CN113205482A - Iron and steel rust removal quality grade judgment method based on visual identification - Google Patents
Iron and steel rust removal quality grade judgment method based on visual identification Download PDFInfo
- Publication number
- CN113205482A CN113205482A CN202110308317.1A CN202110308317A CN113205482A CN 113205482 A CN113205482 A CN 113205482A CN 202110308317 A CN202110308317 A CN 202110308317A CN 113205482 A CN113205482 A CN 113205482A
- Authority
- CN
- China
- Prior art keywords
- rust removal
- grade
- graphic data
- steel
- standard
- 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
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 89
- 239000010959 steel Substances 0.000 title claims abstract description 89
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 title claims abstract description 81
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000000007 visual effect Effects 0.000 title claims abstract description 23
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 title claims description 14
- 229910052742 iron Inorganic materials 0.000 title claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 15
- 238000013441 quality evaluation Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 239000002253 acid Substances 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000005422 blasting Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000005554 pickling Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000004381 surface treatment Methods 0.000 description 3
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 239000003595 mist Substances 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 239000002351 wastewater Substances 0.000 description 2
- 241001270131 Agaricus moelleri Species 0.000 description 1
- 239000007864 aqueous solution Substances 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000003973 paint Substances 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 239000003209 petroleum derivative Substances 0.000 description 1
- 238000005498 polishing Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30136—Metal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
Abstract
The invention relates to the technical field of steel rust removal quality evaluation, in particular to a visual identification-based steel rust removal quality grade judgment method, which comprises the following steps: s1, shooting corresponding standard graphic data according to each grade of the derusting quality grade, matching the standard graphic data with the corresponding derusting quality grade and storing the standard graphic data into a standard grade database; and S2, obtaining surface pattern data of the target steel component, and comparing the surface pattern data with a standard grade database according to a comparison principle to obtain the derusting grade of the target steel component. The scheme is used for detecting the steel surface rust removal result, ensures that the steel surface rust removal effect meets the requirement, and has simple operation and strong practicability. The laser rust removal effect can be detected, monitored and fed back in real time, the rust removal deviation is avoided, an ideal rust removal effect can be obtained, and meanwhile the feedback rust removal efficiency and the residual working hours required by rust removal can be obtained.
Description
Technical Field
The invention relates to the technical field of steel rust removal quality evaluation, in particular to a visual identification-based steel rust removal quality grade judgment method.
Background
The steel pipe used for conveying petroleum and natural gas needs to be subjected to anticorrosion treatment before leaving a factory, and the anticorrosion effect of the steel pipe is greatly influenced by the rust removal quality of the steel pipe. Most of the steel pipe production enterprises generally adopt a one-time shot blasting rust removal process, the one-time rust removal process is focused on removing oxide skin, rust scrap iron, rust spots and the like on the surface of a steel pipe, and irregular anchor lines appear on the surface of the steel pipe. When the surface of a large-diameter steel pipe is derusted, the derusting efficiency is low, the shot blasting quality is not high, the surface anchor line depth and the derusting grade are not high, and the surface derusting quality is poor. In order to achieve better steel pipe surface treatment quality, an acid pickling method is adopted to treat oxide skin or phosphorize after shot blasting rust removal. After the surface of the steel pipe is acid-washed, a large amount of water is adopted to wash the surface of the steel pipe, so that the surface of the steel pipe is neutral, and the subsequent coating process can be carried out. The pickling process cannot further improve the anchor lines on the surface of the steel pipe, and acid mist generated by the pickling process discharges waste acid aqueous solution, so that the waste acid mist and waste water cause serious environmental pollution. The phosphating treatment process is suitable for the surface of a steel pipe which is not seriously corroded and has no black skin or old paint film, but the phosphating treatment process can not further improve the uniformity and the derusting grade of the anchor lines on the surface of the steel pipe, and simultaneously generates a large amount of waste water and causes great pollution to the environment. Therefore, an accurate evaluation system for the rust removal quality is urgently needed for the surface rust removal of the steel pipe, the quality grade evaluation of the rust removal has no visible requirements on the surface, and the stability of the rust removal quality cannot be ensured due to the effects of subjective factors and experience of people.
