CN113689387B - Coating detecting system based on big data - Google Patents
Coating detecting system based on big data Download PDFInfo
- Publication number
- CN113689387B CN113689387B CN202110877758.3A CN202110877758A CN113689387B CN 113689387 B CN113689387 B CN 113689387B CN 202110877758 A CN202110877758 A CN 202110877758A CN 113689387 B CN113689387 B CN 113689387B
- Authority
- CN
- China
- Prior art keywords
- area
- coating
- center
- detection
- rectangular detection
- 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.)
- Active
Links
- 238000000576 coating method Methods 0.000 title claims abstract description 116
- 239000011248 coating agent Substances 0.000 title claims abstract description 115
- 238000001514 detection method Methods 0.000 claims abstract description 194
- 238000012544 monitoring process Methods 0.000 claims abstract description 82
- 238000011156 evaluation Methods 0.000 claims description 17
- 239000003973 paint Substances 0.000 claims description 11
- 238000012163 sequencing technique Methods 0.000 claims description 8
- 238000000034 method Methods 0.000 claims description 7
- 238000005192 partition Methods 0.000 claims description 6
- 238000000638 solvent extraction Methods 0.000 abstract 1
- 239000000463 material Substances 0.000 description 5
- 239000012752 auxiliary agent Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000839 emulsion Substances 0.000 description 1
- 239000000945 filler Substances 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 230000000873 masking effect Effects 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 239000003960 organic solvent Substances 0.000 description 1
- 239000000049 pigment Substances 0.000 description 1
- 239000011347 resin Substances 0.000 description 1
- 229920005989 resin Polymers 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/30—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring roughness or irregularity of surfaces
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Spectrometry And Color Measurement (AREA)
Abstract
The invention discloses a coating detection system based on big data, which comprises a plane surface information acquisition module to be coated, a coating condition comparison module and a coating result output module, wherein the plane surface information acquisition module to be coated is used for acquiring the leveling condition of the plane surface to be coated of coating in advance and determining a monitoring area and a reference area of the plane surface to be coated according to the leveling condition, the coating condition comparison module is used for acquiring and comparing color images before and after the monitoring area is coated by coating and before and after the reference area is coated, the coating result output module outputs information whether coating of the coating meets requirements or not according to the comparison result of the coating condition comparison module, and the plane surface information acquisition module to be coated comprises a partitioning module, a relative distance acquisition module, a monitoring area generation module and a reference area generation module.
Description
Technical Field
The invention relates to the field of coating, in particular to a coating detection system based on big data.
Background
The paint is a traditional name in China. The paint is a continuous film which is coated on the surface of the protected or decorated object and can form firm adhesion with the coated object, and is usually a viscous liquid prepared by using resin, oil or emulsion as main materials, adding or not adding pigment and filler, adding corresponding auxiliary agents and using organic solvent or water. The paint can play roles in protecting, decorating, masking defects of products and the like. Coating is not satisfactory in the process of coating the surface of an object, and detection technology for coating conditions is lacking in the prior art.
Disclosure of Invention
The invention aims to provide a coating detection system and method based on big data, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the coating detection system based on big data comprises a to-be-coated plane surface information acquisition module, a coating condition comparison module and a coating result output module, wherein the to-be-coated plane surface information acquisition module is used for acquiring the leveling condition of the to-be-coated plane surface of the coating in advance and determining a monitoring area and a reference area of the to-be-coated plane surface according to the leveling condition, the coating condition comparison module is used for acquiring and comparing color images before and after the coating is coated on the monitoring area and before and after the coating is coated on the reference area, and the coating result output module outputs information whether coating meets requirements or not according to the comparison result of the coating condition comparison module.
More optimally, the plane surface information acquisition module to be coated comprises a partition module, a relative distance acquisition module, a monitoring area generation module and a reference area generation module, wherein the partition module is used for dividing the average of the plane surface to be coated into a plurality of rectangular detection areas and dividing each rectangular detection area into a plurality of detection subareas, the relative distance acquisition module is used for focusing the center of each rectangular detection area and the center of each detection subarea by an image acquisition device, the distance between each center and the image acquisition device is determined according to focal length data when focusing is completed, and the monitoring area generation module is used for dividing the monitoring area on the plane surface to be coated according to the distance acquired by the relative distance acquisition module; the reference region generating module divides a reference region on the surface of the plane to be coated according to the distance acquired by the relative distance acquisition module.
More preferably, the monitoring area generating module comprises a first distance comparing module, a second distance comparing module, a third distance comparing module and a monitoring area dividing module, wherein the first distance comparing module is used for comparing the difference of the relative distances between the centers of two adjacent rectangular detection areas, and transmitting information to the second distance comparing module when the average value X1 of the difference of the relative distances between the center of one rectangular detection area and the centers of other adjacent rectangular detection areas is larger than or equal to a first distance difference threshold value, the second distance comparing module is used for transmitting information to the third distance comparing module when the average value X2 of the difference of the relative distances between the centers of the rectangular detection areas and the centers of all detection sub-areas of the rectangular detection areas is larger than or equal to a second distance difference threshold value, and the third distance comparing module is used for comparing the information to the monitoring area dividing module when the average value X1 of the difference of the relative distances between the centers of all detection sub-areas of the rectangular detection areas and the centers of other adjacent rectangular detection areas is smaller than the third distance difference threshold value, and the monitoring area dividing module takes the center of the rectangular detection area as a circle center as a preset radius, namely a circular area is formed.
More preferably, the reference area generating module comprises a first average value calculating module, a second average value calculating module, a comprehensive evaluation value calculating module and a sorting module, wherein the first average value calculating module is used for collecting the average value of the differences between the relative distances between the centers of all rectangular detection areas and the centers of other rectangular detection areas adjacent to the first average value, the second average value calculating module is used for collecting the average value of the differences between the centers of all rectangular detection areas and the centers of all detection subareas of the rectangular detection areas, the comprehensive evaluation value calculating module calculates the comprehensive evaluation value of the centers of all rectangular detection areas according to the first average value and the second average value, and the sorting module sorts the comprehensive evaluation values in a sequence from small to large, wherein the first rectangular detection area is the reference area.
More optimally, the coating condition comparison module comprises a chromaticity acquisition module, a first chromaticity comparison module and a second chromaticity comparison module, wherein the chromaticity acquisition module acquires the chromaticity of the color images of the monitoring area and the reference area before and after coating, the first chromaticity comparison module is used for comparing the difference value of the chromaticity of the monitoring area and the reference area before and after coating, transmitting information to the second chromaticity comparison module to further detect the edge of the monitoring area when the difference value is smaller than or equal to a difference threshold value, the second chromaticity comparison module comprises a detection area division module and a detection area chromaticity comparison module, the detection area division module divides the first comparison area in the monitoring area by taking the edge line of the monitoring area as a boundary, divides the second area outside the monitoring area, and the detection area chromaticity comparison module is used for acquiring and comparing the average chromaticity of the images between the first comparison area and the second comparison area and transmitting information to the coating result output module according with the comparison result to output information whether the coating of the coating meets the requirement.
A paint application detection method based on big data, the detection method comprising:
step S1: the method comprises the steps of collecting the leveling condition of the surface of the coating to be coated in advance, and determining a monitoring area and a reference area of the surface of the coating to be coated according to the leveling condition;
step S2: and collecting and comparing color images before and after the coating is coated on the monitoring area and before and after the reference area is coated, and outputting information whether the coating meets the requirements or not according to the comparison result.
More preferably, the step S1 further includes:
dividing the surface of a plane to be coated into n rectangular detection areas on average, and dividing each rectangular detection area into m detection subareas on average;
setting an image acquisition device at a fixed height position, focusing the center of each rectangular detection area and the center of each detection subarea respectively, acquiring focal length data when focusing is completed, and determining the distance between each center and the fixed height position according to the focal length data, wherein the distance between the center of the jth rectangular detection area and the fixed height position is a relative distance L j0 The distance between the center of the ith detection subarea in the jth rectangular detection area and the fixed height position is a relative distance L ji Wherein the value range of j is 1 to n, and the value range of i is 1 to m;
comparing the difference of the relative distances between the centers of two adjacent rectangular detection areas, if the average value X1 of the difference of the relative distances between the center of one rectangular detection area and the centers of other rectangular detection areas adjacent to the center of one rectangular detection area is larger than or equal to a first distance difference threshold value, obtaining the difference of the relative distances between the center of the rectangular detection area and the centers of all detection subareas of the rectangular detection area,
if the average value X2 of the difference between the relative distances between the center of the rectangular detection area and the centers of all the detection subareas of the rectangular detection area is larger than or equal to a second distance difference threshold value, drawing a circle by taking the center of the rectangular detection area as the center of a circle and taking a first preset value as the radius to form a circular area which is a monitoring area, wherein the first preset value is larger than or equal to the length of the diagonal line of the rectangular detection area;
if the average value X2 of the relative distance between the center of the rectangular detection area and the center of each detection sub-area of the rectangular detection area is smaller than the second distance difference threshold value, the average value X3 of the relative distance between the center of each detection sub-area of the rectangular detection area and the centers of the detection sub-areas of other rectangular detection areas adjacent to the center of each detection sub-area is obtained, if the average value X3 of the relative distance is larger than or equal to the third distance difference threshold value, the rectangular detection area is a monitoring area, and if the average value X3 of the relative distance is smaller than the third distance difference threshold value, the center of the rectangular detection area is used as the center of a circle, and a circle is drawn by using the first preset value as the radius, so that a circular area is the monitoring area.
More preferably, the step S1 further includes:
an average value X1 of the differences between the relative distances between the center of each rectangular detection area and the centers of other rectangular detection areas adjacent to the center of each rectangular detection area is acquired,
an average value X2 of the difference between the relative distances of the center of each rectangular detection area and the center of each detection subarea of the rectangular detection area is acquired,
calculating comprehensive evaluation values p=0.5×x1+0.5×x2 for the centers of the respective rectangular detection areas,
and sequencing the comprehensive evaluation values in order from small to large, wherein the rectangular detection area with the first sequencing of the comprehensive evaluation values is the reference area.
More preferably, the step S2 includes:
collecting color image chromaticity of a monitoring area before coating and color image chromaticity of a reference area, calculating chromaticity difference H1 between the monitoring area before coating and the reference area,
collecting color image chromaticity of the monitoring area after coating and color image chromaticity of the reference area, calculating chromaticity difference H2 between the monitoring area and the reference area after coating,
if the difference value between the chromaticity H2 and the chromaticity H1 is larger than the difference threshold, outputting information that the coating is not satisfactory, and if the difference value is smaller than or equal to the difference threshold, further detecting the edge of the monitoring area.
More preferably, the detecting the edge of the monitoring area in step S2 further includes:
drawing a first reference circle and a second reference circle by taking the center of the monitoring area as a circle center and taking a second preset value and a third preset value as radiuses respectively, wherein the second preset value is smaller than the first preset value, the third preset value is larger than the first preset value, an area between an edge line of the first reference circle and an edge line of the monitoring area is a first comparison area, and an area between the edge line of the second reference circle and an edge line of the monitoring area is a second comparison area;
collecting average chromaticity of the images between the first comparison area and the second comparison area, and outputting information meeting the coating requirements of the coating if the chromaticity difference of the average chromaticity of the images of the first comparison area and the second comparison area is smaller than a chromaticity difference threshold;
otherwise, outputting information that the coating of the coating is not satisfactory.
Compared with the prior art, the invention has the beneficial effects that: according to the method, the flatness of the surface of the plane to be coated with the coating is detected to obtain the monitoring area, which is easy to cause coating unsatisfactory, of the surface of the plane to be coated, and then the monitoring area, which is easy to cause coating unsatisfactory, of the coating is detected after the coating is coated, so that the detection efficiency of the coating effect of the coating can be improved, meanwhile, when judging whether the coating of the coating meets the requirements, the coating condition of the reference area of the surface to be coated is selected to serve as the reference contrast of the monitoring area, and the reliability and the accuracy of a judging result are improved.
Drawings
FIG. 1 is a schematic block diagram of a big data based paint coating detection system according to the present invention;
fig. 2 is a schematic flow chart of a coating application detection method based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, in an embodiment of the present invention, a coating application detection system based on big data includes a to-be-coated planar surface information acquisition module, a coating condition comparison module, and a coating result output module, where the to-be-coated planar surface information acquisition module is configured to acquire in advance a leveling condition of a to-be-coated planar surface of a coating material and determine a monitoring area and a reference area of the to-be-coated planar surface according to the leveling condition, the coating condition comparison module is configured to acquire and compare color images before and after the monitoring area is coated with the coating material and before and after the reference area is coated with the coating material, and the coating result output module outputs information whether the coating of the coating material meets a requirement according to a comparison result of the coating condition comparison module.
The surface information acquisition module of the plane to be coated comprises a partition module, a relative distance acquisition module, a monitoring area generation module and a reference area generation module, wherein the partition module is used for dividing the average of the surface of the plane to be coated into a plurality of rectangular detection areas and dividing each rectangular detection area into a plurality of detection subareas, the relative distance acquisition module is used for focusing the center of each rectangular detection area and the center of each detection subarea by an image acquisition device, the distance between each center and the image acquisition device is determined according to focal length data when focusing is completed, and the monitoring area generation module is used for dividing the monitoring area on the surface of the plane to be coated according to the distance acquired by the relative distance acquisition module; the reference region generating module divides a reference region on the surface of the plane to be coated according to the distance acquired by the relative distance acquisition module.
The monitoring area generation module comprises a first distance comparison module, a second distance comparison module, a third distance comparison module and a monitoring area division module, wherein the first distance comparison module is used for comparing the difference of the relative distances between the centers of two adjacent rectangular detection areas, and transmitting information to the second distance comparison module when the average value X1 of the difference of the relative distances between the center of one rectangular detection area and the centers of other rectangular detection areas adjacent to the center of one rectangular detection area is larger than or equal to a first distance difference threshold value, the second distance comparison module is used for transmitting information to the third distance comparison module when the average value X2 of the difference of the relative distances between the center of the rectangular detection area and the centers of all detection areas of the rectangular detection area is larger than or equal to a second distance difference threshold value, and the third distance comparison module is used for comparing the relation that the average value of the relative distances between the centers of all detection areas of the rectangular detection area and the centers of the detection areas of other rectangular detection areas adjacent to the third distance is smaller than the third distance difference threshold value, and the monitoring area division module takes the center of the rectangular detection area as a circle center, and the first preset radius is used for forming a round monitoring area.
The reference area generation module comprises a first average value calculation module, a second average value calculation module, a comprehensive evaluation value calculation module and a sequencing module, wherein the first average value calculation module is used for collecting the average value of the difference between the relative distances between the centers of all rectangular detection areas and the centers of other rectangular detection areas adjacent to the first average value, the second average value calculation module is used for collecting the average value of the difference between the centers of all rectangular detection areas and the relative distances between the centers of all detection subareas of the rectangular detection areas and the second average value, the comprehensive evaluation value calculation module calculates the comprehensive evaluation value of the centers of all rectangular detection areas according to the first average value and the second average value, and the sequencing module sequences the comprehensive evaluation values in a sequence from small to large, wherein the first rectangular detection area is the reference area.
The coating condition comparison module comprises a chromaticity acquisition module, a first chromaticity comparison module and a second chromaticity comparison module, wherein the chromaticity acquisition module acquires the chromaticity of the color images of the monitoring area and the reference area before and after coating, the first chromaticity comparison module is used for comparing the difference value of the chromaticity of the monitoring area and the reference area before and after coating, transmitting information to the second chromaticity comparison module to further detect the edge of the monitoring area when the difference value is smaller than or equal to a difference threshold value, the second chromaticity comparison module comprises a detection area division module and a detection area chromaticity comparison module, the detection area division module divides the first comparison area into the monitoring area by taking the edge line of the monitoring area as a boundary, divides the second area outside the monitoring area, and is used for acquiring and comparing the average chromaticity of the images between the first comparison area and the second comparison area and transmitting information to the coating result output module according with the comparison result to output information whether the coating of the coating meets the requirement.
A paint application detection method based on big data, the detection method comprising:
step S1: the method comprises the steps of pre-collecting the leveling condition of the plane surface to be coated with the coating and determining a monitoring area and a reference area of the plane surface to be coated according to the leveling condition:
determining the monitored area of the planar surface to be coated includes the following:
dividing the surface of a plane to be coated into n rectangular detection areas on average, and dividing each rectangular detection area into m detection subareas on average;
setting an image acquisition device at a fixed height position, focusing the center of each rectangular detection area and the center of each detection subarea respectively, acquiring focal length data when focusing is completed, and determining the distance between each center and the fixed height position according to the focal length data, wherein the distance between the center of the jth rectangular detection area and the fixed height position is a relative distance L j0 The distance between the center of the ith detection subarea in the jth rectangular detection area and the fixed height position is a relative distance L ji Wherein the value range of j is 1 to n, and the value range of i is 1 to m;
comparing the difference of the relative distances between the centers of two adjacent rectangular detection areas, if the average value X1 of the difference of the relative distances between the center of one rectangular detection area and the centers of other rectangular detection areas adjacent to the center of one rectangular detection area is larger than or equal to a first distance difference threshold value, obtaining the difference of the relative distances between the center of the rectangular detection area and the centers of all detection subareas of the rectangular detection area,
if the average value X2 of the difference between the relative distances between the center of the rectangular detection area and the centers of all the detection subareas of the rectangular detection area is larger than or equal to a second distance difference threshold value, drawing a circle by taking the center of the rectangular detection area as the center of a circle and taking a first preset value as the radius to form a circular area which is a monitoring area, wherein the first preset value is larger than or equal to the length of the diagonal line of the rectangular detection area;
if the average value X2 of the relative distance between the center of the rectangular detection area and the center of each detection sub-area of the rectangular detection area is smaller than a second distance difference threshold value, obtaining the average value X3 of the relative distance between the center of each detection sub-area of the rectangular detection area and the centers of the detection sub-areas of other rectangular detection areas adjacent to the center of each detection sub-area, if the average value X3 of the relative distance is larger than or equal to a third distance difference threshold value, the rectangular detection area is a monitoring area, and if the average value X3 of the relative distance is smaller than the third distance difference threshold value, drawing a circle by taking the center of the rectangular detection area as the center of a circle and taking a first preset value as the radius, and forming a circular area as the monitoring area;
determining a reference area of the planar surface to be coated includes:
an average value X1 of the differences between the relative distances between the center of each rectangular detection area and the centers of other rectangular detection areas adjacent to the center of each rectangular detection area is acquired,
an average value X2 of the difference between the relative distances of the center of each rectangular detection area and the center of each detection subarea of the rectangular detection area is acquired,
calculating comprehensive evaluation values p=0.5×x1+0.5×x2 for the centers of the respective rectangular detection areas,
and sequencing the comprehensive evaluation values in order from small to large, wherein the rectangular detection area with the first sequencing of the comprehensive evaluation values is the reference area. In the coating process, the coating tends to be unsatisfactory in the areas where the surface of the planar surface to be coated is raised or recessed and where the raised areas or recessed areas are connected to other areas, and thus detection of these areas is required.
Step S2: collecting and comparing color images before and after coating the monitoring area and before and after coating the reference area, and outputting information whether coating of the coating meets requirements or not according to a comparison result, wherein the method comprises the following steps:
collecting color image chromaticity of a monitoring area before coating and color image chromaticity of a reference area, calculating chromaticity difference H1 between the monitoring area before coating and the reference area,
collecting color image chromaticity of the monitoring area after coating and color image chromaticity of the reference area, calculating chromaticity difference H2 between the monitoring area and the reference area after coating,
if the difference value between the chromaticity H2 and the chromaticity H1 is larger than the difference threshold, outputting information that the coating is not satisfactory, and if the difference value is smaller than or equal to the difference threshold, further detecting the edge of the monitoring area.
The further detection of the edge of the monitored area in step S2 comprises:
drawing a first reference circle and a second reference circle by taking the center of the monitoring area as a circle center and taking a second preset value and a third preset value as radiuses respectively, wherein the second preset value is smaller than the first preset value, the third preset value is larger than the first preset value, an area between an edge line of the first reference circle and an edge line of the monitoring area is a first comparison area, and an area between the edge line of the second reference circle and an edge line of the monitoring area is a second comparison area;
collecting average chromaticity of the images between the first comparison area and the second comparison area, and outputting information meeting the coating requirements of the coating if the chromaticity difference of the average chromaticity of the images of the first comparison area and the second comparison area is smaller than a chromaticity difference threshold; otherwise, outputting information that the coating of the coating is not satisfactory.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (4)
1. A big data based paint coating detection system, characterized in that: the detection system comprises a plane surface information acquisition module to be coated, a coating condition comparison module and a coating result output module, wherein the plane surface information acquisition module to be coated is used for acquiring the leveling condition of the plane surface to be coated of the coating in advance and determining a monitoring area and a reference area of the plane surface to be coated according to the leveling condition, the coating condition comparison module is used for acquiring and comparing color images before and after the monitoring area is coated by the coating and before and after the reference area is coated, and the coating result output module outputs information whether the coating of the coating meets the requirement according to the comparison result of the coating condition comparison module;
the surface information acquisition module of the plane to be coated comprises a partition module, a relative distance acquisition module, a monitoring area generation module and a reference area generation module, wherein the partition module is used for dividing the surface of the plane to be coated into a plurality of rectangular detection areas on average, each rectangular detection area is divided into a plurality of detection subareas on average, the relative distance acquisition module is used for focusing the center of each rectangular detection area and the center of each detection subarea by an image acquisition device, the distance between each center and the image acquisition device is determined according to focal length data when focusing is completed, and the monitoring area generation module is used for dividing the monitoring area on the surface of the plane to be coated according to the distance acquired by the relative distance acquisition module; the reference area generating module divides a reference area on the surface of the plane to be coated according to the distance acquired by the relative distance acquisition module;
the detection method of the paint coating detection system comprises the following steps:
step S1: the method comprises the steps of collecting the leveling condition of the surface of the coating to be coated in advance, and determining a monitoring area and a reference area of the surface of the coating to be coated according to the leveling condition;
step S2: collecting and comparing color images before and after the coating is coated on the monitoring area and before and after the reference area is coated, and outputting information whether the coating meets the requirements or not according to the comparison result;
the step S1 further includes:
dividing the surface of a plane to be coated into n rectangular detection areas on average, and dividing each rectangular detection area into m detection subareas on average;
setting an image acquisition device at a fixed height position, focusing the center of each rectangular detection area and the center of each detection subarea respectively, acquiring focal length data when focusing is completed, and determining the distance between each center and the fixed height position according to the focal length data, wherein the distance between the center of the jth rectangular detection area and the fixed height position is a relative distance L j0 The distance between the center of the ith detection subarea in the jth rectangular detection area and the fixed height position is a relative distance L ji Wherein the value range of j is 1 to n, and the value range of i is 1 to m;
comparing the difference of the relative distances between the centers of two adjacent rectangular detection areas, if the average value X1 of the difference of the relative distances between the center of one rectangular detection area and the centers of other rectangular detection areas adjacent to the center of one rectangular detection area is larger than or equal to a first distance difference threshold value, obtaining the difference of the relative distances between the center of the rectangular detection area and the centers of all detection subareas of the rectangular detection area,
if the average value X2 of the difference between the relative distances between the center of the rectangular detection area and the centers of all the detection subareas of the rectangular detection area is larger than or equal to a second distance difference threshold value, drawing a circle by taking the center of the rectangular detection area as the center of a circle and taking a first preset value as the radius to form a circular area which is a monitoring area, wherein the first preset value is larger than or equal to the length of the diagonal line of the rectangular detection area;
if the average value X2 of the relative distance between the center of the rectangular detection area and the center of each detection sub-area of the rectangular detection area is smaller than the second distance difference threshold value, the average value X3 of the relative distance between the center of each detection sub-area of the rectangular detection area and the centers of the detection sub-areas of other rectangular detection areas adjacent to the center of each detection sub-area is obtained, if the average value X3 of the relative distance is larger than or equal to the third distance difference threshold value, the rectangular detection area is a monitoring area, and if the average value X3 of the relative distance is smaller than the third distance difference threshold value, the center of the rectangular detection area is used as the center of a circle, and a circle is drawn by using the first preset value as the radius, so that a circular area is the monitoring area.
2. A big data based paint application detection system as claimed in claim 1 wherein: the step S1 further includes:
an average value X1 of the differences between the relative distances between the center of each rectangular detection area and the centers of other rectangular detection areas adjacent to the center of each rectangular detection area is acquired,
an average value X2 of the difference between the relative distances of the center of each rectangular detection area and the center of each detection subarea of the rectangular detection area is acquired,
calculating comprehensive evaluation values p=0.5×x1+0.5×x2 for the centers of the respective rectangular detection areas,
and sequencing the comprehensive evaluation values in order from small to large, wherein the rectangular detection area with the first sequencing of the comprehensive evaluation values is the reference area.
3. A big data based paint application detection system as claimed in claim 1 wherein: the step S2 includes:
collecting color image chromaticity of a monitoring area before coating and color image chromaticity of a reference area, calculating chromaticity difference H1 between the monitoring area before coating and the reference area,
collecting color image chromaticity of the monitoring area after coating and color image chromaticity of the reference area, calculating chromaticity difference H2 between the monitoring area and the reference area after coating,
if the difference value between the chromaticity H2 and the chromaticity H1 is larger than the difference threshold, outputting information that the coating is not satisfactory, and if the difference value is smaller than or equal to the difference threshold, further detecting the edge of the monitoring area.
4. A big data based paint application detection system according to claim 3, wherein: the step S2 of further detecting the edge of the monitoring area includes:
drawing a first reference circle and a second reference circle by taking the center of the monitoring area as a circle center and taking a second preset value and a third preset value as radiuses respectively, wherein the second preset value is smaller than the first preset value, the third preset value is larger than the first preset value, an area between an edge line of the first reference circle and an edge line of the monitoring area is a first comparison area, and an area between the edge line of the second reference circle and an edge line of the monitoring area is a second comparison area;
collecting average chromaticity of the images between the first comparison area and the second comparison area, and outputting information meeting the coating requirements of the coating if the chromaticity difference of the average chromaticity of the images of the first comparison area and the second comparison area is smaller than a chromaticity difference threshold;
otherwise, outputting information that the coating of the coating is not satisfactory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110877758.3A CN113689387B (en) | 2020-06-18 | 2020-06-18 | Coating detecting system based on big data |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010559989.5A CN111696102B (en) | 2020-06-18 | 2020-06-18 | Paint coating detection system and method based on big data |
| CN202110877758.3A CN113689387B (en) | 2020-06-18 | 2020-06-18 | Coating detecting system based on big data |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010559989.5A Division CN111696102B (en) | 2020-06-18 | 2020-06-18 | Paint coating detection system and method based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113689387A CN113689387A (en) | 2021-11-23 |
| CN113689387B true CN113689387B (en) | 2024-02-09 |
Family
ID=72481682
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110877758.3A Active CN113689387B (en) | 2020-06-18 | 2020-06-18 | Coating detecting system based on big data |
| CN202010559989.5A Active CN111696102B (en) | 2020-06-18 | 2020-06-18 | Paint coating detection system and method based on big data |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010559989.5A Active CN111696102B (en) | 2020-06-18 | 2020-06-18 | Paint coating detection system and method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN113689387B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115290677B (en) * | 2022-08-03 | 2023-08-22 | 广东聚德机械有限公司 | Substrate blank detection method and coating system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103163144A (en) * | 2011-12-09 | 2013-06-19 | C.R.F.阿西安尼顾问公司 | Method for monitoring the quality of the primer layer applied on a motor-vehicle body before painting |
| CN106951598A (en) * | 2017-02-24 | 2017-07-14 | 天津博迈科海洋工程有限公司 | Fireproof coating optimization method based on thickness constraints |
| CN107869973A (en) * | 2017-12-11 | 2018-04-03 | 湖南太子化工涂料有限公司 | A kind of aqueous paint surface quality detection method |
| CN111060677A (en) * | 2019-12-25 | 2020-04-24 | 泉州市长兴化工材料有限公司 | Test method for rapidly detecting coating performance of sand-containing multicolor paint |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103134785A (en) * | 2013-02-07 | 2013-06-05 | 华南理工大学 | Fluorescent powder coating surface defect detecting system and method based on machine vision |
| CN203117109U (en) * | 2013-02-07 | 2013-08-07 | 华南理工大学 | Machine vision based system used for detecting defects on phosphor powder coating surface |
| CN104655643A (en) * | 2015-02-12 | 2015-05-27 | 天津理工大学 | Quality detection system for surface welding process of electronic devices |
| US10176588B2 (en) * | 2015-09-14 | 2019-01-08 | Sightline Innovation Inc. | System and method for specular surface inspection |
| RU2718483C2 (en) * | 2016-09-23 | 2020-04-08 | Общество с ограниченной ответственностью "Гардиан Стекло Сервиз" | System and/or method of identifying coating for glass |
| KR101945302B1 (en) * | 2017-05-25 | 2019-02-08 | 금오공과대학교 산학협력단 | Method and Electronic Apparatus for Detecting of Painting Error using Image Data |
| EP3633353A4 (en) * | 2017-05-29 | 2020-06-10 | Konica Minolta, Inc. | Surface defect inspection device and method |
| CN108665444B (en) * | 2018-04-12 | 2022-11-01 | 浙江大学 | Fluorescent PCB three-proofing paint coating quality detection system and method |
| CN108982539B (en) * | 2018-07-13 | 2020-01-10 | 浙江大学 | PCB double-sided three-proofing paint coating quality detection system and method |
| CN110827280B (en) * | 2020-01-09 | 2020-04-21 | 莱克电气股份有限公司 | Glue detection method and device based on machine vision and glue detection equipment |
-
2020
- 2020-06-18 CN CN202110877758.3A patent/CN113689387B/en active Active
- 2020-06-18 CN CN202010559989.5A patent/CN111696102B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103163144A (en) * | 2011-12-09 | 2013-06-19 | C.R.F.阿西安尼顾问公司 | Method for monitoring the quality of the primer layer applied on a motor-vehicle body before painting |
| CN106951598A (en) * | 2017-02-24 | 2017-07-14 | 天津博迈科海洋工程有限公司 | Fireproof coating optimization method based on thickness constraints |
| CN107869973A (en) * | 2017-12-11 | 2018-04-03 | 湖南太子化工涂料有限公司 | A kind of aqueous paint surface quality detection method |
| CN111060677A (en) * | 2019-12-25 | 2020-04-24 | 泉州市长兴化工材料有限公司 | Test method for rapidly detecting coating performance of sand-containing multicolor paint |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111696102A (en) | 2020-09-22 |
| CN113689387A (en) | 2021-11-23 |
| CN111696102B (en) | 2021-09-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110544258B (en) | Image segmentation method and device, electronic equipment and storage medium | |
| CN104021676B (en) | Vehicle location based on vehicle dynamic video features and vehicle speed measurement method | |
| CN107315095B (en) | More vehicle automatic speed-measuring methods with illumination adaptability based on video processing | |
| Zhang et al. | Automatic road-marking detection and measurement from laser-scanning 3D profile data | |
| CN103198476B (en) | Image detection method of thick line type cross ring mark | |
| CN110766979A (en) | Parking space detection method for automatic driving vehicle | |
| CN107264570B (en) | Steel rail light band distribution detecting device and method | |
| CN105335955A (en) | Object detection method and object detection apparatus | |
| US20230401729A1 (en) | Line structured light center extraction method for complicated surfaces | |
| CN113689387B (en) | Coating detecting system based on big data | |
| CN104598912A (en) | Traffic light detection and recognition method based CPU and GPU cooperative computing | |
| CN109948591A (en) | A kind of method for detecting parking stalls, device, electronic equipment and read/write memory medium | |
| CN110530278A (en) | Utilize the method for multiple line structure light measurement clearance surface difference | |
| CN111582255A (en) | Vehicle overrun detection method and device, computer equipment and storage medium | |
| CN108922176B (en) | Rapid judgment method for mixed traffic conflict situation | |
| CN115761658B (en) | Highway pavement condition detection method based on artificial intelligence | |
| CN112633035B (en) | Driverless vehicle-based lane line coordinate true value acquisition method and device | |
| CN114092903A (en) | Lane line marking method, lane line detection model determining method, lane line detection method and related equipment | |
| CN114581481B (en) | Target speed estimation method and device, vehicle and storage medium | |
| CN111080640B (en) | Hole detection method, device, equipment and medium | |
| CN116452977A (en) | Unmanned ship platform sea surface ship detection method, system and equipment | |
| CN110969071A (en) | Obstacle detection method, device and system based on travelable area | |
| CN112950562A (en) | Fastener detection algorithm based on line structured light | |
| CN101499214B (en) | Automatic traffic parameter extraction method based on image information entropy | |
| Laureshyn et al. | Automated video analysis as a tool for analysing road user behaviour |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240109 Address after: 053000 Qianzhuang Industrial Zone, Nanwu village, Jizhou District, Hengshui City, Hebei Province Applicant after: HEBEI CHENHONG PAINT Co.,Ltd. Address before: 215000 No. 299, Fengyun Road, high tech Zone, Suzhou, Jiangsu Applicant before: Wang Zhencai |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |