CN115311304A - Iron plate corrosion defect detection method - Google Patents
Iron plate corrosion defect detection method Download PDFInfo
- Publication number
- CN115311304A CN115311304A CN202211248930.XA CN202211248930A CN115311304A CN 115311304 A CN115311304 A CN 115311304A CN 202211248930 A CN202211248930 A CN 202211248930A CN 115311304 A CN115311304 A CN 115311304A
- Authority
- CN
- China
- Prior art keywords
- abnormal
- connected domain
- area
- iron plate
- obtaining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 title claims abstract description 140
- 229910052742 iron Inorganic materials 0.000 title claims abstract description 70
- 230000007797 corrosion Effects 0.000 title claims abstract description 61
- 238000005260 corrosion Methods 0.000 title claims abstract description 61
- 230000007547 defect Effects 0.000 title claims abstract description 26
- 238000001514 detection method Methods 0.000 title claims abstract description 17
- 230000002159 abnormal effect Effects 0.000 claims abstract description 225
- 238000000034 method Methods 0.000 claims abstract description 21
- 230000011218 segmentation Effects 0.000 claims abstract description 20
- 238000004891 communication Methods 0.000 claims abstract description 17
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 claims abstract description 14
- 230000002547 anomalous effect Effects 0.000 claims description 4
- 238000009826 distribution Methods 0.000 description 9
- 235000002566 Capsicum Nutrition 0.000 description 5
- 239000006002 Pepper Substances 0.000 description 5
- 241000722363 Piper Species 0.000 description 5
- 235000016761 Piper aduncum Nutrition 0.000 description 5
- 235000017804 Piper guineense Nutrition 0.000 description 5
- 235000008184 Piper nigrum Nutrition 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 150000003839 salts Chemical class 0.000 description 5
- 208000012868 Overgrowth Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000011800 void material Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000005266 casting Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003973 paint Substances 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000006467 substitution reaction Methods 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
- G06T7/0006—Industrial image inspection using a design-rule based approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Quality & Reliability (AREA)
- Image Processing (AREA)
Abstract
The invention belongs to the technical field of iron plate corrosion defect detection, and particularly relates to an iron plate corrosion defect detection method. The method comprises the following steps: obtaining an iron plate corrosion region growth segmentation image corresponding to the iron plate corrosion gray level image and each connected domain on the iron plate corrosion region growth segmentation image based on a region growth algorithm; obtaining different abnormal connected domains according to the area and color characteristic indexes; obtaining the area difference rate of each abnormal connected domain according to the area of each abnormal connected domain; acquiring a neighborhood pixel point set of each abnormal connected domain; obtaining the average distance corresponding to each abnormal connected domain according to the distance from each pixel point in the neighborhood pixel point set corresponding to each abnormal connected domain to the corresponding abnormal connected domain; finally, obtaining an abnormal communication domain of the vacuum hole according to the area difference rate and the average distance; and repairing the abnormal communication region which is the vacuum hole to obtain an iron plate corrosion region. The invention can accurately determine the rust area of the iron plate.
Description
Technical Field
The invention relates to the technical field of iron plate corrosion defect detection, in particular to an iron plate corrosion defect detection method.
Background
In industrial production and manufacturing, the iron plate is rusted to reduce the mechanical properties of the iron plate, such as strength, plasticity, toughness and the like, and the geometric shape of the iron plate is damaged to shorten the service life of the iron plate; at present, the most common mode for rusting the iron door in the industry is to carry out rust removal and paint repair operation on a rust area, the operation is often an area needing to obtain rusting, and the traditional threshold segmentation cannot play a good role in segmenting the rust, namely the condition that the color changes along with different rusting degrees, because a single threshold cannot be used.
Therefore, the identification and detection of the iron plate surface corrosion defects usually utilizes a region growing algorithm to divide and extract the defects, the algorithm is suitable for the scene with uncertain threshold values, and more accurate corrosion edges can be obtained; however, due to the limitation of the algorithm and the influence of noise, a phenomenon of missing void regions or overgrowth of the edges of the void regions may occur, and this phenomenon may result in low detection or identification accuracy of the iron plate corrosion defect regions.