Disclosure of Invention
The invention provides a method for judging the rust removal quality grade of steel based on visual identification, which solves the technical problem that the rust removal quality is unstable due to subjective factors of people in the rust removal quality grade evaluation.
The invention provides a method for judging the rust removal quality grade of steel based on visual identification, which comprises the following steps:
s1, shooting corresponding standard graphic data according to each grade of the derusting quality grade, matching the standard graphic data with the corresponding derusting quality grade and storing the standard graphic data into a standard grade database;
and S2, obtaining surface pattern data of the target steel component, and comparing the surface pattern data with a standard grade database according to a comparison principle to obtain the derusting grade of the target steel component.
Optionally, the surface pattern data includes a plurality of frame images, and it is determined whether a frame image with a repeated period exists in the frame images, if so, a redundant frame is removed, and then the surface pattern data with the redundant frame removed is synthesized again according to a time sequence.
Optionally, the S2 specifically includes: acquiring surface graphic data of a target steel component, performing gray processing to obtain a gray image, calculating an average gray value of a unit area on the gray image, and comparing and analyzing the average gray value with a standard grade database to obtain a corresponding derusting grade.
Optionally, the S2 specifically includes:
firstly, covering a region of a target steel component to form a dark region, exposing the dark region and acquiring region graphic data through a visual sensor;
sequentially exposing to obtain regional graphic data of different regions until the surface of the target steel member is completely covered to obtain a plurality of regional graphic data;
and splicing the graphic data of all the areas to obtain complete surface image data.
Optionally, the complete surface graph data is compared with a standard grade database according to a comparison principle to obtain the rust removal grade of the target steel component.
Optionally, the graphic data of each region are respectively compared with the standard grade database for analysis to obtain the rust removal grade corresponding to each region, and the lowest rust removal grade is used as the rust removal grade of the target steel member.
Optionally, the S2 specifically includes: and placing the target steel member in a darkroom, exposing the darkroom and acquiring surface pattern data of the target steel member through a vision sensor.
Optionally, the comparison principle includes extracting features of the surface graphic data, performing corresponding comparison analysis on the features of the surface graphic data and the features of the standard graphic data, and if the comparison similarity of one or more features is within a preset threshold range, determining the rust removal grade corresponding to the surface graphic data and the standard graphic data.
Has the advantages that: the invention provides a method for judging the rust removal quality grade of steel based on visual identification, which comprises the following steps: s1, shooting corresponding standard graphic data according to each grade of the derusting quality grade, matching the standard graphic data with the corresponding derusting quality grade and storing the standard graphic data into a standard grade database; and S2, obtaining surface pattern data of the target steel component, and comparing the surface pattern data with a standard grade database according to a comparison principle to obtain the derusting grade of the target steel component. The scheme is used for detecting the steel surface rust removal result, ensures that the steel surface rust removal effect meets the requirement, and has simple operation and strong practicability. The laser rust removal effect can be detected, monitored and fed back in real time, the rust removal deviation is avoided, an ideal rust removal effect can be obtained, and meanwhile the feedback rust removal efficiency and the residual working hours required by rust removal can be obtained.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic flow chart of a steel rust removal quality grade determination method based on visual identification;
FIG. 2 is a schematic diagram of a specific working principle of the steel rust removal quality grade determination method based on visual identification.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention. The invention is described in more detail in the following paragraphs by way of example with reference to the accompanying drawings. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
As shown in fig. 1, the invention provides a method for judging the rust removal quality grade of steel based on visual identification, which comprises the following steps: s1, shooting corresponding standard graphic data according to each grade of the derusting quality grade, matching the standard graphic data with the corresponding derusting quality grade and storing the standard graphic data into a standard grade database; and S2, obtaining surface pattern data of the target steel component, and comparing the surface pattern data with a standard grade database according to a comparison principle to obtain the derusting grade of the target steel component. The scheme is used for detecting the steel surface rust removal result, ensures that the steel surface rust removal effect meets the requirement, and has simple operation and strong practicability. The laser rust removal effect can be detected, monitored and fed back in real time, the rust removal deviation is avoided, an ideal rust removal effect can be obtained, and meanwhile the feedback rust removal efficiency and the residual working hours required by rust removal can be obtained.
