114659453 A CN 114659453A
- Authority
- CN
- China
- Prior art keywords
- main body
- identification
- color
- module
- inspection picture
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000003973 paint Substances 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000007689 inspection Methods 0.000 claims abstract description 60
- 238000007781 pre-processing Methods 0.000 claims abstract description 20
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims abstract description 10
- 230000011218 segmentation Effects 0.000 claims abstract description 8
- 239000000284 extract Substances 0.000 claims abstract description 4
- 238000004364 calculation method Methods 0.000 claims description 16
- 238000001514 detection method Methods 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000004519 manufacturing process Methods 0.000 abstract description 19
- 238000005070 sampling Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
- G01B11/0616—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material of coating
Abstract
The invention provides a method and a system for detecting the thickness of a paint film of an enameled wire, wherein the method comprises the following steps: step S1: a camera acquires a video of a copper wire after being enameled in real time, and extracts one frame in the video as an inspection picture within a specified time threshold; step S2: identifying the inspection picture to obtain an identification main body and a background main body; step S3: preprocessing the inspection picture according to the brightness and the saturation of the background main body; step S4: converting the identification main body into an HSV space, and generating a color histogram vector of the identification main body according to an HSV interval segmentation rule; step S5: the color similarity between the color template and the recognition main body is obtained according to the color histogram vector of the color template and the color histogram vector of the recognition main body, whether the color similarity is within the range of a color threshold value or not is judged, the thickness of a paint film can be detected in real time during production of the enameled wire, production accidents can be found in time, risks can be avoided in time, and the cost of the production accidents is reduced.
Description
Technical Field
The invention relates to the technical field of enameled wire production, in particular to a method and a system for detecting the thickness of a paint film of an enameled wire.
Background
In the production of the enameled wire, after the enameled wire is covered by the copper wire, a plurality of sections of the enameled wire can be selected, the paint film thickness of the enameled wire is checked by using a paint film thickness detector, and whether the enameled wire meets the produced paint film thickness or not is judged.
However, the existing inspection method adopts pure random sampling, so that whether the overall paint film thickness of the enameled wire meets the production requirement cannot be ensured, and a great space for improving quality monitoring exists.
Disclosure of Invention
Aiming at the defects, the invention aims to provide a method and a system for detecting the thickness of a paint film of an enameled wire, and solves the problems that the thickness of the paint film cannot be ensured and the quality monitoring is poor in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for detecting the thickness of a paint film of an enameled wire comprises the following steps:
step S1: a camera acquires a video of a copper wire after being enameled in real time, and extracts one frame in the video as an inspection picture within a specified time threshold;
step S2: identifying the inspection picture to obtain an identification main body and a background main body;
step S3: preprocessing the inspection picture according to the brightness and the saturation of the background subject, wherein the preprocessing comprises the step of adjusting the brightness and the saturation of the inspection picture;
step S4: converting the identification main body into an HSV space, and generating a color histogram vector of the identification main body according to an HSV interval segmentation rule;
step S5: and performing Euclidean distance calculation according to the color histogram vector of the color template and the color histogram vector of the identification subject to obtain the color similarity between the color template and the identification subject, and judging whether the color similarity is within the color threshold range.
Preferably, the time threshold value is 30 s-2 min.
Preferably, the specific steps of step S2 are as follows:
step S21: identifying the object in the inspection picture by using an One-Stage algorithm to obtain a plurality of identification frames;
step S22: and matching the plurality of identification frames by using an identification template to obtain the identification main body, and marking the region except the identification main body in the inspection picture as the background main body.
Preferably, the following steps are performed before step S1:
step S31: acquiring a standard white RGB component signal value through a camera, and dividing the acquired white RGB component signal value by a set threshold value R to obtain a resolution value of a minimum component of the minimum white RGB component signal value;
step S32: and setting a control ratio threshold, and adjusting the distance of the camera and the intensity of a shooting light source to enable the resolution value of the minimum component of the minimum white RGB component signal value in the shooting picture of the camera to fall within the control ratio threshold.
Preferably, the specific steps of step S3 are as follows:
step S31: acquiring lightness and saturation of the color template, and lightness and saturation of the background main body to obtain lightness difference and saturation difference between the color template and the background main body;
step S32: adding the brightness difference and saturation difference to the inspection picture.
Preferably, the calculation formula for the color similarity in step S5 is as follows:
the method comprises the following steps that Dist (x, y) is a comprehensive Euclidean distance between a color histogram vector of a color template and a color histogram vector of an identification main body, i is the dimension of the color histogram, and beta and alpha are adjusting coefficients and are determined by the dimension of the color histogram;
wherein
Wherein x isiAnd yiA vector representing the ith dimension of the color histogram of the color template and the ith vector of the color histogram of the identified subject, respectively, with n being the total dimension.
A paint film thickness detection system of an enameled wire uses the paint film thickness detection method of the enameled wire, and is characterized by comprising a video acquisition module, an inspection picture acquisition module, a main body identification module, a preprocessing module, a conversion module and a color similarity calculation module;
the video acquisition module is used for acquiring a video of the copper wire after being enameled in real time;
the inspection picture acquisition module is used for extracting a frame in the video within a specified time threshold value to serve as an inspection picture;
the main body identification module is used for identifying the inspection picture to obtain an identification main body and a background main body;
the preprocessing module is used for preprocessing the inspection picture according to the brightness and the saturation of the background main body;
the conversion module is used for converting the identification main body into an HSV space and generating a color histogram vector of the identification main body according to an HSV interval segmentation rule;
the color similarity calculation module is used for performing Euclidean distance calculation according to the color histogram vector of the color template and the color histogram vector of the identification main body to obtain the color similarity between the color template and the identification main body and judging whether the color similarity is within the range of a color threshold value.
Preferably, the subject identification module comprises an identification frame module and a matching module;
the identification frame module is used for identifying the object in the inspection picture by using One-Stage algorithm to obtain a plurality of identification frames;
the matching module is used for matching the plurality of identification frames by using the identification template to obtain the identification main body, and marking the region except the identification main body in the inspection picture as the background main body.
Preferably, the device also comprises a camera shooting adjusting module;
the camera shooting adjusting module is used for acquiring a standard white RGB component signal value through a camera, and dividing the acquired white RGB component signal value by a set threshold value R to obtain a resolution value of the minimum component of the minimum white RGB component signal value;
and setting a control ratio threshold, and adjusting the distance of the camera and the intensity of a shooting light source to enable the resolution value of the minimum component of the minimum white RGB component signal value in the shooting picture of the camera to fall within the control ratio threshold.
Preferably, the preprocessing module comprises a difference value obtaining module and an increasing module;
the difference value acquisition module is used for acquiring the lightness and the saturation of the color template and the lightness and the saturation of the background main body to obtain the lightness difference and the saturation difference between the color template and the background main body;
the adding module is to add the brightness difference and the saturation difference to the inspection picture.
One of the above technical solutions has the following advantages or beneficial effects: the method can detect the thickness of the paint film in real time during the production of the enameled wire, can find production accidents in time, avoid risks in time and reduce the cost of the production accidents.
Drawings
FIG. 1 is a schematic flow diagram of one embodiment of the method of the present invention.
Figure 2 is a schematic block diagram of one embodiment of the system of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
As shown in FIGS. 1-2, a method for detecting the thickness of a paint film of an enameled wire comprises the following steps:
step S1: a camera acquires a video of a copper wire after being enameled in real time, and extracts one frame in the video as an inspection picture within a specified time threshold;
step S2: identifying the inspection picture to obtain an identification main body and a background main body;
step S3: preprocessing the inspection picture according to the brightness and the saturation of the background subject, wherein the preprocessing comprises adjusting the brightness and the saturation of the inspection picture;
step S4: converting the identification main body into an HSV space, and generating a color histogram vector of the identification main body according to an HSV interval segmentation rule;
step S5: and according to the color histogram vector of the color template and the color histogram vector of the recognition main body, performing Euclidean distance calculation to obtain the color similarity of the color template and the recognition main body, and judging whether the color similarity is in a color threshold range.
In the invention, a video of the copper wire in the enameled wire is shot by the camera, and frame extraction operation is carried out on the video within a specified time threshold value to obtain an inspection picture, wherein the time threshold value can be determined according to the production speed of the enameled wire and the production scale in the enameled wire, and is generally set between 30s and 2min to ensure that the state of the enameled wire can be obtained in real time. Since the paint film has not solidified after enamelling, the paint film thickness detection cannot be carried out using a paint film thickness detection probe directly touching the enamelled wire. Therefore, the color similarity is adopted to judge whether the paint film of the enameled wire meets the production requirement. Because the colors reflected by different paint film thicknesses under light are different, the color templates with different paint film thicknesses can be obtained only by using the template to shoot. And during production, the production specification of the corresponding paint film thickness is known, the corresponding color-shooting template is selected to calculate the color similarity of the detected picture, and then the judgment can be made, and in the production process at the moment, whether the paint film thickness of the enameled wire meets the production requirement or not. The invention can increase the frequency of sampling detection without manual sampling check, thereby greatly improving the detection efficiency. Meanwhile, the method can detect the thickness of the paint film in real time during the production of the enameled wire, can find production accidents in time, avoid risks in time and reduce the cost of the production accidents. It is worth mentioning that it is a prior art to convert the identification subject into HSV space, and the interval segmentation rule is to segment the interval by artificially setting the dimension of HSV in the color histogram. For example, lightness 0 ~ 1 is one dimension, 1 ~ 2 is another dimension, and so on.
Preferably, the time threshold value is 30 s-2 min.
Preferably, the specific steps of step S2 are as follows:
step S21: identifying the object in the inspection picture by using an One-Stage algorithm to obtain a plurality of identification frames;
step S22: and matching the plurality of identification frames by using an identification template to obtain the identification main body, and marking the region except the identification main body in the inspection picture as the background main body.
The identification template is obtained by training, an enamelled copper wire model is made through an image convolution technology, and then the model is trained to obtain the identification template. According to the invention, the One-Stage algorithm is firstly used for obtaining the identification frame in the inspection picture, unnecessary identification objects are reduced, and then the identification template is used for matching the content in the identification frame to obtain the identification main body and the background main body. The method divides the inspection picture into two parts, the background subject does not have matching content in the color template, but the brightness and the saturation of the background subject can reflect the environment of the inspection picture when the inspection picture is shot, and the brightness and the saturation of the inspection picture are adjusted through the brightness and the saturation of the background subject, so that the brightness and the saturation of the inspection picture can be similar to the brightness and the saturation of the color template when the inspection picture is shot. Reducing the influence of the difference of lightness and saturation on the result of matching the recognition subject and the color template.
Preferably, the following steps are performed before step S1:
step S31: acquiring a standard white RGB component signal value through a camera, and dividing the acquired white RGB component signal value by a set threshold value R to obtain a resolution value of a minimum component of the minimum white RGB component signal value;
step S32: and setting a control ratio threshold, and adjusting the distance of the camera and the intensity of a shooting light source to enable the resolution value of the minimum component of the minimum white RGB component signal value in the shooting picture of the camera to fall within the control ratio threshold.
In this application, the threshold R is set to 255, and the minimum resolution is obtained by dividing the white RGB component signal value by the threshold R. And then setting the control ratio threshold, wherein the control ratio threshold can be determined according to the performance of the camera, and when the shooting performance of the camera is high, the control ratio threshold can be correspondingly improved so as to improve an instruction for acquiring an inspection picture and facilitate subsequent matching work. Through the adjustment of the distance of the camera and the adjustment of the intensity of the shooting light source, the white balance can be corrected, the interference of ambient light to shooting is eliminated as much as possible, the error of the checking picture and the color template caused by the white light in the environment is reduced, and the accuracy of color identification is improved.
Preferably, the specific steps of step S3 are as follows:
step S31: acquiring lightness and saturation of the color template, and lightness and saturation of the background main body to obtain lightness difference and saturation difference between the color template and the background main body;
step S32: adding the brightness difference and the saturation difference to the inspection picture.
In the invention, brightness and saturation of the check picture are required to be adjusted before the color similarity is calculated. So that the brightness and saturation in the check picture tend towards the brightness and saturation of the color template. It should be noted that before brightness and saturation are adjusted, it is further determined whether the brightness difference and the saturation difference between the background main body and the recognition main body are greater than 5%, and if so, brightness and saturation of the inspection picture are not adjusted.
Preferably, the calculation formula for the color similarity in step S5 is as follows:
the method comprises the following steps that Dist (x, y) is a comprehensive Euclidean distance between a color histogram vector of a color template and a color histogram vector of an identification main body, i is the dimension of the color histogram, and beta and alpha are adjusting coefficients and are determined by the dimension of the color histogram;
wherein
Wherein xiAnd yiA vector representing the ith dimension of the color histogram of the color template and the ith vector of the color histogram of the identified subject, respectively, with n being the total dimension.
The enameled wire paint film thickness detection system is characterized by comprising a video acquisition module, an inspection picture acquisition module, a main body identification module, a preprocessing module, a conversion module and a color similarity calculation module;
the video acquisition module is used for acquiring a video of the copper wire after being enameled in real time;
the inspection picture acquisition module is used for extracting a frame in the video within a specified time threshold value to serve as an inspection picture;
the main body identification module is used for identifying the inspection picture to obtain an identification main body and a background main body;
the preprocessing module is used for preprocessing the inspection picture according to the brightness and the saturation of the background main body;
the conversion module is used for converting the identification main body into an HSV space and generating a color histogram vector of the identification main body according to an HSV interval segmentation rule;
the color similarity calculation module is used for performing Euclidean distance calculation according to the color histogram vector of the color template and the color histogram vector of the identification main body to obtain the color similarity between the color template and the identification main body and judging whether the color similarity is within the range of a color threshold value.
Preferably, the subject identification module comprises an identification frame module and a matching module;
the identification frame module is used for identifying objects in the inspection picture by using an One-Stage algorithm to obtain a plurality of identification frames;
the matching module is used for matching the plurality of identification frames by using the identification template to obtain the identification main body, and marking the region except the identification main body in the inspection picture as the background main body.
Preferably, the system also comprises a camera shooting adjusting module;
the camera shooting adjusting module is used for acquiring a standard white RGB component signal value through a camera, and dividing the acquired white RGB component signal value by a set threshold value R to obtain a resolution value of the minimum component of the minimum white RGB component signal value;
and setting a control ratio threshold, and adjusting the distance of the camera and the intensity of a shooting light source to enable the resolution value of the minimum component of the minimum white RGB component signal value in the shooting picture of the camera to fall within the control ratio threshold.
Preferably, the preprocessing module comprises a difference value obtaining module and an increasing module;
the difference value acquisition module is used for acquiring the lightness and the saturation of the color template and the lightness and the saturation of the background main body to obtain the lightness difference and the saturation difference between the color template and the background main body;
the adding module is to add the brightness difference and the saturation difference to the inspection picture.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A method for detecting the thickness of a paint film of an enameled wire is characterized by comprising the following steps:
step S1: a camera acquires a video of a copper wire after being enameled in real time, and extracts one frame of the video within a specified time threshold value to serve as an inspection picture;
step S2: identifying the inspection picture to obtain an identification main body and a background main body;
step S3: preprocessing the inspection picture according to the brightness and the saturation of the background subject, wherein the preprocessing comprises adjusting the brightness and the saturation of the inspection picture;
step S4: converting the identification main body into an HSV space, and generating a color histogram vector of the identification main body according to an HSV interval segmentation rule;
step S5: and performing Euclidean distance calculation according to the color histogram vector of the color template and the color histogram vector of the identification subject to obtain the color similarity between the color template and the identification subject, and judging whether the color similarity is within the color threshold range.
2. The method for detecting the thickness of the paint film of the enameled wire according to claim 1, wherein the time threshold is 30 s-2 min.
3. The method for detecting the paint film thickness of the enameled wire according to claim 1, wherein the step S2 includes the following steps:
step S21: identifying the object in the inspection picture by using an One-Stage algorithm to obtain a plurality of identification frames;
step S22: and matching the plurality of identification frames by using an identification template to obtain the identification main body, and marking the region except the identification main body in the inspection picture as the background main body.
4. The method for detecting the paint film thickness of the enameled wire according to claim 1, wherein the step S1 is preceded by the following steps:
step S31: acquiring a standard white RGB component signal value through a camera, and dividing the acquired white RGB component signal value by a set threshold value R to obtain a resolution value of a minimum component of the minimum white RGB component signal value;
step S32: and setting a control ratio threshold, and adjusting the distance of the camera and the intensity of a shooting light source to enable the resolution value of the minimum component of the minimum white RGB component signal value in the shooting picture of the camera to fall within the control ratio threshold.
5. The method for detecting the paint film thickness of the enameled wire according to claim 1, wherein the step S3 includes the following steps:
step S31: acquiring lightness and saturation of the color template, and lightness and saturation of the background main body to obtain lightness difference and saturation difference between the color template and the background main body;
step S32: adding the brightness difference and saturation difference to the inspection picture.
6. The method for detecting the paint film thickness of the enameled wire according to claim 1, wherein the calculation formula for the color similarity in step S5 is as follows:
the method comprises the following steps that Dist (x, y) is a comprehensive Euclidean distance between a color histogram vector of a color template and a color histogram vector of an identification main body, i is the dimension of the color histogram, and beta and alpha are adjusting coefficients and are determined by the dimension of the color histogram;
wherein
Wherein xiAnd yiA vector representing the ith dimension of the color histogram of the color template and the ith vector of the color histogram of the identified subject, respectively, with n being the total dimension.
7. The system for detecting the paint film thickness of the enameled wire is characterized by comprising a video acquisition module, an inspection picture acquisition module, a main body identification module, a preprocessing module, a conversion module and a color similarity calculation module, wherein the video acquisition module, the inspection picture acquisition module, the main body identification module, the preprocessing module, the conversion module and the color similarity calculation module are applied to the system;
the video acquisition module is used for acquiring a video of the copper wire after being enameled in real time;
the inspection picture acquisition module is used for extracting a frame in the video within a specified time threshold value to serve as an inspection picture;
the main body identification module is used for identifying the inspection picture to obtain an identification main body and a background main body;
the preprocessing module is used for preprocessing the inspection picture according to the brightness and the saturation of the background main body;
the conversion module is used for converting the identification main body into an HSV space and generating a color histogram vector of the identification main body according to an HSV interval segmentation rule;
the color similarity calculation module is used for performing Euclidean distance calculation according to the color histogram vector of the color template and the color histogram vector of the identification main body to obtain the color similarity between the color template and the identification main body and judging whether the color similarity is within the range of a color threshold value.
8. The enameled wire paint film thickness detection system of claim 7, wherein the main body identification module comprises an identification frame module and a matching module;
the identification frame module is used for identifying the object in the inspection picture by using One-Stage algorithm to obtain a plurality of identification frames;
the matching module is used for matching the plurality of identification frames by using the identification template to obtain the identification main body, and marking the region except the identification main body in the inspection picture as the background main body.
9. The enameled wire paint film thickness detection system according to claim 8, further comprising a camera adjustment module;
the camera shooting adjusting module is used for acquiring a standard white RGB component signal value through a camera, and dividing the acquired white RGB component signal value by a set threshold value R to obtain a resolution value of the minimum component of the minimum white RGB component signal value;
and setting a control ratio threshold, and adjusting the distance of the camera and the intensity of a shooting light source to enable the resolution value of the minimum component of the minimum white RGB component signal value in the shooting picture of the camera to fall within the control ratio threshold.
10. The enameled wire paint film thickness detection system of claim 8, wherein the preprocessing module comprises a difference value acquisition module and an addition module;
the difference value acquisition module is used for acquiring the lightness and the saturation of the color template and the lightness and the saturation of the background main body to obtain the lightness difference and the saturation difference between the color template and the background main body;
the adding module is to add the brightness difference and the saturation difference to the inspection picture.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210301127.1A CN114659453A (en) | 2022-03-25 | 2022-03-25 | Method and system for detecting paint film thickness of enameled wire |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210301127.1A CN114659453A (en) | 2022-03-25 | 2022-03-25 | Method and system for detecting paint film thickness of enameled wire |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114659453A true CN114659453A (en) | 2022-06-24 |
Family
ID=82031996
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210301127.1A Pending CN114659453A (en) | 2022-03-25 | 2022-03-25 | Method and system for detecting paint film thickness of enameled wire |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114659453A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116045791A (en) * | 2023-04-03 | 2023-05-02 | 成都飞机工业（集团）有限责任公司 | Metal paint coating thickness assessment method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001144984A (en) * | 1999-11-16 | 2001-05-25 | Sharp Corp | Image processor |
| CN106767461A (en) * | 2017-02-27 | 2017-05-31 | 青岛理工大学 | A kind of face contacts lubrication experiment machine oil film thickness On-line Measuring Method |
| CN107704844A (en) * | 2017-10-25 | 2018-02-16 | 哈尔滨理工大学 | Electric power line ice-covering thickness discrimination method based on unmanned plane binocular parallax images |
| CN110440921A (en) * | 2019-07-24 | 2019-11-12 | 南京中车浦镇城轨车辆有限责任公司 | A kind of Tansducer For Color Distiguishing module and color identification method |
| US20210073970A1 (en) * | 2019-09-10 | 2021-03-11 | The Boeing Company | Method and apparatus for coating thickness inspection of a surface and coating defects of the surface |
| CN113578778A (en) * | 2021-07-27 | 2021-11-02 | 福建工程学院 | Method and system for detecting automobile glass mixed line by utilizing contour characteristic and color characteristic |
-
2022
- 2022-03-25 CN CN202210301127.1A patent/CN114659453A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001144984A (en) * | 1999-11-16 | 2001-05-25 | Sharp Corp | Image processor |
| CN106767461A (en) * | 2017-02-27 | 2017-05-31 | 青岛理工大学 | A kind of face contacts lubrication experiment machine oil film thickness On-line Measuring Method |
| CN107704844A (en) * | 2017-10-25 | 2018-02-16 | 哈尔滨理工大学 | Electric power line ice-covering thickness discrimination method based on unmanned plane binocular parallax images |
| CN110440921A (en) * | 2019-07-24 | 2019-11-12 | 南京中车浦镇城轨车辆有限责任公司 | A kind of Tansducer For Color Distiguishing module and color identification method |
| US20210073970A1 (en) * | 2019-09-10 | 2021-03-11 | The Boeing Company | Method and apparatus for coating thickness inspection of a surface and coating defects of the surface |
| CN113578778A (en) * | 2021-07-27 | 2021-11-02 | 福建工程学院 | Method and system for detecting automobile glass mixed line by utilizing contour characteristic and color characteristic |
Non-Patent Citations (4)
| Title |
|---|
| 张晶: "《数字图像处理应用研究》", 30 April 2019, 吉林大学出版社 * |
| 张森;傅圣雪;: "基于模板匹配的集装箱实时识别与定位", 科技信息(科学教研), no. 20, 10 July 2007 (2007-07-10) * |
| 李晖晖 等: "《深度学习与计算机视觉》", 31 October 2021, 西北工业大学出版社 * |
| 陈倩;潘中良;: "基于内容的服装检索系统中颜色特征提取算法的研究和改进", 激光杂志, no. 04, 25 April 2016 (2016-04-25) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116045791A (en) * | 2023-04-03 | 2023-05-02 | 成都飞机工业（集团）有限责任公司 | Metal paint coating thickness assessment method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109785316B (en) | Method for detecting apparent defects of chip | |
| CN105956618B (en) | Converter steelmaking blowing state identification system and method based on image dynamic and static characteristics | |
| CN110298840A (en) | A kind of yarn faults detection method based on image | |
| CN109993154B (en) | Intelligent identification method for single-pointer sulfur hexafluoride instrument of transformer substation | |
| CN115691026A (en) | Intelligent early warning monitoring management method for forest fire prevention | |
| CN112966571B (en) | Standing long jump flight height measurement method based on machine vision | |
| CN111611907A (en) | Image-enhanced infrared target detection method | |
| CN109520706A (en) | Automobile fuse box assembly detection system, image-recognizing method and screw hole positioning mode | |
| CN114659453A (en) | Method and system for detecting paint film thickness of enameled wire | |
| CN112598733A (en) | Ship detection method based on multi-mode data fusion compensation adaptive optimization | |
| CN106709529B (en) | Visual detection method for photovoltaic cell color difference classification | |
| CN112561899A (en) | Electric power inspection image identification method | |
| CN113688817A (en) | Instrument identification method and system for automatic inspection | |
| CN112861645A (en) | Infrared camera dim light environment compensation method and device and electronic equipment | |
| CN115639248A (en) | System and method for detecting quality of building outer wall | |
| CN116797977A (en) | Method and device for identifying dynamic target of inspection robot and measuring temperature and storage medium | |
| CN112903692B (en) | Industrial hole wall defect detection system and identification algorithm based on AI | |
| CN116630332B (en) | PVC plastic pipe orifice defect detection method based on image processing | |
| CN111046834B (en) | Monitoring video figure proportion correction method based on automatic learning analysis | |
| CN114037682A (en) | Two-dimensional automatic detection method for optical element surface defects | |
| CN108229459B (en) | Target tracking method | |
| CN111539329A (en) | Self-adaptive substation pointer instrument identification method | |
| EP4321857A1 (en) | Method and apparatus for detecting welding mark, and electronic device | |
| CN109858474A (en) | A kind of detection of transformer oil surface temperature controller and recognition methods | |
| CN115511833B (en) | Glass surface scratch detecting system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |