CN114463589A - A method for selecting different grades of temperature-indicating paint in a specific temperature range - Google Patents
A method for selecting different grades of temperature-indicating paint in a specific temperature range Download PDFInfo
- Publication number
- CN114463589A CN114463589A CN202210125050.7A CN202210125050A CN114463589A CN 114463589 A CN114463589 A CN 114463589A CN 202210125050 A CN202210125050 A CN 202210125050A CN 114463589 A CN114463589 A CN 114463589A
- Authority
- CN
- China
- Prior art keywords
- temperature
- color
- different
- different grades
- indicating
- 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.)
- Granted
Links
- 239000003973 paint Substances 0.000 title claims abstract description 96
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000009529 body temperature measurement Methods 0.000 claims abstract description 33
- 238000012360 testing method Methods 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims abstract description 10
- 238000010801 machine learning Methods 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims 1
- 230000011218 segmentation Effects 0.000 claims 1
- 238000010304 firing Methods 0.000 abstract description 3
- 239000003086 colorant Substances 0.000 description 2
- 238000003066 decision tree Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2431—Multiple classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Spectrometry And Color Measurement (AREA)
Abstract
A method for selecting temperature indicating paints of different brands in a specific temperature interval comprises the following steps: in a specific temperature interval, firing standard sample plates of different grades of temperature indicating paint at different temperature points; extracting the color characteristics of standard sample plates of different grades of temperature indicating paint at different temperature points; clustering color features of different-brand temperature indicating paint training sets to obtain color segments, establishing a color segment identification model according to color feature data in each color segment, and establishing a temperature identification model of each color segment; color section identification is carried out on color characteristic data of different-brand temperature indicating paint test sets through a color section identification model, temperature identification is further carried out through a corresponding temperature identification model, an identification temperature value is compared with a corresponding temperature point, and the temperature measurement accuracy rates of different temperature points are counted; and dividing a specific temperature interval to obtain temperature sections, so that the temperature measurement accuracy of at least one mark temperature indicating paint at different temperature points in each temperature section meets the requirement of temperature measurement accuracy.
Description
Technical Field
The application belongs to the technical field of different-brand temperature indicating paints in a specific temperature interval and particularly relates to a method for selecting different-brand temperature indicating paints in the specific temperature interval.
Background
The temperature indicating paint is used for measuring the surface temperature of the part, different temperature indicating paints of different grades have different optimal temperature measuring sections, and the temperature measuring accuracy rates at different temperature points have differences.
Currently, when the temperature in a specific temperature interval is measured, a technician mainly selects one or more temperature indicating paints from a limited number according to experience to measure the temperature.
The present application has been made in view of the above-mentioned technical drawbacks.
It should be noted that the above background disclosure is only for the purpose of assisting understanding of the inventive concept and technical solutions of the present invention, and does not necessarily belong to the prior art of the present patent application, and the above background disclosure should not be used for evaluating the novelty and inventive step of the present application without explicit evidence to suggest that the above content is already disclosed at the filing date of the present application.
Disclosure of Invention
The application aims to provide a method for selecting different-grade temperature indicating paints in a specific temperature interval so as to overcome or alleviate at least one technical defect in the known prior art.
The technical scheme of the application is as follows:
a method for selecting temperature indicating paints of different brands in a specific temperature interval comprises the following steps:
in a specific temperature interval, firing standard sample plates of different grades of temperature indicating paint at different temperature points;
extracting the color characteristics of standard sample plates of different grades of temperature indicating paint at different temperature points;
dividing color characteristics of temperature indicating paints of different grades at different temperature points into a training set and a testing set;
clustering the color characteristics of the temperature indicating paint training sets with different brands to obtain color segments;
for different grades of temperature indicating paints, establishing a color section identification model according to color characteristic data in each color section;
for different grades of temperature indicating paints, establishing a temperature identification model of each color section according to color characteristic data in each color section;
color section identification is carried out on color characteristic data of different-brand temperature indicating paint test sets through a color section identification model, temperature identification is further carried out through a corresponding temperature identification model, an identification temperature value is compared with a corresponding temperature point, and the temperature measurement accuracy rates of different temperature points are counted;
and dividing a specific temperature interval to obtain temperature sections, so that the temperature measurement accuracy of at least one mark temperature indicating paint at different temperature points in each temperature section meets the requirement of temperature measurement accuracy.
According to at least one embodiment of the present application, in the method for selecting different-grade temperature paints in the specific temperature interval, the interval between different temperature points is not more than 10 ℃.
According to at least one embodiment of the application, in the method for selecting different-grade temperature indicating paints in the specific temperature interval, the extracting of the color characteristics of the different-grade temperature indicating paints on the standard sample plates at different temperature points specifically comprises:
collecting images of standard sample plates of temperature indicating paints of different marks at different temperature points;
and performing color space conversion on images of the standard sample plates of the temperature-indicating paints of different marks at different temperature points, and extracting color features in the set area.
According to at least one embodiment of the application, in the method for selecting the temperature-indicating paints of different grades in the specific temperature range, the colors of the temperature-indicating paints of different grades in each color section are gradually changed and do not change suddenly.
According to at least one embodiment of the present application, in the method for selecting different grades of temperature indicating paints in the specific temperature range, the color section identification model is established for the different grades of temperature indicating paints by using color feature data in each color section, and specifically:
and for different grades of temperature indicating paints, establishing a color section identification model by using a machine learning algorithm according to color characteristic data in each color section.
According to at least one embodiment of the present application, in the method for selecting different grades of temperature indicating paints in the specific temperature range, the temperature identification model of each color segment is established for the different grades of temperature indicating paints according to the color feature data in each color segment, and specifically:
and for different grades of temperature indicating paints, establishing a temperature identification model of each color section by using a machine learning algorithm according to color characteristic data in each color section.
According to at least one embodiment of the application, in the method for selecting different-grade temperature indicating paints in the specific temperature interval, color characteristic data of different-grade temperature indicating paint test sets are subjected to color section identification by using a color section identification model, then temperature identification is performed by using a corresponding temperature identification model, an identification temperature value is compared with a corresponding temperature point, and the temperature measurement accuracy rates of different temperature points are counted, specifically:
if the deviation of the identified temperature value relative to the corresponding temperature point exceeds the temperature allowable error threshold value, the temperature identification is considered to be inaccurate;
if the deviation of the identified temperature value relative to the corresponding temperature point is smaller than the temperature allowable error threshold value, the temperature identification is considered to be accurate;
and counting the number of inaccurate and accurate temperature identification for different temperature points to obtain the temperature measurement accuracy.
According to at least one embodiment of the application, in the method for selecting the temperature indicating paints of different brands in the specific temperature interval, the specific temperature interval is divided to obtain temperature sections, and the temperature measurement accuracy of at least one temperature indicating paint of different brands at different temperature points in each temperature section meets the requirement of temperature measurement accuracy, specifically:
the temperature nodes of all color sections of the temperature indicating paints with different marks are used as interval points for dividing a specific temperature interval, partial adjacent interval points are combined to obtain temperature sections, and the average value of the temperature measuring accuracy rate of at least one temperature indicating paint with different marks at different temperature points in each temperature section is greater than the threshold value required by the temperature measuring accuracy.
The application has at least the following beneficial technical effects:
the method comprises extracting color characteristics of standard sample plates of different-grade temperature paints at different temperature points in a specific temperature interval, clustering the color characteristics of training sets of different-grade temperature paints to obtain color segments, establishing color segment identification models according to the color characteristic data in each color segment, establishing temperature identification models of each color segment, performing color segment identification according to the color characteristic data of a color segment identification model test set, performing temperature identification on corresponding temperature identification models, counting temperature measurement accuracy rates of different temperature points, dividing the specific temperature interval to obtain temperature segments, enabling the temperature measurement accuracy rates of at least one grade temperature paint at different temperature points in each temperature segment to meet the requirement of temperature measurement accuracy, and measuring the temperature in the specific temperature interval, one or more marks of temperature indicating paint which can meet the requirement of temperature measurement accuracy can be selected for corresponding temperature sections to be measured, and the accuracy of temperature measurement can be effectively ensured.
In the method for selecting the temperature indicating paints of different brands in the specific temperature interval, the color section identification is firstly carried out on the color characteristic data of the temperature indicating paint test sets of different brands by using the color section identification model, the color section where the color section is located is identified, then the temperature identification is carried out by using the temperature identification model of the corresponding color section, the temperature value is obtained by identification, the temperature is subjected to distribution identification, and the accuracy of temperature measurement can be effectively ensured.
Drawings
FIG. 1 is a schematic diagram of a method for selecting different grades of temperature indicating paints in a specific temperature range according to an embodiment of the present application;
fig. 2 is a schematic diagram of the average value of the temperature measurement accuracy rates of the grades KN3, KN6 and KN8 provided by the embodiment of the present application at different temperature points in each temperature section compared with the 95% threshold value required for the temperature measurement accuracy.
For the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; in addition, the drawings are used for illustrative purposes, and the positional relationship is only for illustrative purposes and is not to be construed as limiting the patent.
Detailed Description
In order to make the technical solutions and advantages of the present application clearer, the technical solutions of the present application will be further clearly and completely described in the following detailed description with reference to the accompanying drawings, and it should be understood that the specific embodiments described herein are only some of the embodiments of the present application, and are only used for explaining the present application, but not limiting the present application. It should be noted that, for convenience of description, only the parts related to the present application are shown in the drawings, other related parts may refer to general designs, and the embodiments and technical features in the embodiments in the present application may be combined with each other to obtain a new embodiment without conflict.
In addition, unless otherwise defined, technical or scientific terms used in the description of the present application shall have the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "upper", "lower", "left", "right", "center", "vertical", "horizontal", "inner", "outer", and the like used in the description of the present application, which indicate orientations, are used only to indicate relative directions or positional relationships, and do not imply that the devices or elements must have a specific orientation, be constructed and operated in a specific orientation, and when the absolute position of the object to be described is changed, the relative positional relationships may be changed accordingly, and thus, should not be construed as limiting the present application. The use of "first," "second," "third," and the like in the description of the present application is for descriptive purposes only to distinguish between different components and is not to be construed as indicating or implying relative importance. The use of the terms "a," "an," or "the" and similar referents in the context of describing the application is not to be construed as an absolute limitation on the number, but rather as the presence of at least one. The word "comprising" or "comprises", and the like, when used in this description, is intended to specify the presence of stated elements or items, but not the exclusion of other elements or items.
Further, it is noted that, unless expressly stated or limited otherwise, the terms "mounted," "connected," and the like are used in the description of the invention in a generic sense, e.g., connected as either a fixed connection or a removable connection or integrally connected; can be mechanically or electrically connected; they may be directly connected or indirectly connected through an intermediate medium, or they may be connected through the inside of two elements, and those skilled in the art can understand their specific meaning in this application according to the specific situation.
In a specific embodiment, the specific temperature interval is 150 ℃ to 1200 ℃, the three grades of temperature indicating paint comprise KN3, KN6 and KN8, the temperature allowable error threshold is 10 ℃, the temperature measurement accuracy requirement threshold is 95%, and the selection method of the different grades of temperature indicating paint in the specific temperature interval provided by the application is further described in detail with reference to the attached drawings 1 to 2.
And firing standard templates of the temperature-indicating paint grades KN3, KN6 and KN8 at a specific temperature interval of 150-1200 ℃ at each interval of 10 ℃.
The camera or video camera is used for collecting images of the KN3, KN6 and KN8 temperature indicating paint at different temperature points, the standard light source is used for lighting in the collecting process, so that the lighting conditions on the standard sample plate are consistent, and the influence of uneven lighting is avoided.
And (3) carrying out color space conversion on the images of the standard sample plates of the temperature-indicating paints with the brands of KN3, KN6 and KN8 at different temperature points, wherein the color space can be HSV, LUV or Lab, extracting color features in a set area, the set area does not exceed the area occupied by each temperature point on the image of the standard sample plate, and is usually designed to be not less than 50 multiplied by 50 pixels, and carrying out filtering processing on the color features.
The color characteristics of the temperature indicating paint with the KN3 trademark, the KN6 trademark and the KN8 trademark are divided into a training set and a testing set at different temperature points, the training set and the testing set can be divided in a random sampling mode, wherein the number of the training set is 0.6-0.9, and 0.8 can be selected specifically.
Calculating the distances from the color features of the KN3, KN6 and KN8 brand temperature indicating paint training set to the color average value, selecting the color features with the distance of 80% as effective data, clustering to obtain color segments, wherein the colors in the color segments are gradually changed and do not suddenly change, 5 color segments are obtained by the KN3 brand temperature indicating paint, 8 color segments are obtained by the KN6 brand temperature indicating paint, and 5 color segments are obtained by the KN8 brand temperature indicating paint.
And for KN3, KN6 and KN8 brand temperature indicating paint, establishing a color segment recognition model by using a machine learning algorithm according to color feature data in each color segment, wherein the machine learning algorithm can be KNN, SVM or decision tree algorithm.
And for KN3, KN6 and KN8 brand temperature indicating paint, establishing a temperature identification model of each color segment by using a machine learning algorithm according to the color characteristic data in each color segment, wherein the machine learning algorithm can be KNN, SVM or decision tree algorithm.
And color feature data of the temperature indicating paint test set of the grades KN3, KN6 and KN8 are subjected to color section identification by a color section identification model, then temperature identification is carried out by a corresponding temperature identification model, an identification temperature value is compared with a corresponding temperature point, if the deviation of the identification temperature value relative to the corresponding temperature point exceeds 10 ℃, the temperature identification is considered to be inaccurate, if the deviation of the identification temperature value relative to the corresponding temperature point is less than 10 ℃, the temperature identification is considered to be accurate, and the inaccurate and accurate number of the temperature identification is counted for different temperature points, so that the temperature measurement accuracy rates of the temperature indicating paints of the grades KN3, KN6 and KN8 at different temperature points are obtained.
The temperature nodes of all color sections of the temperature-indicating paint with the brands of KN3, KN6 and KN8 are used as interval points divided by a specific temperature interval of 150-1200 ℃, partial adjacent interval points are combined to obtain temperature sections, the average value of the temperature-measuring accuracy of at least one brand temperature-indicating paint at different temperature points in each temperature section is greater than the threshold 95% of the temperature-measuring accuracy requirement, when the requirement cannot be met, the temperature sections are divided again, or the threshold required by the temperature-measuring accuracy is properly reduced, and the specific results are shown in the following table:
| temperature range (. degree.C.) | 150～210 | 220～270 | 280～360 | 370～630 | 640～990 | 1000～1200 |
| KN3 | N | N | Y | Y | Y | N |
| KN6 | Y | Y | Y | N | N | Y |
| KN8 | N | Y | Y | Y | N | N |
In the above table, N represents that the average value of the temperature measurement accuracy rates of the temperature indication paints of the corresponding signs at different temperature points in the corresponding temperature sections is less than the required threshold value of the temperature measurement accuracy by 95%, Y represents that the average value of the temperature measurement accuracy rates of the temperature indication paints of the corresponding signs at different temperature points in the corresponding temperature sections is greater than the required threshold value of the temperature measurement accuracy by 95%, and when the temperature in each temperature section is measured, one or more temperature indication paints of the corresponding signs by Y can be selected, and particularly, refer to fig. 2.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
Having thus described the present application in connection with the preferred embodiments illustrated in the accompanying drawings, it will be understood by those skilled in the art that the scope of the present application is not limited to those specific embodiments, and that equivalent modifications or substitutions of related technical features may be made by those skilled in the art without departing from the principle of the present application, and those modifications or substitutions will fall within the scope of the present application.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210125050.7A CN114463589B (en) | 2022-02-10 | 2022-02-10 | A method for selecting different grades of temperature-indicating paint within a specific temperature range |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210125050.7A CN114463589B (en) | 2022-02-10 | 2022-02-10 | A method for selecting different grades of temperature-indicating paint within a specific temperature range |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114463589A true CN114463589A (en) | 2022-05-10 |
| CN114463589B CN114463589B (en) | 2024-09-13 |
Family
ID=81412736
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210125050.7A Active CN114463589B (en) | 2022-02-10 | 2022-02-10 | A method for selecting different grades of temperature-indicating paint within a specific temperature range |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114463589B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102313607A (en) * | 2010-06-30 | 2012-01-11 | 中国航空工业集团公司沈阳发动机设计研究所 | Automatic identification method for temperature-change color of chameleon paint |
| CN203629720U (en) * | 2013-05-17 | 2014-06-04 | 中国燃气涡轮研究院 | Thermal paint temperature detection device |
| WO2020248854A1 (en) * | 2019-06-13 | 2020-12-17 | 南京航空航天大学 | Electrical impedance tomography-based temperature measurement method for high-temperature ceramic matrix composite component |
| CN112633292A (en) * | 2020-09-01 | 2021-04-09 | 广东电网有限责任公司 | Method for measuring temperature of oxide layer on metal surface |
-
2022
- 2022-02-10 CN CN202210125050.7A patent/CN114463589B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102313607A (en) * | 2010-06-30 | 2012-01-11 | 中国航空工业集团公司沈阳发动机设计研究所 | Automatic identification method for temperature-change color of chameleon paint |
| CN203629720U (en) * | 2013-05-17 | 2014-06-04 | 中国燃气涡轮研究院 | Thermal paint temperature detection device |
| WO2020248854A1 (en) * | 2019-06-13 | 2020-12-17 | 南京航空航天大学 | Electrical impedance tomography-based temperature measurement method for high-temperature ceramic matrix composite component |
| CN112633292A (en) * | 2020-09-01 | 2021-04-09 | 广东电网有限责任公司 | Method for measuring temperature of oxide layer on metal surface |
Non-Patent Citations (3)
| Title |
|---|
| 刘忠奎;葛俊锋;张羽鹏;张兴;: "示温漆温度自动判读技术研究", 中国测试, no. 09, 30 September 2015 (2015-09-30) * |
| 王展: "基于示温漆图像的温度自动判读算法研究", 中国博士学位论文电子期刊网, 15 October 2018 (2018-10-15) * |
| 马春武;姜斌;陈复扬;: "示温漆温度自动判读与数字图像处理系统", 航空发动机, no. 02, 15 June 2007 (2007-06-15) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114463589B (en) | 2024-09-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112699876B (en) | Automatic reading method for various meters of gas collecting station | |
| CN102184405B (en) | Image Acquisition and Analysis Method | |
| CN112818988A (en) | Automatic reading identification method and system for pointer instrument | |
| CN109115800B (en) | Method for rapidly detecting burrs of product and accurately measuring length | |
| CN104990892B (en) | The spectrum picture Undamaged determination method for establishing model and seeds idenmtification method of seed | |
| TW201935316A (en) | Reduced false positive identification for spectroscopic quantification | |
| CN115797352A (en) | Tongue picture image processing system for traditional Chinese medicine health-care physique detection | |
| CN115049656A (en) | Method for identifying and classifying defects in silicon steel rolling process | |
| CN103776482B (en) | The image detecting method of the scale of pointer instrument without scale line | |
| CN110874835B (en) | Crop leaf disease resistance identification method and system, electronic equipment and storage medium | |
| CN118501177B (en) | Appearance defect detection method and system for formed foil | |
| CN103712565B (en) | A kind of wear scar diameter measuring method based on steel ball polishing scratch gradient | |
| CN103514599A (en) | Image optimum segmentation dimension selecting method based on neighborhood total variation | |
| CN114463589A (en) | A method for selecting different grades of temperature-indicating paint in a specific temperature range | |
| CN104715160B (en) | Soft sensor modeling data exception point detecting method based on KMDB | |
| CN119540233A (en) | A method for detecting pattern printing defects on drug packaging boxes | |
| CN115239947A (en) | Method and device for assessing the severity of wheat stripe rust based on unsupervised learning | |
| CN205158399U (en) | Multispectral vision formation of image da ye crops blade grading system | |
| CN112991425B (en) | Water level extraction method, system and storage medium | |
| CN103761729A (en) | Steel ball grinding crack detection method based on neighborhood gray level similarity | |
| CN119445159A (en) | A quality detection and identification method | |
| CN116258864B (en) | A big data management system for village planning and construction | |
| CN118781071A (en) | A system and method for detecting appearance defects of finished cigarette boxes | |
| CN117975425A (en) | Target detection pointer instrument reading method and system based on deep learning | |
| CN114742795A (en) | An intelligent detection method for tunnel lining diseases based on deep learning |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |