CN113689432B - Detection method for identifying special point-like defects - Google Patents
Detection method for identifying special point-like defects Download PDFInfo
- Publication number
- CN113689432B CN113689432B CN202111252331.0A CN202111252331A CN113689432B CN 113689432 B CN113689432 B CN 113689432B CN 202111252331 A CN202111252331 A CN 202111252331A CN 113689432 B CN113689432 B CN 113689432B
- Authority
- CN
- China
- Prior art keywords
- clustering
- sample
- data
- class
- value
- 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
- 230000007547 defect Effects 0.000 title claims abstract description 114
- 238000001514 detection method Methods 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 11
- 206010027146 Melanoderma Diseases 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 15
- 230000000694 effects Effects 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000007689 inspection Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000012549 training Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 241001132374 Asta Species 0.000 description 1
- 102000002274 Matrix Metalloproteinases Human genes 0.000 description 1
- 108010000684 Matrix Metalloproteinases Proteins 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 230000000007 visual effect Effects 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Quality & Reliability (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a detection method for identifying special point defects, which comprises the steps of acquiring black point and pockmark defect data detected by a defect detection device after scanning and identification of an industrial camera, and taking the black point and pockmark defect data as sample data; carrying out center standardization processing on the sample data, removing unit limitation among characteristic physical quantities in the sample data, and converting the sample data into dimensionless pure numerical data; clustering sample data by using a fuzzy C-means clustering method; evaluating the clustering result using the interval statistics; and taking different clustering center number C values as the fuzzy C mean clustering numbers, judging the class with the sample size of 1 in the class C as the black point defect after clustering is finished, and judging the class with the sample size of more than 1 in the class C as the pockmarked point defect. The method improves the accuracy of detecting the black spot and pockmark defects by the equipment on the premise of not changing the existing industrial camera scanning mode, and has the advantages of saving time and labor, reducing project cost and improving defect detection efficiency.
Description
Technical Field
The invention relates to the technical field of special defect detection, in particular to a detection method for identifying special point defects.
Background
In the existing industrial quality inspection technology, a workpiece is usually fixed on an inspection machine, an industrial camera scans and identifies the workpiece according to a specific track in different dimensions of visual fields (the industrial camera can only identify an abnormality but cannot determine the cause of the abnormality), and a defect detection device detects the types of the scanned and identified defects, wherein most of the defects can be correctly identified and the types of the defects can be detected.
However, due to the fixed moving track of the industrial camera, some specific dense but discontinuous defects are respectively scanned and identified by the industrial camera into a plurality of similar other defects. In the industrial quality inspection standard, the pockmark defect belongs to a dense but discontinuous defect type, and the pockmark defect consists of a plurality of small point-shaped defects and appears in an aggregated state or in a flaky state. An industrial camera often identifies a single pockmark defect as a plurality of small punctate defects in a blocking manner, and under the influence, a defect detection device can wrongly judge the small punctate defects as a plurality of black spot defects with close distances, namely the defect detection device wrongly judges the originally single pockmark defect as a plurality of black spot defects with close distances, so that a detection result has a deviation with an actual industrial quality inspection standard.
The existing solution is to adopt a manual checking method to review the special point defects identified by the industrial camera one by one and manually mark and identify inaccurate defects again, so that the method consumes a large amount of manpower, increases project cost and is difficult to improve efficiency.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art.
Therefore, the invention provides a detection method for identifying special point defects, which improves the accuracy of equipment for detecting black point and pockmark defects on the premise of not changing the scanning mode of the existing industrial camera, and has the advantages of saving time and labor, reducing project cost and improving defect detection efficiency.
The detection method for identifying the special point-like defects according to the embodiment of the invention comprises the following steps:
step 1, acquiring black dot and pockmark defect data detected by a defect detection device after scanning and identification of an industrial camera, and taking the black dot and pockmark defect data as sample data;
and 4, evaluating the clustering result by using interval statistics: calculating the Euclidean distance square sum of samples in each class after clustering, and using the Euclidean distance square sum as the class compactnessTo indicate the degree of intra-class compactnessThe calculation formula of (2) is:
wherein,representing the kth cluster center;andis class kThe sample of (1);to representAndthe euclidean distance between;
wherein,representing a cluster number ofKInterval statistics of time;to representNumber of cluster centers in a cluster;is that(iii) a desire;to representThe logarithm of (d);
The method has the advantages that the method is cooperated with the defect detection device and is superposed behind the defect detection device, the execution efficiency is improved under the condition that the dynamic requirements of an industrial field are met, the training cost and the influence of the existing industrial camera movement track and the defect detection device are reduced, the special defects are independently processed by adopting the detection method for identifying the special point defects, the method does not need to consume a large amount of labor, the accuracy is high, the method is particularly suitable for the condition of large sample quantity, and the method can assist in finishing the accurate detection and division of the defects of multiple projects and multiple defects.
Further specifically, in the above technical solution, in the 4 th step,the smaller, theThe smaller the intra-class distance of this class, the more compact the clustering(ii) a The larger the Gap value, the better the clustering effect at this cluster number.
More specifically, in the above technical solution, in the 5 th step, the value set of C is 2, 3, 4, … …, n × 2/3, where n represents the number of data samples, n is a positive integer greater than or equal to 1, n × 2/3 is rounded upward, and the 3 rd step and the 4 th step are repeated, so as to obtain (n × 2/3-1) Gap values, where n × 2/3 is rounded upward.
Further specifically, in the above technical solution, in step 1, consistency check, missing value and abnormal value processing are performed on the sample data set, whether data is missing is checked, if data is missing, the data is deleted, whether a numerical value is within a range of an actual physical quantity value is checked, and if the numerical value is out of the range, the data is deleted.
More specifically, in the above technical means, in the 2 nd step, a characteristic physical quantity sequence is subjected toCarrying out normalization conversion, wherein n represents the number of data sample quantities, and n is a positive integer greater than or equal to 1, and the calculation formula of the center normalization processing is as follows:
wherein,representing the characteristic physical quantity after central standardization conversion;to be a value range of [1, n]A positive integer of (d);represents a characteristic physical quantity;representing an ith value of a characteristic physical quantity;a mean value representing a characteristic physical quantity sequence z;represents the standard deviation;
the center-normalized sequence of certain characteristic physical quantities isRemoval of standard deviationIs a characteristic physical quantity of 0.
More specifically, in the above technical solution, the mean value of the characteristic physical quantity sequence zThe calculation formula of (2) is:
wherein n represents the number of data sample quantities, and n is a positive integer greater than or equal to 1;representing the ith value of a characteristic physical quantity.
More specifically, in the above technical solution, the standard deviationThe calculation formula of (2) is:
wherein n represents the number of data samples and n is 1 or moreA positive integer;representing an ith value of a characteristic physical quantity;represents the mean value of the characteristic physical quantity sequence z.
Further specifically, in the above technical solution, in step 3, a weight is given to each sample and each cluster, the class to which the weight of each sample is the largest is classified, the range of the weight is [0,1], the closer to 1 the sample is, the larger the weight is, the closer to 0 the sample is, the smaller the weight is, and the number m of clusters and the number C of cluster centers of the clusters need to be determined before the sample data is clustered by the fuzzy C-means clustering method, where m is a positive integer with the range of [1, ∞).
Further specifically, in the above technical solution, the clustering method specifically includes the following steps:
Step 3.3, recalculating the weight matrix U, which is recorded as,tIs shown astPerforming secondary iteration;
3.4, calculating the error sum of squares of the iteration, and when the error sum of squares is relatively small, achieving a better clustering result;
step 3.5, repeating the step 3.1, the step 3.2, the step 3.3 and the step 3.4, and iteratively calculating the clustering centerAnd weight matrixSum of squares of errors calculated iteratively up to t +1 th iterationSum of squares of errors calculated from the t-th iterationWhen the results are almost the same, taketC clustering centers and n x C weight matrix in the secondary iteration according to thetAnd determining the attribution type of each sample by the weight matrix after the secondary iteration according to the maximum membership principle in the fuzzy set.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of the detection method of the present invention;
FIG. 2 is a flow chart of clustering;
FIG. 3 is a schematic diagram of a defect captured by an industrial camera and identified by an algorithm;
FIG. 4 is a diagram illustrating fuzzy C-means clustering results when the C value of the clustering center number is equal to 2;
FIG. 5 is a diagram illustrating the fuzzy C-means clustering results when the C value is equal to 3;
FIG. 6 is a diagram illustrating the fuzzy C-means clustering results when the C value is equal to 4;
fig. 7 is a graph of Gap value distribution for fuzzy C-means clustering with different C-value of the cluster center number.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1 and fig. 2, the detection method for identifying a special point-like defect of the present invention includes the following steps:
step 1, acquiring black point and pockmark defect data detected by a defect detection device after scanning and identification of an industrial camera, taking the black point and pockmark defect data as sample data, wherein the sample data does not have a label for distinguishing defect types because a detection result of the defect detection device cannot be completely relied, only keeping sample characteristic physical quantity, carrying out consistency check on the sample data set, processing missing values and abnormal values, checking whether the data is missing or not, deleting the data if the data is missing, checking whether a numerical value is in an actual characteristic physical quantity value range or not, and deleting the data if the numerical value is out of the range.
wherein,representing the characteristic physical quantity after central standardization conversion;to be a value range of [1, n]A positive integer of (d);represents a characteristic physical quantity;representing an ith value of a characteristic physical quantity;a mean value representing a characteristic physical quantity sequence z;represents the standard deviation;
wherein n represents the number of data sample quantities, and n is a positive integer greater than or equal to 1;representing the ith value of a characteristic physical quantity.
wherein n represents the number of data sample quantities, and n is a positive integer greater than or equal to 1;representing an ith value of a characteristic physical quantity;represents the mean value of the characteristic physical quantity sequence z.
The center-normalized sequence of certain characteristic physical quantities isRemoval of standard deviationCharacteristic physical quantity of 0, standard deviationA value of 0 indicates that the characteristic physical quantity has the same performance (the value remains unchanged) on each piece of data in the data set, and the characteristic physical quantity is a redundant characteristic and is subjected to deletion processing.
And 3, clustering the sample data by using a fuzzy C-means clustering method, wherein the principle is that each sample and each cluster are endowed with a weight (the weight represents the degree of the designated sample belonging to the cluster), the class with the largest weight of each sample belongs to the class, the weight is in a value range of [0,1], the closer to 1, the larger the weight is, and the closer to 0, the smaller the weight is. Before carrying out fuzzy C-means clustering on sample data, the cluster number (cluster number in cluster) m and the cluster center number C of the cluster need to be determined, wherein m is a positive integer with the value range of [1, ∞ ], but the parameter of the cluster number m is usually set to be 2. Firstly, selecting the number m =2 of clusters and the number C =2 of cluster centers to perform fuzzy C-means clustering. The clustering method comprises the following specific steps:
3.1, randomly generating a weight matrix of n × C(ii) a Since the sample data has been processed by center standardization, the weight matrixIn (2) is distributed at [0,1]]To (c) to (d); n represents the number of data sample quantities, and n is a positive integer greater than or equal to 1; c represents the clustering center number, the value range is [2,p]is a positive integer of (a) to (b),pis the characteristic number of sample data, andpis a positive integer greater than or equal to 1;the middle U represents a weight matrix and,0 in the value indicates that the weight matrix value is an initial random value, namely the 0 th iteration; weight matrixThe formula of (1) is as follows:
wherein,is shown astA second iteration, andt∈[0,∞);a first cluster center representing a first sample;a second cluster center representing the first sample;a third cluster center representing the first sample;a c-th cluster center representing a first sample;a first cluster center representing a second sample;a second cluster center representing a second sample;a third cluster center representing a second sample;a c-th cluster center representing a second sample;a first cluster center representing a third sample;a second cluster center representing a third sample;a third cluster center representing a third sample;a c-th cluster center representing a third sample;a first cluster center representing an nth sample;a second cluster center representing an nth sample;a third cluster center representing the nth sample;the c-th cluster center of the n-th sample is represented.
wherein,is shown asjThe center of each cluster is determined by the center of each cluster,j∈[1,p],pis the characteristic number of sample data, andpis a positive integer greater than or equal to 1;is shown asiThe number of the samples is one,i∈[1,n]n represents the number of data samples, and n is a positive integer greater than or equal to 1;representing a sampleBelong to the firstjThe weight of each cluster center;representing a sampleBelong to the firstjDegree of membership (i.e. weight) of individual cluster centersmThe power of the next power;is obtained from the value matrix U;representing the number of clusters (number of clusters in a cluster); wherein,
step 3.3, recalculating the weight matrix U, which is recorded as,tIs shown astPerforming secondary iteration; weight matrixThe weight calculation method comprises the following steps:
wherein,representing a sampleBelong to the firstjThe weight of each cluster center;representing the number of cluster centers;is shown asjThe center of each cluster is determined by the center of each cluster,j∈[1,p],pis the characteristic number of sample data, andpis a positive integer greater than or equal to 1;is shown askThe center of each cluster is determined by the center of each cluster,k∈[1,p],pis the characteristic number of sample data, andpis a positive integer greater than or equal to 1;representing a sampleTo the firstjEuclidean distance of individual cluster centers;representing a sampleTo the firstkEuclidean distance of individual cluster centers; by a samplegAnd a samplehFor example, the Euclidean Distance (Euclidean Distance) calculation method is as follows:
wherein,pis the characteristic number of sample data, andpis a positive integer greater than or equal to 1;is shown asThe characteristics of the device are as follows,∈[1,p],is a positive integer;representing a sampleTo the sampleThe Euclidean distance of (c);
step 3.4, calculating the Sum of squares of Errors (SSE for short) of the iteration, and when the Sum of squares of Errors is relatively small, achieving a better clustering result; the equation for the sum of the squares of the errors is:
wherein,representing a sampleBelong to the firstjDegree of membership (i.e. weight) of individual cluster centersmThe power of the next power;representing a sampleTo the firstjEuclidean distance of individual cluster centers.
Step 3.5, repeating the step 3.1, the step 3.2, the step 3.3 and the step 3.4, and iteratively calculating the clustering centerAnd weight matrixIs iterated toAndthe results of (a) are almost the same,denotes the t-thThe sum of the squared errors calculated for +1 iterations,representing the sum of squared errors calculated in the t-th iteration, takingtC clustering centers and n x C weight matrix in the secondary iteration according to thetAnd determining the attribution type of each sample by the weight matrix after the secondary iteration according to the maximum membership principle in the fuzzy set. This allows the class to which each sample in the sample dataset belongs to be determined at cluster center C = 2.
wherein,representing the kth cluster center;andis class kThe sample of (1);to representAndthe euclidean distance between;the smaller, theThe smaller the intra-class distance of the class is, the more compact the clustering is;
wherein,representing a cluster number ofKInterval statistics of time;representing the number of cluster centers in the cluster;is that(iii) a desire;to representThe logarithm of (d); the larger the Gap value, the better the clustering effect at this cluster number.
Calculating a Gap value when the current fuzzy C-means clustering used in the sample data is of type 2 (K = C = 2).
The defects of the black points and the pockmarks are very similar, and the difference is that the black points are a single block of defect, and the pockmarks are a plurality of blocks of black point-like defects which are compact but possibly discontinuous, and are gathered together, so that the category of the clustered sample amount not being 1 belongs to the pockmarks and accords with the form of the pockmarks, and the special point-like defects of the pockmarks can be detected.
See fig. 3, a certain workpiece portion is photographed using an industrial camera. The white rectangular frame is a defect marked one by the industrial camera and is recognized as a black point defect by the defect detection device. It can be clearly seen in the figure that the defect part subjected to labeling processing is difficult to accurately judge the type of the defect due to the reason that the cameras label one by one. The left area with one defect tightly clustered is a pock defect (industrial quality inspection standard), and the remaining three defects in the picture are black defects (industrial quality inspection standard). The pockmarked defects which are originally a whole piece are marked and processed separately, which may cause deviation of the detection result.
Referring to fig. 4, 5, and 6, when C = 4, the category of the fuzzy C-means clustering result where the samples are greater than 1 is a pockmark defect, and the category of the sample number 1 is a black spot defect. This indicates that when the fuzzy C-means clustering method is classified into 4 types, black spots and pockmark defects can be distinguished.
See fig. 7, which is a graph illustrating the Gap values of the fuzzy C-means clusters when C is equal to 2, 3, 4, and 5, respectively. As is obvious from the figure, when the cluster number is 2, the Gap value is between 0.35 and 0.4; when the cluster number is 3, the Gap value is 0.45; when the cluster number is 4, the Gap value is between 0.55 and 0.6; when the number of clusters is 5, the Gap value is 0.45. Obviously, when C =2, the clustering effect is poor; when C = 4, the clustering effect exhibits a relatively good state, and the Gap value is the highest. Therefore, the data are selected to be clustered into 4 classes, and the sample size of each clustered class conforms to the distribution form of black point and pockmark defects. The detection method for identifying the special point defects can effectively improve the accuracy of detecting the black point and pockmark defects by the equipment on the premise of not changing the scanning mode of the existing industrial camera, and overcomes the limitation that the shooting result of the industrial camera is checked manually at the present stage.
The method is cooperated with a defect detection device, is superposed behind the defect detection device, improves the execution efficiency under the condition of meeting the dynamic requirement of an industrial field, reduces the moving track of the existing industrial camera and the training cost and the influence of the defect detection device, adopts a detection method for identifying the special point defects to independently process the special defects, does not need to consume a large amount of labor, has high accuracy, is particularly suitable for the condition of large sample quantity, and can assist in finishing the accurate detection and division of the defects of multiple projects and multiple defects.
The detection method for identifying the special point defects fully meets the condition that the detection of the black point and pockmark defects is inaccurate due to the specific moving track of the industrial field camera and the large defect detection device. Under the condition that the existing moving track of the camera cannot be changed, the expression form of the black point defect is very similar to that of a part of pockmark defects, and the defect detection device is difficult to distinguish the defects when the defects are compared independently. Therefore, the detection method for identifying the special point-like defects is introduced, each defect marked by the camera is used as one piece of data, and the data are used for fuzzy C-means clustering with different types of numbers. And selecting the category number with better clustering effect for clustering by taking the Gap value as an evaluation standard, wherein each category is a defect after clustering is finished, the category number with the data volume of 1 is a black point defect, and the category number with the data volume of more than 1 is a pock point defect, so that the purpose of classifying the black point and the pock point defects is achieved. The method greatly reduces the training cost and influence of the existing defect detection device, and improves the accuracy and the execution efficiency of the industrial field on the premise of not damaging the defect detection device.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention are equivalent to or changed within the technical scope of the present invention.
Claims (9)
1. A detection method for identifying special point-like defects is characterized by comprising the following steps:
step 1, acquiring black dot and pockmark defect data detected by a defect detection device after scanning and identification of an industrial camera, and taking the black dot and pockmark defect data as sample data;
step 2, carrying out center standardization processing on the sample data, removing unit limits among characteristic physical quantities in the sample data, and converting the sample data into dimensionless pure numerical data;
step 3, clustering sample data by using a fuzzy C-means clustering method;
and 4, evaluating the clustering result by using interval statistics: calculating the Euclidean distance square sum of samples in each class after clustering, and using the Euclidean distance square sum as the class compactnessTo representDegree of like internal tightnessThe calculation formula of (2) is:
wherein,representing the kth cluster center;andis class kThe sample of (1);to representAndthe euclidean distance between;
wherein,representing a cluster number ofKInterval statistics of time;representing the number of cluster centers in the cluster;is that(iii) a desire;to representThe logarithm of (d);
step 5, taking different clustering center numbers C as fuzzy C mean clustering numbers, wherein C belongs to [1, n ], n represents the number of data sample quantities, and n is a positive integer greater than or equal to 1; repeating the step 3 and the step 4, thus obtaining n Gap values, and selecting the C value with the maximum Gap value as the optimal clustering number of the fuzzy C mean value to carry out fuzzy C mean value clustering; and after the clustering is finished, judging the class with the sample size of 1 in the class C as the black spot defect, and judging the class with the sample size of more than 1 in the class C as the pockmark defect.
2. The detection method for identifying special point-like defects according to claim 1, wherein: in the 4 th step, the first step is carried out,the smaller, theThe smaller the intra-class distance of the class is, the more compact the clustering is; the larger the Gap value, the better the clustering effect at this cluster number.
3. The detection method for identifying special point-like defects according to claim 1, wherein: in the 5 th step, the value set of C is 2, 3, 4, … …, n × 2/3, wherein n represents the number of data samples, n is a positive integer greater than or equal to 1, n × 2/3 is rounded up, and the 3 rd step and the 4 th step are repeated, so that (n × 2/3-1) Gap values are obtained, wherein n × 2/3 is rounded up.
4. The detection method for identifying special point-like defects according to claim 1, wherein: in step 1, the sample data set is subjected to consistency check, missing values and abnormal values are processed, whether data are missing or not is checked, if the data are missing, the data are deleted, whether the numerical value is within the range of the actual characteristic physical quantity measuring value is checked, and if the numerical value is beyond the range, the data are deleted.
5. The detection method for identifying special point-like defects according to claim 1, wherein: in step 2, a characteristic physical quantity sequence is measuredCarrying out normalization conversion, wherein n represents the number of data sample quantities, and n is a positive integer greater than or equal to 1, and the calculation formula of the center normalization processing is as follows:
wherein,representing the characteristic physical quantity after central standardization conversion;to be a value range of [1, n]A positive integer of (d);represents a characteristic physical quantity;representing an ith value of a characteristic physical quantity;a mean value representing a characteristic physical quantity sequence z;represents the standard deviation;
6. The detection method for identifying special point-like defects according to claim 5, wherein: mean value of characteristic physical quantity sequence zThe calculation formula of (2) is:
7. The detection method for identifying special point-like defects according to claim 5, wherein: standard deviation ofThe calculation formula of (2) is:
8. The detection method for identifying special point-like defects according to claim 1, wherein: in the step 3, a weight is given to each sample and each cluster, the class to which the weight of each sample is the largest belongs is determined, the value range of the weight is between [0,1], the closer to 1, the larger the weight is, the closer to 0, the smaller the weight is, and the cluster number m and the cluster center number C of the cluster need to be determined before the sample data is clustered by the fuzzy C-means clustering method, wherein m is a positive integer with the value range of [1, ∞).
9. The detecting method for identifying special point-like defects according to claim 8, wherein: the clustering method comprises the following specific steps:
3.4, calculating the error sum of squares of the iteration, and when the error sum of squares is relatively small, achieving a better clustering result;
step 3.5, repeating the step 3.1, the step 3.2, the step 3.3 and the step 3.4, and iteratively calculating the clustering centerAnd weight matrixSum of squares of errors calculated iteratively up to t +1 th iterationSum of squares of errors calculated from the t-th iterationWhen the results are almost the same, taketC clustering centers and n x C weight matrix in the secondary iteration according to thetAnd determining the attribution type of each sample by the weight matrix after the secondary iteration according to the maximum membership principle in the fuzzy set.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111252331.0A CN113689432B (en) | 2021-10-27 | 2021-10-27 | Detection method for identifying special point-like defects |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111252331.0A CN113689432B (en) | 2021-10-27 | 2021-10-27 | Detection method for identifying special point-like defects |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113689432A CN113689432A (en) | 2021-11-23 |
CN113689432B true CN113689432B (en) | 2022-02-08 |
Family
ID=78588251
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111252331.0A Active CN113689432B (en) | 2021-10-27 | 2021-10-27 | Detection method for identifying special point-like defects |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113689432B (en) |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPWO2018225406A1 (en) * | 2017-06-08 | 2020-04-09 | コニカミノルタ株式会社 | Method for determining arrangement of surface defect inspection apparatus, apparatus, program, and recording medium |
CN110987970A (en) * | 2019-10-26 | 2020-04-10 | 惠州高视科技有限公司 | Object surface defect detection system and detection method |
-
2021
- 2021-10-27 CN CN202111252331.0A patent/CN113689432B/en active Active
Also Published As
Publication number | Publication date |
---|---|
CN113689432A (en) | 2021-11-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113989279B (en) | Plastic film quality detection method based on artificial intelligence and image processing | |
CN109977808B (en) | Wafer surface defect mode detection and analysis method | |
CN115351598A (en) | Numerical control machine tool bearing detection method | |
CN113838054B (en) | Mechanical part surface damage detection method based on artificial intelligence | |
CN111242123B (en) | Power equipment fault diagnosis method based on infrared image | |
CN107085846B (en) | Workpiece surface defect image identification method | |
CN111080691A (en) | Infrared hot spot detection method and device for photovoltaic module | |
CN114723704A (en) | Textile quality evaluation method based on image processing | |
CN113537321B (en) | Network flow anomaly detection method based on isolated forest and X mean value | |
CN117115148B (en) | Chip surface defect intelligent identification method based on 5G technology | |
CN116091504A (en) | Connecting pipe connector quality detection method based on image processing | |
CN112561875A (en) | Photovoltaic cell panel coarse grid detection method based on artificial intelligence | |
CN117455917B (en) | Establishment of false alarm library of etched lead frame and false alarm on-line judging and screening method | |
CN116778520B (en) | Mass license data quality inspection method | |
CN117252840B (en) | Photovoltaic array defect elimination evaluation method and device and computer equipment | |
CN113689432B (en) | Detection method for identifying special point-like defects | |
CN117593499A (en) | Hydropower station electromechanical equipment fault identification method based on distributed routing inspection strategy | |
CN113808129A (en) | Effective detection method for confusable defects | |
CN112132823A (en) | Photovoltaic cell image local uneven illumination processing method based on artificial intelligence | |
CN113780488A (en) | Scratching and scratching defect detection method based on position polymerization degree | |
CN115761211A (en) | Petrochemical enterprise pump room equipment temperature monitoring method and device based on RGB image and thermal imaging coupling | |
CN112508946A (en) | Cable tunnel abnormity detection method based on antagonistic neural network | |
CN112529835A (en) | Image splicing tampering detection and positioning method based on source camera identification | |
CN117523324B (en) | Image processing method and image sample classification method, device and storage medium | |
CN117690846B (en) | Visual detection method, device and equipment for solar silicon wafer and storage medium |
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 |