Disclosure of Invention
The invention provides an iron plate corrosion defect detection method, which is used for solving the problem of lower precision when the existing method is used for detecting an iron plate corrosion defect area, and adopts the following technical scheme:
the embodiment of the invention provides a method for detecting iron plate corrosion defects, which comprises the following steps:
acquiring a gray image of iron plate corrosion; obtaining an iron plate corrosion area growth segmentation image corresponding to the iron plate corrosion gray image and each communication domain on the iron plate corrosion area growth segmentation image based on a region growth algorithm; the number of the pixel points in the connected domain is more than or equal to 1;
acquiring the area and color characteristic index of each connected domain; obtaining different abnormal connected domains according to the area and color characteristic indexes; obtaining the area difference rate of each abnormal connected domain according to the area of each abnormal connected domain; acquiring a neighborhood pixel point set of each abnormal connected domain; obtaining the average distance corresponding to each abnormal connected domain according to the distance from each pixel point in the neighborhood pixel point set corresponding to each abnormal connected domain to the corresponding abnormal connected domain; obtaining an abnormal connected domain which is a vacuum hole according to the area difference rate and the average distance;
and repairing the abnormal communication region which is the vacuum hole to obtain an iron plate corrosion region.
Preferably, the method for obtaining the area and color feature index of each connected domain includes:
acquiring the area of each connected domain; the area of each connected domain is measured by the number of pixel points in the connected domain;
and acquiring the mean value of the gray values of the pixels in each connected domain, and recording the mean value of the gray values of the pixels in each connected domain as the color characteristic index of each connected domain.
Preferably, the method for obtaining the different connected domains comprises:
recording a connected domain with the area smaller than a preset area threshold value and the color characteristic index of the corresponding connected domain between [220 and 255] as an abnormal connected domain; and marking the pixel points in the abnormal connected domain as abnormal points.
Preferably, the method of area difference ratio to each of the abnormally connected domains comprises:
acquiring the area of a standard noise connected domain; the number of the pixel points in the standard noise connected domain is
(ii) a The area of the standard noise connected component is recorded as
；
Obtaining the area difference rate of each abnormal connected domain according to the area of the standard noise connected domain and the area of each abnormal connected domain;
for any abnormal connected domain, calculating the area difference rate of the abnormal connected domain according to the following formula:
wherein,
as the area difference rate of the abnormal connected region,
the area of the abnormal connected region is defined as,
is the area of the standard noise connected domain,
and n is the number of the abnormal connected domains.
Preferably, the method for obtaining the neighborhood pixel point set of each abnormal connected domain includes:
for any abnormal connected domain:
constructing and obtaining a target circle corresponding to the abnormal connected domain by taking the abnormal connected domain as a circle center;
recording other abnormal points except the abnormal point in the abnormal connected domain in a target circle corresponding to the abnormal connected domain as neighborhood pixel points of the abnormal connected domain;
and constructing and obtaining a neighborhood pixel point set of the abnormal connected domain according to each neighborhood pixel point of the abnormal connected domain.
Preferably, the method for obtaining the average distance corresponding to each abnormal connected domain includes:
for any abnormal connected domain:
calculating the distance from each pixel point in the neighborhood pixel point set corresponding to the abnormal connected domain;
sorting according to the distance from small to large to obtain a corresponding distance sequence;
obtaining the average distance corresponding to the abnormal connected domain according to the distance sequence corresponding to the abnormal connected domain;
calculating the average distance corresponding to the abnormal connected domain according to the following formula:
wherein,
the average distance corresponding to the abnormal connected component is,
the number of pixels in the neighborhood pixel set corresponding to the abnormal connected domain,
the first parameter value in the distance sequence corresponding to the abnormal connected component,
the weight of the first parameter value in the distance sequence corresponding to the abnormal connected component,
the sum of the other parameter values except the first parameter value in the distance sequence corresponding to the abnormal connected component,
is composed of
The weight of (c).
Preferably, for any abnormal connected domain, the probability index of the abnormal connected domain being a vacuum hole is calculated according to the following formula:
wherein,
the abnormal connected region is an index of the possibility of vacuum holes,
the average distance corresponding to the abnormal connected domain is,
as the area difference rate of the abnormal connected component,
is composed of
The weight of (a) is determined,
is composed of
The weight of (c).
