CN110135477A - A kind of strip surface quality defect classifier and its classification method based on serial/parallel row integrated study frame - Google Patents

A kind of strip surface quality defect classifier and its classification method based on serial/parallel row integrated study frame Download PDF

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CN110135477A
CN110135477A CN201910351038.6A CN201910351038A CN110135477A CN 110135477 A CN110135477 A CN 110135477A CN 201910351038 A CN201910351038 A CN 201910351038A CN 110135477 A CN110135477 A CN 110135477A
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CN110135477B (en
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曾亮
常雨芳
黄文聪
徐操
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Hubei University of Technology
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Abstract

The invention proposes a kind of strip surface quality defect classifier and its classification method based on serial/parallel row integrated study frame, specifically include: a kind of classifier structure based on serial/parallel row integrated study frame, merge with the defect that the classifier structure is adapted and stage division and mischief rule table construction method.Compared with traditional technology, the invention has the following advantages that classifier based on serial/parallel row integrated study frame change it is common " multiple classifiers are simply parallel; then final classification result is determined by Nearest Neighbor with Weighted Voting " thinking, traditional parallel sorting device structure of modification at unique serial/parallel row integrated morphology.By sufficiently merging newest machine learning classification identification technology, defect mechanism of production and artificial experience rule, it is aided with the defect being adapted with classifier structure to merge and stage division, the accuracy of strip surface quality defect Classification and Identification is further improved, and improves self-learning capability, real-time performance and the generalization ability of classifier.

Description

A kind of strip surface quality defect classifier based on serial/parallel row integrated study frame And its classification method
Technical field
The invention belongs to strip surface quality defects detections and sorting technique field, are related to computer vision and engineering Learning method is related specifically to a kind of strip surface quality defect classification method based on serial/parallel row integrated study frame.
Background technique
Quality is the soul of modern enterprise, is the foundation stone that enterprise depends on for existence and development.
In the mainstream industry class of multiple national economy such as steel, papermaking, weaving, printing and dyeing and glass, product surface matter The superiority and inferiority of amount will directly affect the final quality of product, performance and value, simultaneously as downstream client is to product surface quality It is required that more and more harsh, good surface quality becomes the important magic weapon that enterprise wins in fierce market competition, thus, There is extensive product surface qualities to detect demand in these industries.
The present invention relates to one of major products of steel and iron industry --- and the surface quality defect detection classification of strip is asked Topic.It is traditionally general using artificial range estimation or offline sampling observation or the two in order to accurately identify strip surface quality defect The mode combined.But with the development of steel and iron industry production level, traditional detection mode obviously has been unable to meet the modern times The requirement of industrial production high speed, high yield and high-quality is unable to get the surface quality detection result of continuous, stable multiplicity.Machine The extensive use of device vision technique and flourishing for artificial intelligence theory provide for surface quality detection problem in every profession and trade Brand-new thinking and solution route.Also it is based on this, has studied the problem gradually using machine vision and artificial intelligence approach Hot spot as domestic and foreign scholars, academic institution and business research.
Summary of the invention
The classification of strip surface quality defect is that multiple features under the conditions of a complicated production, multi-class pattern-recognition are asked Topic.The accuracy and stability that improve Classification and Identification are the key scientific problems that classifier design is faced.Existing more classification Device combined method often " multiple classifiers are simply parallel, and then Nearest Neighbor with Weighted Voting determines classification results ", it is no it is any pretreatment and The link of centre intervention.Not high for such as classification accuracy present in traditional Combination of Multiple Classifiers method, classifier is learnt by oneself The problems such as habit ability and generalization ability are weak, real-time performance is bad, the invention proposes one kind to be based on serial/parallel row integrated study frame Strip surface quality defect classification method.
The technical solution adopted by the present invention includes following technology contents:
A kind of strip surface quality defect classifier based on serial/parallel row integrated study frame, which is characterized in that classifier It include rule list and Machine learning classifiers for the three layers of serial structure divided from top to bottom, specifically:
Upper layer is rule list,
Middle layer is multiple parallel Machine learning classifiers,
Lower layer is rule list or single Machine learning classifiers.
Rule list is presented as a kind of bivariate table in structure, specific comprising two column, be respectively serial number and to it is regular it is specific in The description of appearance.The line number of rule list is determined by regular quantity.
In a kind of above-mentioned strip surface quality defect classifier based on serial/parallel row integrated study frame, rule list is The process mechanism that binding deficient generates, and the experience and knowledge of live expert is used for reference and absorbs, the classification formed after inverted Decision rule.In selecting when multiple collateral learning classifiers of interbed, focus on their mutual complementarity, rule list Construction step includes:
S201 finds a value range feature Chong Die with other all categories zero, if having found such feature, Then this feature is added in the rule list of upper layer, is the defect of upper layer level by the defect classification that this feature is distinguished.
S202 finds a value range and other Chong Die features of particular category (not all classification) zero, if found Then this feature is added in lower layer's rule list for such feature, is lacking for lower layer level by the defect classification that this feature is distinguished It falls into.
S203 executes traversal than choosing to all features, until terminating.
In a kind of above-mentioned strip surface quality defect classifier based on serial/parallel row integrated study frame, middle layer dress With random forest grader, self organizing neural network and the three kinds of classifiers of GentlBoost classifier encoded based on ECOC.This Invention proposes the defect classifier based on serial/parallel row integrated study frame, is exactly in order to fully integrate existing machine learning point Class identification technology, defect mechanism of production and artificial experience rule, further increase accuracy and the classifier of Classification and Identification Self-learning capability, real-time performance and generalization ability.
A kind of classification method of the defect classifier based on serial/parallel row integrated study frame, which is characterized in that including defect Merging and stage division, the specific steps are as follows:
S1 is isolated as the defect for being included in upper layer rule list the defect of upper layer level；
S2 compares the characteristic value of two kinds of defect sample in training set, if two kinds of defect classification sequence numbers are respectively yiAnd yj, instruction Practicing the corresponding sample size of concentration is respectively ciAnd cj, characteristic value dimension is n dimension, and defect characteristic value sequence is respectively (xi1, xi2,…,xik,…,xin) and (xj1,xj2,…,xjk,…,xjn), wherein xikAnd xjkIn respectively two kinds of defect characteristic value sequences Component, calculate the mean value of two kinds of defect characteristic value respective components, be denoted as:WithK=1 ..., n, Two mean bias percentages are further calculated, as follows:
Compare the respective components mean bias percentage and preset two threshold value δ1And δ2Size relation.If should Respective components mean bias percentage is less than threshold value δ1Eigenvalue components number account for all characteristic value dimensions percentage it is big In threshold value δ2, then corresponding two kinds of defects are merged.
The present invention is by threshold value δ1It is taken as 70%, threshold value δ2It is taken as 90%.
S3, if before not merging in the confusion matrix of two class defect classification results, mutual mistake divides rate to be greater than threshold Value δ3, then also two class defects are merged.
The present invention is by threshold value δ3It is taken as 20%.
S4, the defect after merging are placed on middle layer level.
S5, other unassorted defects are also placed in middle layer level, i.e., other unassorted defects with merge after Defect is placed on same level, and with the defect classifier middle layer proposed by the present invention based on serial/parallel row integrated study frame Multiple parallel machine learning classification methods realize classification.
The invention has the following advantages that the classifier based on serial/parallel row integrated study frame changes commonly " by multiple points Class device is simply parallel, then determines final classification result by Nearest Neighbor with Weighted Voting " thinking, traditional parallel sorting device structure is changed Cause unique serial/parallel row integrated morphology.By sufficiently merging newest machine learning classification identification technology, defect mechanism of production With artificial experience rule, it is aided with the defect being adapted with classifier structure and merges and stage division, further improve strip table The accuracy of face mass defect Classification and Identification, and improve self-learning capability, real-time performance and the generalization ability of classifier.
Detailed description of the invention
Fig. 1 is the application defect classification method based on serial/parallel row integrated study frame of the invention mentioned to belt steel surface The flow diagram that mass defect is classified.
A kind of strip surface quality defect classifier based on serial/parallel row integrated study frame that Fig. 2 is mentioned for the present invention Structure chart.
Fig. 3 a is the five kinds of different types of typical defect pictures applied in the embodiment of the present invention two, and defect type is weldering Seam, Fig. 3 b are the five kinds of different types of typical defect pictures applied in the embodiment of the present invention two, and defect type is hole；
Fig. 3 c is the five kinds of different types of typical defect pictures applied in the embodiment of the present invention two, and defect type is to draw Wound；
Fig. 3 d is the five kinds of different types of typical defect pictures applied in the embodiment of the present invention two, and defect type is roller Print；
Fig. 3 e is the five kinds of different types of typical defect pictures applied in the embodiment of the present invention two, and defect type is oil Spot.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, with reference to the accompanying drawings and detailed description The present invention is described in further detail.
Embodiment one
Fig. 1 is the application defect classification method based on serial/parallel row integrated study frame of the invention mentioned to belt steel surface The flow diagram that mass defect is classified.
As shown in Figure 1, detailed process includes 3 key steps, it is as follows respectively:
Step 1, specific, the significant defect of feature difference is separated from all defect classification, with first layer Regular table sort；
Step 2, then according to process knowledges such as the formation mechenisms of defect, with defect merging method by appearance or feature It is worth close/similar defect to merge, Machine learning classifiers are only trained and classify to the defect classification after merging；
Step 3, the classification before merging is finely divided by the rule list of third layer and classifier.
Specifically, the construction method of above-mentioned first layer and the rule list of third layer is as follows:
Firstly, the value range feature Chong Die with other all categories zero is found, if having found such feature, Then this feature is added in the rule list of upper layer, is the defect of upper layer level by the defect classification that this feature is distinguished.
Then, a value range and other Chong Die features of particular category (not all classification) zero are found, if found Then this feature is added in lower layer's rule list for such feature, is lacking for lower layer level by the defect classification that this feature is distinguished It falls into.
It should be noted that traversal need to be executed to all features than choosing, until terminating.
A kind of strip surface quality defect classifier based on serial/parallel row integrated study frame that Fig. 2 is mentioned for the present invention Structure chart.
As shown in Fig. 2, the defect classifier based on serial/parallel row integrated study frame includes rule list and machine learning classification Two kinds of basic building blocks of device, are generally represented as a kind of serial structure, are divided into three layers from top to bottom, upper layer is rule list, middle layer It is multiple parallel Machine learning classifiers, lower layer is also rule list or single Machine learning classifiers.In addition to being embodied in structure Except serial/parallel row integrated study frame, the fusion to rule list is also one of the technical characteristic of the classifier.Rule list is knot The process mechanism that defect generates is closed, and uses for reference and absorb the experience and knowledge of live expert, the classification formed after inverted is sentenced Set pattern is then.In selecting when multiple collateral learning classifiers of interbed, focus on their mutual complementarity.
Preferably, the present invention is assembled random forest grader, self organizing neural network in middle layer and is encoded based on ECOC Three kinds of classification methods such as GentlBoost classifier.
Defect Multiple Classifier Fusion based on serial/parallel row integrated study frame priori rules and expertise, will be common Parallel sorting device structure of modification at unique serial/parallel row integrated morphology, taken into account strip surface quality defect operational characteristic and The versatility of classifying identification method is applied to other plate band-type products except strip (in such as paper industry for the classifier Paper and with the fabric etc. in textile industry) provide necessary prerequisite.When concrete application, in order to meet strip, paper Demand is detected with the surface quality defect of the different products such as fabric, is write after priori knowledge relevant to Product Process characteristic is concluded Enter in rule list, versatility is then embodied with the Study strategies and methods via the training of standardization sample library.
In order to give full play to rule list and machine learning classification in the defect classifier based on serial/parallel row integrated study frame The performance of device, the defect that the present invention also provides a kind of to be adapted with the defect classifier structure merges and stage division.
Specific step is as follows with stage division for defect merging:
S201 is isolated as the defect for being included in upper layer rule list the defect of upper layer level；
S202 compares the characteristic value of two kinds of defect sample in training set, if two kinds of defect classification sequence numbers are respectively yiAnd yj, Corresponding sample size is respectively c in training setiAnd cj, characteristic value dimension is n dimension, and defect characteristic value sequence is respectively (xi1, xi2,…,xik,…,xin) and (xj1,xj2,…,xjk,…,xjn), wherein xikAnd xjkIn respectively two kinds of defect characteristic value sequences Component, calculate the mean value of two kinds of defect characteristic value respective components, be denoted as:WithK=1 ..., n, Two mean bias percentages are further calculated, as follows:
Compare the respective components mean bias percentage and preset two threshold value δ1And δ2Size relation.If should Respective components mean bias percentage is less than threshold value δ1Eigenvalue components number account for all characteristic value dimensions percentage it is big In threshold value δ2, then corresponding two kinds of defects are merged.
Preferably, the present invention is by threshold value δ1It is taken as 70%, threshold value δ2It is taken as 90%.
S203, if mutual mistake point rate is greater than before not merging in the confusion matrix of two class defect classification results Threshold value δ3, then also two class defects are merged.
Preferably, the present invention is by threshold value δ3It is taken as 20%.
S203, if mutual mistake point rate is greater than before not merging in the confusion matrix of two class defect classification results Threshold value δ3, then also two class defects are merged.
Preferably, the present invention is by threshold value δ3It is taken as 20%.
S204, the defect after merging are placed on middle layer level.
S205, other unassorted defects are also placed in middle layer level, i.e., other unassorted defects with merge after Defect be placed on same level, and among the defect classifier proposed by the present invention based on serial/parallel row integrated study frame The multiple parallel machine learning classification methods of layer realize classification.
Embodiment two
The plan of embodiment two classifies to five kinds of different types of strip surface quality typical defects, this five defective class Type title is respectively weld seam, hole, scuffing, roll marks and oil mark, and defect image pattern is respectively as shown in Fig. 3 a~Fig. 3 e.
The steel strip surface defect classification method based on serial/parallel row integrated study frame is proposed to above-mentioned five kinds using the present invention When defect is classified, first the 1st kind of defect --- weld seam is separated from all defect classification, and foundation is real in building When applying rule list involved by example two, " the defect image width " and " defect image height/width ratio " two of weld defect are found A feature is Chong Die with the individual features zero of other all defect classifications, it is obvious that the building side of mentioned rule list according to the present invention Method, the two features should all be added in the rule list of first layer, and the rule list of first layer is as shown in table 1 below:
The rule list of 1 first layer of table
Serial number Rule description
1 Defect image width is equal with strip breadth
2 Defect image height/width ratio is less than 1/100
The defect merging mentioned further according to the present invention and stage division, should be by " defect image width " and " defect image Defect of the corresponding defect classification of height/width ratio " as upper layer level, and the two features are directed to weld defect.This Sample, all weld defects can simply be determined by the rule list of first layer.
Then, it is sent out by merging to the defect not mentioned using the present invention with defect classification results analysis before stage division It is existing, it scratches and the mutual mistake point rate of two kinds of defects of roll marks is above 20%, for expansion, i.e., scuffing defect is divided into roll marks by mistake and lacks Sunken percentage is 26.9582%, and roll marks defect is 29.3761% by the percentage that mistake is divided into scuffing defect, according to the present invention The defect mentioned merges and stage division, is higher than threshold value δ3, so will scratch and two kinds of defects of roll marks merge, rename for " strip defect ", as the defect of intermediate level, other holes not merged and oil mark are also placed in this level, in this way, in Between level defect include three kinds, be respectively as follows:
Strip defect
Hole
Oil mark
Mentioned method according to the present invention, using middle layer in the defect classifier based on serial/parallel row integrated study frame Machine learning classifiers classify to three kinds of defects of above-mentioned intermediate level.Middle layer Machine learning classifiers contain at random Forest classified device, self organizing neural network and the GentlBoost classifier based on ECOC coding, by three kinds of parallel classifiers It weights and determines classification results.
So far, the five defective in embodiment two has distinguished four kinds, is determined by upper layer rule list respectively Weld seam, and the strip defect, hole and the oil mark that are determined by middle layer Machine learning classifiers, it is to be noted, however, that just In beginning defect classification and strip defect is not included, this is the intersection scratched with two kinds of defects of roll marks, so also needing according to third Layer rule list and classifier do further classification processing.
In embodiment two third layer rule list be sky, classifier using support vector machines realization, with support vector machines to by The strip defect sample set that middle layer Machine learning classifiers determine is trained and classifies, and obtains final classification results, Scuffing and two kinds of defects of roll marks are distinguished i.e. in strip defect.
Comprehensive final classification accuracy, as shown in table 2.
2 defect classification accuracy of table
Defect classification Weld seam Hole It scratches Roll marks Oil mark
Accuracy rate 100.00% 98.61% 96.27% 98.09% 99.86%
In conclusion a kind of strip surface quality defect based on serial/parallel row integrated study frame disclosed by the invention point Class method is aided with by sufficiently merging newest machine learning classification identification technology, defect mechanism of production and artificial experience rule The defect adaptable with classifier structure merges and stage division, further improves strip surface quality defect Classification and Identification Accuracy, and improve self-learning capability, real-time performance and the generalization ability of classifier.
Those of ordinary skill in the art be further appreciated that implement the method for the above embodiments be can It is completed with instructing relevant hardware by program, the program can store in computer-readable storage medium, Described storage medium, including ROM/RAM, disk, CD etc..
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.

Claims (4)

1. a kind of strip surface quality defect classifier based on serial/parallel row integrated study frame, which is characterized in that classifier is The three layers of serial structure divided from top to bottom, comprising rule list and Machine learning classifiers, specifically:
Upper layer is rule list,
Middle layer is multiple parallel Machine learning classifiers,
Lower layer is rule list or single Machine learning classifiers；
Rule list is presented as a kind of bivariate table in structure, specific comprising two column, is serial number and to regular particular content respectively Description；The line number of rule list is determined by regular quantity.
2. a kind of strip surface quality defect classifier based on serial/parallel row integrated study frame according to claim 1, It is characterized in that, rule list is the process mechanism that binding deficient generates, and use for reference and absorb the experience and knowledge of live expert, warp The classification decision rule formed after conversion；In selecting when multiple collateral learning classifiers of interbed, focus on they mutually it Between complementarity, the construction step of rule list includes:
S201 finds a value range feature Chong Die with other all categories zero, will if having found such feature This feature is added in the rule list of upper layer, is the defect of upper layer level by the defect classification that this feature is distinguished；
S202 finds a value range and other Chong Die features of particular category (not all classification) zero, if having found this Then this feature is added in lower layer's rule list for the feature of sample, is the defect of lower layer's level by the defect classification that this feature is distinguished；
S203 executes traversal than choosing to all features, until terminating.
3. a kind of strip surface quality defect classifier based on serial/parallel row integrated study frame according to claim 1, It is characterized in that, middle layer assembly random forest grader, self organizing neural network and the GentlBoost based on ECOC coding Three kinds of classifiers of classifier；The present invention proposes the defect classifier based on serial/parallel row integrated study frame, exactly in order to sufficiently melt Existing machine learning classification identification technology, defect mechanism of production and artificial experience rule are closed, Classification and Identification is further increased The self-learning capability of accuracy and classifier, real-time performance and generalization ability.
4. a kind of classification method of the defect classifier based on serial/parallel row integrated study frame, which is characterized in that closed including defect And and stage division, the specific steps are as follows:
S1 is isolated as the defect for being included in upper layer rule list the defect of upper layer level；
S2 compares the characteristic value of two kinds of defect sample in training set, if two kinds of defect classification sequence numbers are respectively yiAnd yj, training set In corresponding sample size be respectively ciAnd cj, characteristic value dimension is n dimension, and defect characteristic value sequence is respectively (xi1,xi2,…, xik,…,xin) and (xj1,xj2,…,xjk,…,xjn), wherein xikAnd xjkComponent in respectively two kinds of defect characteristic value sequences, The mean value for calculating two kinds of defect characteristic value respective components, is denoted as:WithK=1 ..., n are further counted Two mean bias percentages are calculated, as follows:
Compare the respective components mean bias percentage and preset two threshold value δ1And δ2Size relation；If the correspondence Component mean bias percentage is less than threshold value δ1Eigenvalue components number account for all characteristic value dimensions percentage be greater than threshold Value δ2, then corresponding two kinds of defects are merged；
S3, if before not merging in the confusion matrix of two class defect classification results, mutual mistake divides rate to be greater than threshold value δ3, Then also two class defects are merged；
S4, the defect after merging are placed on middle layer level；
S5, other unassorted defects are also placed in middle layer level, i.e., other unassorted defects with merge after defect It is placed on same level, and multiple with the defect classifier middle layer proposed by the present invention based on serial/parallel row integrated study frame Parallel machine learning classification method realizes classification.
CN201910351038.6A 2019-04-28 2019-04-28 Strip steel surface quality defect classifier based on serial/parallel integrated learning framework and classification method thereof Active CN110135477B (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160328837A1 (en) * 2015-05-08 2016-11-10 Kla-Tencor Corporation Method and System for Defect Classification
CN108846831A (en) * 2018-05-28 2018-11-20 中冶南方工程技术有限公司 The steel strip surface defect classification method combined based on statistical nature and characteristics of image
CN109636772A (en) * 2018-10-25 2019-04-16 同济大学 The defect inspection method on the irregular shape intermetallic composite coating surface based on deep learning

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160328837A1 (en) * 2015-05-08 2016-11-10 Kla-Tencor Corporation Method and System for Defect Classification
CN108846831A (en) * 2018-05-28 2018-11-20 中冶南方工程技术有限公司 The steel strip surface defect classification method combined based on statistical nature and characteristics of image
CN109636772A (en) * 2018-10-25 2019-04-16 同济大学 The defect inspection method on the irregular shape intermetallic composite coating surface based on deep learning

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