The method comprises the steps of firstly drawing up a standard, grading the corrosion degrees of the surfaces of different steel products, corresponding the different grades to different rust removal grades, and then respectively photographing the different rust removal grades to form a standard grade database. Each rust removal rating may include one or more corresponding steel rust pictures. And then, acquiring surface graphic data of the target steel component in an image acquisition mode, and comparing the surface graphic data with a standard grade database according to a comparison principle to obtain the derusting grade of the target steel component. And (3) as long as the similarity between the surface graph data of the target steel component and any picture in the standard grade database reaches a preset threshold value, the target steel component is considered as the rust removal grade.
Optionally, the surface pattern data includes a plurality of frame images, and it is determined whether a frame image with a repeated period exists in the frame images, if so, a redundant frame is removed, and then the surface pattern data with the redundant frame removed is synthesized again according to a time sequence. Specifically, the frame images are sequentially ordered in chronological order, in which a difference in deflection between the deflection angle of the first initial frame image received at the initial time and the deflection angle of the frame image received at a time (denoted as time T1) after the specified time is determined. If the difference in deflection between the two deflection angles is greater than the predetermined deflection difference and the difference in deflection between the deflection angle of the initial frame image and the deflection angle of the frame image received at the time subsequent to time T1 (referred to as time T2) does not exceed the predetermined deflection difference, the frame images received at the initial time and the time T1 are considered to be duplicate frame images, and the frame images received from the initial time to the time T1 are defined as first periodic frame images. The method can eliminate the artifacts which may occur in a synthesis mode, thereby not influencing the image comparison speed due to the problem of the artifacts, and simultaneously improving the accuracy.
Optionally, the S2 specifically includes: acquiring surface graphic data of a target steel component, performing gray processing to obtain a gray image, calculating an average gray value of a unit area on the gray image, and comparing and analyzing the average gray value with a standard grade database to obtain a corresponding derusting grade. Grayscale refers to pure white, pure black, and a series of transition colors from black to white in both. The highest gray level corresponds to the highest black level, i.e., pure black. The lowest grey scale corresponds to the lowest black, i.e. pure white. For example, the gray scale value with continuous black-gray-white variation is quantized to 256 gray scales, the gray scale value is in the range of 0-255, which indicates that the brightness is from dark to light, and the corresponding color in the image is from black to white. Black and white photographs contain all shades of gray between black and white, with each pixel value being one of 256 shades of gray between black and white. And calculating the acquired gray level image to obtain an average gray level value. The average gray value calculation method may be an arithmetic average gray value, a geometric average gray value, a root mean square average gray value, a harmonic average gray value, a weighted average gray value, or the like. And comparing the average gray value with a preset derusting grade gray threshold value to determine the derusting grade of the derusting area.
As shown in fig. 2, optionally, the S2 specifically includes: firstly, covering a region of a target steel component to form a dark region, exposing the dark region and acquiring region graphic data through a visual sensor; sequentially exposing to obtain regional graphic data of different regions until the surface of the target steel member is completely covered to obtain a plurality of regional graphic data; and splicing the graphic data of all the areas to obtain complete surface image data. The method comprises the following steps of manufacturing a telescopic darkroom, wherein the telescopic darkroom is provided with a semi-closed cavity, an opening of the semi-closed cavity can be stretched, so that an area is conveniently and hermetically framed, and a visual recognition sensor for recognizing lighting for exposure and shooting and for acquiring image data is arranged in the telescopic darkroom. Wherein, the flexible darkroom is smaller than the steel component, can shoot many times and can be full coverage. By taking a picture in a darkroom, the surface treatment of the steel member can be prevented from being disturbed and errors can be avoided.
And optionally, comparing the complete surface graph data with a standard grade database according to a comparison principle to obtain the derusting grade of the target steel component. Or comparing and analyzing the graphic data of each region with a standard grade database respectively to obtain the rust removal grade corresponding to each region, and taking the lowest rust removal grade as the rust removal grade of the target steel member. The same effect can be realized by both the two schemes, and the selection is specifically carried out according to the actual working condition.
Optionally, the target steel member is placed in a darkroom, the darkroom is exposed, and surface pattern data of the target steel member is acquired through a vision sensor. The whole steel component is located in the darkroom, then exposure and shooting are carried out, and interference and error generation can be judged on the surface of the steel component in various environments by carrying out shooting in the darkroom.
Optionally, the comparison principle includes extracting features of the surface pattern data, performing corresponding comparison analysis on the features of the surface pattern data and features of the standard pattern data, and if the comparison similarity of one or more features is within a preset threshold range, determining a rust removal grade corresponding to the surface pattern data and the standard pattern data.
Has the advantages that: the invention provides a method for judging the rust removal quality grade of steel based on visual identification, which comprises the following steps: s1, shooting corresponding standard graphic data according to each grade of the derusting quality grade, matching the standard graphic data with the corresponding derusting quality grade and storing the standard graphic data into a standard grade database; and S2, obtaining surface pattern data of the target steel component, and comparing the surface pattern data with a standard grade database according to a comparison principle to obtain the derusting grade of the target steel component. The scheme is used for detecting the steel surface rust removal result, ensures that the steel surface rust removal effect meets the requirement, and has simple operation and strong practicability. The laser rust removal effect can be detected, monitored and fed back in real time, the rust removal deviation is avoided, an ideal rust removal effect can be obtained, and meanwhile the feedback rust removal efficiency and the residual working hours required by rust removal can be obtained. The possibility of objective evaluation of the quality grade of the surface treatment of the steel member is improved, and the phenomena of excessive polishing rust removal and unqualified steel members are avoided.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (8)
1. A method for judging the rust removal quality grade of steel based on visual identification is characterized by comprising the following steps:
s1, shooting corresponding standard graphic data according to each grade of the derusting quality grade, matching the standard graphic data with the corresponding derusting quality grade and storing the standard graphic data into a standard grade database;
and S2, obtaining surface pattern data of the target steel component, and comparing the surface pattern data with a standard grade database according to a comparison principle to obtain the derusting grade of the target steel component.
2. The visual recognition-based steel rust removal quality grade determination method according to claim 1, wherein the surface graphic data includes a plurality of frame images, whether a frame image with a repeated period exists in the frame images is determined, if so, a redundant frame is removed, and then the surface graphic data from which the redundant frame is removed is re-synthesized according to a time sequence.
3. The method for judging the quality grade of rust removal for steel and iron based on visual recognition as claimed in claim 1, wherein the step S2 specifically comprises: acquiring surface graphic data of a target steel component, performing gray processing to obtain a gray image, calculating an average gray value of a unit area on the gray image, and comparing and analyzing the average gray value with a standard grade database to obtain a corresponding derusting grade.
4. The method for judging the quality grade of rust removal for steel and iron based on visual recognition as claimed in claim 1, wherein the step S2 specifically comprises:
firstly, covering a region of a target steel component to form a dark region, exposing the dark region and acquiring region graphic data through a visual sensor;
sequentially exposing to obtain regional graphic data of different regions until the surface of the target steel member is completely covered to obtain a plurality of regional graphic data;
and splicing the graphic data of all the areas to obtain complete surface image data.
5. The visual recognition-based steel rust removal quality grade determination method according to claim 4, wherein the complete surface graphic data is compared with a standard grade database according to a comparison principle to obtain the rust removal grade of the target steel member.
6. The method for judging the rust removal quality grade of the steel and iron based on the visual identification as claimed in claim 4, wherein the image data of each area is compared and analyzed with a standard grade database respectively to obtain the rust removal grade corresponding to each area, and the lowest rust removal grade is taken as the rust removal grade of the target steel member.
7. The method for judging the quality grade of rust removal for steel and iron based on visual recognition as claimed in claim 1, wherein the step S2 specifically comprises: and placing the target steel member in a darkroom, exposing the darkroom and acquiring surface pattern data of the target steel member through a vision sensor.
8. The visual recognition-based steel rust removal quality grade judgment method according to claim 4, wherein the comparison principle comprises the steps of performing feature extraction on the surface graphic data, performing corresponding comparison analysis on the features of the surface graphic data and the features of the standard graphic data, and judging the rust removal grade corresponding to the surface graphic data and the standard graphic data if the comparison similarity of one or more features is within a preset threshold range.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110308317.1A CN113205482A (en) | 2021-03-23 | 2021-03-23 | Iron and steel rust removal quality grade judgment method based on visual identification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110308317.1A CN113205482A (en) | 2021-03-23 | 2021-03-23 | Iron and steel rust removal quality grade judgment method based on visual identification |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113205482A true CN113205482A (en) | 2021-08-03 |
Family
ID=77025669
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110308317.1A Pending CN113205482A (en) | 2021-03-23 | 2021-03-23 | Iron and steel rust removal quality grade judgment method based on visual identification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113205482A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115229641A (en) * | 2022-09-22 | 2022-10-25 | 南通速维工程设备有限公司 | Robot pressure pipeline rust removal control method and system |
| CN115383410A (en) * | 2022-09-16 | 2022-11-25 | 达力普石油专用管有限公司 | Steel pipe external thread machining method, steel pipe machining system and workshop for steel pipe machining |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203219583U (en) * | 2013-04-29 | 2013-09-25 | 大连民族学院 | Novel machine vision light source control system |
| CN107610125A (en) * | 2017-10-16 | 2018-01-19 | 云南电网有限责任公司临沧供电局 | A kind of long distance laser derusting monitoring in real time and feedback method, apparatus and system |
| CN109002974A (en) * | 2018-06-29 | 2018-12-14 | 首钢京唐钢铁联合有限责任公司 | A kind of coil of strip surface quality grading determination method and device |
| CN109332900A (en) * | 2018-10-22 | 2019-02-15 | 西安云鑫电子科技有限公司 | Away rust by laser method and device, storage medium, electric terminal |
| CN110634139A (en) * | 2019-09-27 | 2019-12-31 | 首都医科大学附属北京世纪坛医院 | Image comparison method and system |
| CN110880179A (en) * | 2019-12-20 | 2020-03-13 | 易思维（杭州）科技有限公司 | Method for testing working state of visual sensor |
| US20200134773A1 (en) * | 2018-10-27 | 2020-04-30 | Gilbert Pinter | Machine vision systems, illumination sources for use in machine vision systems, and components for use in the illumination sources |
-
2021
- 2021-03-23 CN CN202110308317.1A patent/CN113205482A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203219583U (en) * | 2013-04-29 | 2013-09-25 | 大连民族学院 | Novel machine vision light source control system |
| CN107610125A (en) * | 2017-10-16 | 2018-01-19 | 云南电网有限责任公司临沧供电局 | A kind of long distance laser derusting monitoring in real time and feedback method, apparatus and system |
| CN109002974A (en) * | 2018-06-29 | 2018-12-14 | 首钢京唐钢铁联合有限责任公司 | A kind of coil of strip surface quality grading determination method and device |
| CN109332900A (en) * | 2018-10-22 | 2019-02-15 | 西安云鑫电子科技有限公司 | Away rust by laser method and device, storage medium, electric terminal |
| US20200134773A1 (en) * | 2018-10-27 | 2020-04-30 | Gilbert Pinter | Machine vision systems, illumination sources for use in machine vision systems, and components for use in the illumination sources |
| CN110634139A (en) * | 2019-09-27 | 2019-12-31 | 首都医科大学附属北京世纪坛医院 | Image comparison method and system |
| CN110880179A (en) * | 2019-12-20 | 2020-03-13 | 易思维（杭州）科技有限公司 | Method for testing working state of visual sensor |
Non-Patent Citations (2)
| Title |
|---|
| 吴晓君等: "基于机器视觉技术的表面缺陷在线检测系统设计", 《自动化与仪表》 * |
| 马竹梧: "工业视觉系统及其在钢铁工业中应用", 《自动化博览》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115383410A (en) * | 2022-09-16 | 2022-11-25 | 达力普石油专用管有限公司 | Steel pipe external thread machining method, steel pipe machining system and workshop for steel pipe machining |
| CN115383410B (en) * | 2022-09-16 | 2023-12-15 | 达力普石油专用管有限公司 | Steel pipe external thread machining method, steel pipe machining system and workshop for steel pipe machining |
| CN115229641A (en) * | 2022-09-22 | 2022-10-25 | 南通速维工程设备有限公司 | Robot pressure pipeline rust removal control method and system |
| CN115229641B (en) * | 2022-09-22 | 2023-12-29 | 南通速维工程设备有限公司 | Rust removal control method and system for pressure pipeline of robot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108734690B (en) | Visual defect detection equipment and detection method thereof | |
| CN107543828B (en) | Workpiece surface defect detection method and system | |
| CN109870461B (en) | Electronic components quality detection system | |
| CN111833306B (en) | Defect detection method and model training method for defect detection | |
| CN107228860B (en) | Gear defect detection method based on image rotation period characteristics | |
| CN108876768B (en) | Shadow defect detection method for light guide plate | |
| CN107490582B (en) | Assembly line workpiece detection system | |
| CN113205482A (en) | Iron and steel rust removal quality grade judgment method based on visual identification | |
| CN111640104B (en) | Visual detection method for screw assembly | |
| CN107590512B (en) | The adaptive approach and system of parameter in a kind of template matching | |
| CN116563282B (en) | Drilling tool detection method and system based on machine vision | |
| CN111539927B (en) | Detection method of automobile plastic assembly fastening buckle missing detection device | |
| JP2014228357A (en) | Crack detecting method | |
| CN114119535A (en) | Laser cleaning effect on-line monitoring method based on visual detection | |
| CN117237330B (en) | Automatic bridge defect detection method based on machine vision | |
| CN113487538B (en) | Multi-target segmentation defect detection method and device and computer storage medium thereof | |
| CN114612418A (en) | Method, device and system for detecting surface defects of mouse shell and electronic equipment | |
| CN116883408B (en) | Integrating instrument shell defect detection method based on artificial intelligence | |
| CN112950594B (en) | Method, device and storage medium for detecting surface defects of product | |
| CN109671084A (en) | A kind of measurement method of workpiece shapes | |
| CN111353507B (en) | Image recognition method and device for oil stains on surface of transformer substation device | |
| CN112288680B (en) | Automatic defect area extraction method and system for automobile hub X-ray image | |
| CN110530894B (en) | Light guide plate bright spot defect detection method | |
| CN116188826A (en) | Template matching method and device under complex illumination condition | |
| CN116740036A (en) | Method and system for detecting cutting point position of steel pipe end arc striking and extinguishing plate |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210803 |