Firstly, acquiring an iron plate corrosion gray image; obtaining an iron plate corrosion area growth segmentation image corresponding to the iron plate corrosion gray image and each communication area on the iron plate corrosion area growth segmentation image based on an area growth algorithm; the number of the pixel points in the connected domain is more than or equal to 1; then obtaining the area and color characteristic index of each connected domain; obtaining various abnormal connected domains according to the area and color characteristic indexes; obtaining the area difference rate of each abnormal connected domain according to the area of each abnormal connected domain; acquiring a neighborhood pixel point set of each abnormal connected domain; obtaining the average distance corresponding to each abnormal connected domain according to the distance from each pixel point in the neighborhood pixel point set corresponding to each abnormal connected domain to the corresponding abnormal connected domain; finally, obtaining an abnormal connected domain of the vacuum hole according to the area difference rate and the average distance; and repairing the abnormal communication region which is the vacuum hole to obtain the iron plate corrosion region. The invention can accurately determine the rust area of the iron plate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for detecting iron plate corrosion defects according to the present invention;
FIG. 2 is a schematic diagram of an anomalous connected domain and neighborhood outliers of the anomalous connected domain of the present invention;
FIG. 3 is a schematic diagram of the distribution of edge pixel blocks according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art based on the embodiments of the present invention belong to the protection scope of the embodiments of the present invention.
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 embodiment provides a method for detecting iron plate corrosion defects, which is explained in detail as follows:
as shown in fig. 1, the method for detecting iron plate corrosion defects comprises the following steps:
s001, acquiring a gray image of iron plate corrosion; obtaining an iron plate corrosion area growth segmentation image corresponding to the iron plate corrosion gray image and each communication domain on the iron plate corrosion area growth segmentation image based on a region growth algorithm; and the number of the pixel points in the connected domain is more than or equal to 1.
Since the area growth algorithm for extracting the defect area in the prior art is limited and also influenced by noise, a phenomenon that a missing cavity area or an area edge overgrows may be generated, and the existence of the phenomenon can cause low detection or identification precision of the iron plate corrosion defect area, so that cavities without seed points put in and overgrowth parts need to be found and repaired, but in the searching process, the interference of signals can be generated when an image is collected to generate salt and pepper noise, the noise is also expressed as cavities in the image, the misjudgment of results is often generated, the interference is caused when the cavities (subsequently called as 'vacuum cavities') which are not put in seeds are searched, and the cavity information is lost in a traditional denoising mode.
The method comprises the steps of firstly, acquiring an iron plate image with a corrosion defect on the surface by using an image acquisition system, and recording the image as the iron plate corrosion image; graying the iron plate corrosion image to obtain an iron plate corrosion gray image; selecting a region with small gray value (with dark color) for casting seed points on the image after the graying treatment, and defining the gray value difference to be at a threshold value
Judging and combining the pixels in eight neighborhoods of the current seed pixel within the range belonging to the same kind of seed points; performing a region growing algorithm to obtain an effect image after the region growing and dividing and each region on the effect image after the region growing and dividing, recording the effect image after the region growing and dividing as an iron plate corrosion region growing and dividing image corresponding to the iron plate corrosion gray image, recording each region on the effect image after the region growing and dividing as each communicating region on the iron plate corrosion region growing and dividing image, wherein the number of pixel points in each communicating region is more than or equal to 1; the threshold value
The setting is required according to actual conditions.
S002, obtaining the area and color characteristic indexes of each connected domain; obtaining different abnormal connected domains according to the area and color characteristic indexes; obtaining the area difference rate of each abnormal connected domain according to the area of each abnormal connected domain; acquiring a neighborhood pixel point set of each abnormal connected domain; obtaining the average distance corresponding to each abnormal connected domain according to the distance from each pixel point in the neighborhood pixel point set corresponding to each abnormal connected domain to the corresponding abnormal connected domain; and obtaining an abnormal connected domain which is the vacuum hole according to the area difference rate and the average distance.
Next, in this embodiment, each obtained connected domain needs to be analyzed to obtain an abnormal point or area on the image, which is usually white, and then, further analysis is performed based on the size appearance of the abnormal area and the distribution in the image to eliminate the influence of noise, and a real cavity area is found out to be corrected, and then, abnormal point detection is performed on the edge of the obtained area to obtain a more accurate corrosion area.
For an iron plate corrosion area growth segmentation image obtained by using an area growth algorithm, adjacent pixel points with similar gray values on the image form the same communicated area, but because the area growth algorithm can only grow in continuous areas and uncertainty exists in selection of seed pixel points, the number of input seed pixel points and the number of input seed pixel points, missing holes can possibly occur due to the fact that partial areas do not grow, and the areas are not endowed with correspondingly classified pixel values after segmentation, so that the color presented in the image is white; the areas of the regions which are not grown by the seed points are generally small, the probability of being grown is very high as the areas are larger, and only small regions have small putting probability, so that the phenomenon of omission occurs; therefore, the present embodiment uses the above features as a basis for determining to find abnormal points or regions in the iron plate rust region growth segmentation image and make a unified mark; firstly, the embodiment acquires the area of each connected domain; the calculation of the connected component area is well known and will not be described in detail; then obtaining the mean value of the gray values of the pixel points in each connected domain, recording the mean value of the gray values of the pixel points in each connected domain as the color characteristic index of each connected domain, wherein the larger the mean value is, the closer the color of the connected domain is to white; in the embodiment, a connected domain with the area smaller than a preset area threshold value and the color characteristic index of the corresponding connected domain between [220 and 255] is marked as an abnormal connected domain; the preset area threshold needs to be set according to actual conditions, but the value of the preset area threshold needs to be greater than or equal to 1, and pixel points in an abnormal connected domain are marked as abnormal points.
Thus, abnormal connected domains on the growth segmentation image of the iron plate corrosion region are obtained.
Then, analyzing the abnormal connected domain to obtain a real cavity and eliminate noise interference; the areas of the cavity regions are different in size, so that the shapes of the cavity regions are irregular; in contrast, the appearance of salt and pepper noise is similar and is often represented as isolated pixel points on the image, so that the total area of the pixel points is smaller than that of the vacuum hole; and because the abnormal points in the abnormal area of the rusty part in the segmentation image are always noise (if the abnormal points are missed holes, the abnormal points are included by a normal growth mode and are not in accordance with logic), the abnormal point characteristics in the rusty part can be used as a reference template to be distinguished by comparing the external size characteristics of the abnormal points in other areas.
Firstly, analyzing the area of an abnormal connected domain, wherein the area of the connected domain is measured by the number of pixel points; because the abnormal points are abrupt, the gray difference of the abnormal points relative to surrounding pixel points is large, and the abnormal points are similar to an abrupt state; therefore, abnormal points in the rust can be acquired according to the gradient value; therefore, in this embodiment, the area of the standard noise connected domain is obtained first, and the number of the pixel points in the standard noise connected domain is
Then the area of the standard noise connected domain is recorded as
(ii) a When judging abnormal points in other areas, if the area is larger than
Then the region is likely to be a vacuum hole region, where the difference between the area of the current anomaly point and the reference template area can be made, the larger the value obtained, the more likely it is to be a vacuum hole, according to the standardObtaining the area difference rate of each abnormal connected domain by the area of the noise connected domain and the area of each abnormal connected domain; for any abnormal connected domain, calculating the area difference rate of the abnormal connected domain according to the following formula:
wherein,
as the area difference rate of the abnormal connected component,
the area of the abnormal connected region is defined as,
is the area of the standard noise connected domain,
the average value of the areas of the abnormal connected domains, wherein n is the number of the abnormal connected domains; when in use
When the abnormal connected domain is noise, the probability of the abnormal connected domain being noise is higher
If the value is more than 1, the probability that the abnormal connected domain is a vacuum hole is higher.
And then calculating the distance distribution condition of each abnormal connected domain from the nearest abnormal point around the abnormal connected domain in the image to obtain the discrete condition of each abnormal connected domain, and judging whether the point is a vacuum hole according to the discrete distribution condition, wherein the specific logic is as follows: because the distribution of noise is relatively common in the whole image, the whole image is filled with the noise, and the number of noise points is far larger than that of vacuum holes, the nearest point from the current point to the periphery can be calculated in the judgment of the discrete distribution degree of the abnormal connected domain
The mean value of the distances of the abnormal points represents the discrete condition of the abnormal points; then the point at which the discrete value is more abnormal (most recent)
Small distance mean) can be determined as a point with a high likelihood of a vacuum hole.
The basis for judging whether the abnormal communication area is a vacuum hole is as follows: the salt and pepper noise is distributed relatively evenly in the local space and relatively regular, so that the distance between the noise point and the noise point is relatively even; if the abnormal point is a vacuum hole, the distance between the abnormal point and the surrounding pixel points is closer, namely the density is higher than that of other points in the field.
The discrete degree of each abnormal connected domain in the space is reflected by the judgment of the distance between each abnormal connected domain and the surrounding abnormal points; for any anomalous connected domain: using the abnormal connected domain as the center of a circle and the abnormal points in any direction around the abnormal connected domain as the search target range to find out the point nearest to the current center of the circle
The distance of each abnormal point is stored in the set
Recording the abnormal connected domain as a neighborhood pixel point set of the abnormal connected domain, namely constructing and obtaining a target circle corresponding to the abnormal connected domain by taking the abnormal connected domain as a circle center, wherein the radius of the target circle needs to be determined according to actual conditions, and recording other abnormal points except the abnormal point in the abnormal connected domain in the target circle corresponding to the abnormal connected domain as neighborhood pixel points of the abnormal connected domain; if the current connected domain is a vacuum hole, the distribution of the connected domain in the image can disturb salt and pepper noise which is regularly distributed in the image originally, and because the noise point and the cavity point in the space can be sequentially used as central points to be judged according to the mode, the distance between the noise point and the cavity point and the first abnormal point closest to the noise point and the cavity point can be influenced by the vacuum hole; such asAs shown in FIG. 2, assume that
Is a hollow hole, the rest
Some small points around the image are the salt and pepper noise in the original image, and the noise is passed through
Will be subjected to surrounding abnormal points
The influence of (since the appearance of the vacuum hole can cause the distance between the adjacent abnormal point and the vacuum hole to be the nearest distance, these points are abnormal points), the abnormal point around the abnormal point which is the nearest to itself is the cavity, i.e. the nearest distance is the distance from itself to the cavity; and the rest noise points are nearest to the noise points except the affected noise points
The distance mean value difference of the abnormal points is not large; in order to be unaffected by the distance of the nearest outlier, appropriate weights need to be assigned.
For any abnormal connected domain: calculating the distance from each pixel point in the neighborhood pixel point set corresponding to the abnormal connected domain; sorting according to the distance from small to large to obtain a corresponding distance sequence; obtaining the average distance corresponding to the abnormal connected domain according to the distance sequence corresponding to the abnormal connected domain; calculating the average distance corresponding to the abnormal connected domain according to the following formula:
wherein,
is the average corresponding to the abnormal connected domainThe average distance is equal to the sum of the distances,
the number of pixels in the neighborhood set of pixels corresponding to the abnormal connected domain,
the first parameter value in the distance sequence corresponding to the abnormal connected component,
the weight of the first parameter value in the distance sequence corresponding to the abnormal connected component,
the sum of the other parameter values except the first parameter value in the distance sequence corresponding to the abnormal connected component,
is composed of
The weight of (c). The distance data interference brought by the first abnormal point is prevented
Assign less weight and thus set
、
。
Calculating the average of each abnormal connected domain in the space according to the formula
A distance; the smaller the value, the more dense the current abnormal connected domain is proved to be, i.e. the more probable the vacuum hole is. Thus when
Greater than 1 and larger values are more likely to be vacuum holes, and smaller average distances are more likely to be vacuum holes.
For any abnormal connected domain, calculating the possibility index of the abnormal connected domain as the vacuum hole according to the following formula:
wherein,
the abnormal connected region is an index of the possibility of vacuum holes,
the average distance corresponding to the abnormal connected domain is,
as the area difference rate of the abnormal connected region,
is composed of
The weight of (a) is determined,
is composed of
The weight of (c);
and
representing a weight, wherein an appropriate weight is given to the judged influence according to the difference rate and the discrete degree; due to flat distance versus densityIs more objective than the calculation of the difference rate of the former and is set accordingly
0.4，
(ii) a Due to the fact that
And holes of suspected vacuum
Is proportional to the value of (c) in the sample,
and holes of suspected vacuum
Is inversely proportional to the value of (a); the hyperbolic tangent function and the exponential function in the above equation are chosen to normalize the results.
And S003, repairing the abnormal communication region which is the vacuum hole to obtain an iron plate corrosion region.
In the embodiment, the abnormal communication region which is the vacuum hole is corrected to be the color of the rusty region, so as to obtain specific rusty region data; the steps are comprehensively analyzed and judged from two aspects to obtain the part belonging to the vacuum hole in the abnormal point region in the image, and the vacuum hole part is repaired to obtain a specific corrosion region; at this time, abnormal point detection needs to be performed on the edge of the segmented image, because some errors may exist in the effect of the combination processing of the algorithm and the actual scene in the segmentation process, which causes abnormal burrs or small protruding pixel points on the edge, and this step is used to specifically solve the edge defect. The information characteristics of a certain pixel point on the edge relative to the front and rear edge points under the normal condition are known to be coherent, if the point is an abnormal point, the point has some salience for the front and rear edge points, the abnormal change condition of the edge curve can be analyzed through chain code operation, and the abnormal point can be found according to the mutation of numerical values. Selecting 8 connected chain codes, comparing with the actual situation that 4 connected chain codes are more consistent with pixel point distribution, describing that the obtained adjacent pixel point information is more accurate, firstly randomly finding a starting point on the edge, starting traversing the edge point information in the clockwise direction, and expressing the information by using a numerical value in the 8 connected domain chain codes according to the position of the next pixel point relative to the current pixel point; the edge pixel point distribution as in fig. 3 can be represented by 8 connected domain chain codes as: 1110070; searching pixel points in the image corresponding to the mutation data by analyzing the obtained coded data information to obtain abnormal points in the edge points, and then repairing the points; and finally, a more accurate rusted area is obtained.
Through the steps, a more accurate rust area can be obtained, the rust degree of the iron plate is judged according to the obtained segmentation image, the influence degree of rust and the difficulty degree of rust removal are evaluated, and classification processing is performed in a targeted manner.
In the embodiment, firstly, an iron plate corrosion gray image is obtained; obtaining an iron plate corrosion area growth segmentation image corresponding to the iron plate corrosion gray level image and each connected domain on the iron plate corrosion area growth segmentation image based on a region growth algorithm; the number of the pixel points in the connected domain is more than or equal to 1; then obtaining the area and color characteristic index of each connected domain; obtaining various abnormal connected domains according to the area and color characteristic indexes; obtaining the area difference rate of each abnormal connected domain according to the area of each abnormal connected domain; acquiring a neighborhood pixel point set of each abnormal connected domain; obtaining the average distance corresponding to each abnormal connected domain according to the distance from each pixel point in the neighborhood pixel point set corresponding to each abnormal connected domain to the corresponding abnormal connected domain; finally, obtaining an abnormal connected domain of the vacuum hole according to the area difference rate and the average distance; and repairing the abnormal communication region which is the vacuum hole to obtain the iron plate corrosion region. The embodiment can accurately determine the rust area of the iron plate.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; the modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application, and are included in the protection scope of the present application.
Claims (7)
1. A method for detecting iron plate corrosion defects is characterized by comprising the following steps:
acquiring a gray image of iron plate corrosion; obtaining an iron plate corrosion region growth segmentation image corresponding to the iron plate corrosion gray level image and each connected domain on the iron plate corrosion region growth segmentation image based on a region growth algorithm; the number of the pixel points in the connected domain is more than or equal to 1;
acquiring the area and color characteristic index of each connected domain; obtaining various abnormal connected domains according to the area and color characteristic indexes; obtaining the area difference rate of each abnormal connected domain according to the area of each abnormal connected domain; acquiring a neighborhood pixel point set of each abnormal connected domain; obtaining the average distance corresponding to each abnormal connected domain according to the distance from each pixel point in the neighborhood pixel point set corresponding to each abnormal connected domain to the corresponding abnormal connected domain; obtaining an abnormal communication domain of the vacuum hole according to the area difference rate and the average distance;
and repairing the abnormal communication region which is the vacuum hole to obtain an iron plate corrosion region.
2. The iron plate corrosion defect detection method of claim 1, wherein the method for obtaining the area and color characteristic indexes of each connected domain comprises the following steps:
acquiring the area of each connected domain; the area of each connected domain is measured by the number of pixel points in the connected domain;
and acquiring the mean value of the gray values of the pixels in each connected domain, and recording the mean value of the gray values of the pixels in each connected domain as the color characteristic index of each connected domain.
3. The iron plate corrosion defect detection method of claim 1, wherein the method for obtaining each abnormal connected domain comprises:
recording connected domains with the areas smaller than a preset area threshold value and corresponding color characteristic indexes of the connected domains between [220 and 255] as abnormal connected domains; and marking the pixel points in the abnormal connected domain as abnormal points.
4. The iron plate corrosion defect detection method of claim 1, wherein the method for obtaining the area difference rate of each abnormal connected domain comprises:
acquiring the area of a standard noise connected domain; the number of the pixel points in the standard noise connected domain is
(ii) a The area of the standard noise connected component is recorded as
；
Obtaining the area difference rate of each abnormal connected domain according to the area of the standard noise connected domain and the area of each abnormal connected domain;
for any abnormal connected domain, calculating the area difference rate of the abnormal connected domain according to the following formula:
wherein,
as the area difference rate of the abnormal connected region,
the area of the abnormal connected region is defined as,
is the area of the standard noise connected component,
and the average value of the areas of the abnormal connected domains, wherein n is the number of the abnormal connected domains.
5. The iron plate rust defect detection method of claim 3, wherein the method for obtaining the neighborhood pixel point set of each abnormal connected domain comprises the following steps:
for any anomalous connected domain:
constructing and obtaining a target circle corresponding to the abnormal communication domain by taking the abnormal communication domain as a circle center;
recording other abnormal points except the abnormal point in the abnormal connected domain in a target circle corresponding to the abnormal connected domain as neighborhood pixel points of the abnormal connected domain;
and constructing and obtaining a neighborhood pixel point set of the abnormal connected domain according to each neighborhood pixel point of the abnormal connected domain.
6. The iron plate corrosion defect detection method of claim 5, wherein the method for obtaining the average distance corresponding to each abnormal connected domain comprises:
for any abnormal connected domain:
calculating the distance from each pixel point in the neighborhood pixel point set corresponding to the abnormal connected domain;
sorting according to the distance from small to large to obtain a corresponding distance sequence;
obtaining the average distance corresponding to the abnormal connected domain according to the distance sequence corresponding to the abnormal connected domain;
calculating the average distance corresponding to the abnormal connected domain according to the following formula:
wherein,
the average distance corresponding to the abnormal connected domain is,
the number of pixels in the neighborhood pixel set corresponding to the abnormal connected domain,
the first parameter value in the distance sequence corresponding to the abnormal connected component,
the weight of the first parameter value in the distance sequence corresponding to the abnormal connected component,
the sum of the other parameter values except the first parameter value in the distance sequence corresponding to the abnormal connected component,
is composed of
The weight of (c).
7. The method for detecting the rust defect of the iron plate as claimed in claim 6, wherein for any abnormal connected domain, the probability index that the abnormal connected domain is a vacuum hole is calculated according to the following formula:
wherein,
the abnormal connected region is an index of possibility of vacuum hole,
the average distance corresponding to the abnormal connected domain is,
as the area difference rate of the abnormal connected region,
is composed of
The weight of (a) is calculated,
is composed of
The weight of (c).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211248930.XA CN115311304B (en) | 2022-10-12 | 2022-10-12 | Iron plate corrosion defect detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211248930.XA CN115311304B (en) | 2022-10-12 | 2022-10-12 | Iron plate corrosion defect detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115311304A true CN115311304A (en) | 2022-11-08 |
| CN115311304B CN115311304B (en) | 2023-04-14 |
Family
ID=83868325
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211248930.XA Active CN115311304B (en) | 2022-10-12 | 2022-10-12 | Iron plate corrosion defect detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115311304B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116363126A (en) * | 2023-05-30 | 2023-06-30 | 东莞市立时电子有限公司 | Welding quality detection method for data line USB plug |
| CN116485797A (en) * | 2023-06-25 | 2023-07-25 | 潍坊亚贝涂料有限公司 | Artificial intelligence-based paint color difference rapid detection method |
| CN117197127A (en) * | 2023-11-02 | 2023-12-08 | 广东乾威精密连接器有限公司 | Automobile terminal detection method based on machine vision |
| CN117649412A (en) * | 2024-01-30 | 2024-03-05 | 山东海天七彩建材有限公司 | Aluminum material surface quality detection method |
| CN118196025A (en) * | 2024-03-13 | 2024-06-14 | 深圳个元科技有限公司 | Visual detection method and system for gear appearance defects |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140270377A1 (en) * | 2011-11-30 | 2014-09-18 | Olympus Corporation | Image processing apparatus, image processing method, and computer-readable recording device |
| CN115049649A (en) * | 2022-08-12 | 2022-09-13 | 山东振鹏建筑钢品科技有限公司 | Reinforcing steel bar polishing and rust removing control method based on corrosion degree |
| CN115100203A (en) * | 2022-08-25 | 2022-09-23 | 山东振鹏建筑钢品科技有限公司 | Steel bar polishing and rust removing quality detection method |
-
2022
- 2022-10-12 CN CN202211248930.XA patent/CN115311304B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140270377A1 (en) * | 2011-11-30 | 2014-09-18 | Olympus Corporation | Image processing apparatus, image processing method, and computer-readable recording device |
| CN115049649A (en) * | 2022-08-12 | 2022-09-13 | 山东振鹏建筑钢品科技有限公司 | Reinforcing steel bar polishing and rust removing control method based on corrosion degree |
| CN115100203A (en) * | 2022-08-25 | 2022-09-23 | 山东振鹏建筑钢品科技有限公司 | Steel bar polishing and rust removing quality detection method |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116363126A (en) * | 2023-05-30 | 2023-06-30 | 东莞市立时电子有限公司 | Welding quality detection method for data line USB plug |
| CN116363126B (en) * | 2023-05-30 | 2023-08-22 | 东莞市立时电子有限公司 | Welding quality detection method for data line USB plug |
| CN116485797A (en) * | 2023-06-25 | 2023-07-25 | 潍坊亚贝涂料有限公司 | Artificial intelligence-based paint color difference rapid detection method |
| CN116485797B (en) * | 2023-06-25 | 2023-08-29 | 潍坊亚贝涂料有限公司 | Artificial intelligence-based paint color difference rapid detection method |
| CN117197127A (en) * | 2023-11-02 | 2023-12-08 | 广东乾威精密连接器有限公司 | Automobile terminal detection method based on machine vision |
| CN117649412A (en) * | 2024-01-30 | 2024-03-05 | 山东海天七彩建材有限公司 | Aluminum material surface quality detection method |
| CN117649412B (en) * | 2024-01-30 | 2024-04-09 | 山东海天七彩建材有限公司 | Aluminum material surface quality detection method |
| CN118196025A (en) * | 2024-03-13 | 2024-06-14 | 深圳个元科技有限公司 | Visual detection method and system for gear appearance defects |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115311304B (en) | 2023-04-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115311304B (en) | Iron plate corrosion defect detection method | |
| CN115829883B (en) | Surface image denoising method for special-shaped metal structural member | |
| CN117173184B (en) | Road construction quality detection method and system based on artificial intelligence | |
| CN115457041B (en) | Road quality identification and detection method | |
| CN115330783A (en) | Steel wire rope defect detection method | |
| CN115294120B (en) | Valve surface quality detection method based on image recognition | |
| CN116703251B (en) | Rubber ring production quality detection method based on artificial intelligence | |
| CN109685760B (en) | MATLAB-based SLM powder bed powder laying image convex hull depression defect detection method | |
| CN115294159B (en) | Method for dividing corroded area of metal fastener | |
| CN115115638B (en) | Oil leakage detection and judgment method for hydraulic system | |
| CN116309537A (en) | Defect detection method for oil stain on surface of tab die | |
| CN115690108A (en) | Aluminum alloy rod production quality evaluation method based on image processing | |
| CN106780347B (en) | Early loquat bruise identification method based on OCT image processing | |
| CN116468728B (en) | Production quality detection method for hardware stamping part die | |
| CN113838054A (en) | Mechanical part surface damage detection method based on artificial intelligence | |
| CN117611563B (en) | Plastic mold production defect detection method based on image processing | |
| CN116304963B (en) | Data processing system suitable for geological disaster early warning | |
| CN115841491B (en) | Quality detection method for porous metal material | |
| CN117911408B (en) | Road pavement construction quality detection method and system | |
| CN115511907B (en) | Scratch detection method for LED screen | |
| CN115115603A (en) | Automobile accessory flywheel surface detection method based on artificial intelligence | |
| CN117705815B (en) | Printing defect detection method based on machine vision | |
| CN115222735A (en) | Metal mold quality detection method based on pockmark defects | |
| CN117078688B (en) | Surface defect identification method for strong-magnetic neodymium-iron-boron magnet | |
| CN115063603B (en) | Wood annual ring line accurate extraction and restoration method based on edge information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |