CN111709920A - Template defect detection method - Google Patents
Template defect detection method Download PDFInfo
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- CN111709920A CN111709920A CN202010483881.2A CN202010483881A CN111709920A CN 111709920 A CN111709920 A CN 111709920A CN 202010483881 A CN202010483881 A CN 202010483881A CN 111709920 A CN111709920 A CN 111709920A
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- 230000007547 defect Effects 0.000 title claims abstract description 55
- 238000001514 detection method Methods 0.000 title claims abstract description 25
- 238000012549 training Methods 0.000 claims abstract description 13
- 230000004927 fusion Effects 0.000 claims abstract description 5
- 238000000605 extraction Methods 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 abstract description 11
- 238000002360 preparation method Methods 0.000 abstract description 3
- 230000002950 deficient Effects 0.000 description 5
- 238000013135 deep learning Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013480 data collection Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000011282 treatment Methods 0.000 description 1
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Abstract
A template defect detection method comprises the following steps: acquiring product defect data by fusing small defects and good products acquired from a defect library, and generating corresponding marking data; the first half of the network is a twin network based on a residual error network, and the second half is a feature fusion and extraction network; inputting good products and corresponding defect sample generation and outputting as a label by a network, and training a model; obtaining a comparison network through training and learning, wherein the comparison network has the capability of comparing the difference between two samples, and the output of the network is the data difference degree of the two input samples; and the defect detection is realized by comparing the difference degree between two products output by the network. According to the invention, defect detection can be realized by comparing the difference degree between two products output by the network, so that the time cost of artificial marking is saved, the early-stage preparation work of model training is reduced, the fast switching detection of the products in the work production is realized, and the production efficiency of the products is improved.
Description
Technical Field
The invention relates to the field of defect detection, in particular to a template defect detection method.
Background
For template defect detection, a traditional algorithm and a deep learning algorithm exist at present. The traditional algorithm has the defects that the development period of the algorithm is long, the generalization capability is poor, the development difficulty of the traditional algorithm is high for some complex samples, meanwhile, the current algorithm is not suitable any more due to slight changes of the shape, the size or the color and the like of a product, a professional algorithm person is required to develop a new algorithm again, and the long maintenance period and the high cost of the traditional algorithm are caused.
The basic operation flow of the current deep learning algorithm applied to template defect detection is as follows, firstly, a large amount of defective product data and non-defective product data need to be collected, then corresponding data label marking is carried out manually, and finally, the training model is applied to an industrial production line.
Meanwhile, for some products, the yield is extremely high, the defective rate is extremely low, collection of a large amount of defect data is unrealistic, otherwise serious errors of sample data unbalance can be caused, and time and labor input of data collection and marking are restricted, so that a part of industrial manufacturers apply deep learning algorithms.
Disclosure of Invention
The invention provides a template defect detection method, which aims to solve at least one technical problem.
To solve the above problem, as an aspect of the present invention, there is provided a template defect detecting method including:
step 1, collecting template good product data: acquiring product defect data by fusing small defects and good products acquired from a defect library, and generating corresponding marking data;
step 2, constructing a double-input network model: the first half of the network is a twin network based on a residual error network, and the second half is a feature fusion and extraction network;
step 3, putting training data into the network: inputting good products and corresponding defect sample generation and outputting as a label by a network, and training a model;
step 4, obtaining a comparison network through training and learning, wherein the comparison network has the capability of comparing the difference between two samples, and the output of the network is the data difference degree of the two input samples;
and 5, outputting the difference degree between the two products through a comparison network to further realize defect detection.
Preferably, the defect detection includes the presence or absence of a defect and the position, size, etc. of the defect.
Preferably, the comparison network outputs a thermodynamic diagram, wherein a larger value in the thermodynamic diagram represents a larger difference between the two samples.
Preferably, whether the product has defects, the corresponding defect size and the like are calculated through the process of setting a threshold value and the like.
By adopting the technical scheme, the defect detection can be realized by comparing the difference degree between two products output by the network, so that the time cost of artificial marking is saved for the production detection of the template product, the early preparation work of model training is reduced, the fast switching detection of the product in the working production is realized, and the production efficiency of the product is improved.
Drawings
Fig. 1 schematically shows a flow chart of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
When the deep learning algorithm in the prior art is applied to template defect detection, defect data needs to be collected and sorted and data labels need to be marked, so that high labor and time costs need to be invested. In order to overcome the problem, the invention constructs a double-input depth model comparison network according to the characteristics of the product template, and the scheme does not need to collect defect data samples and label the samples, thereby greatly reducing the production cost.
For the production of template products, the consistency between two good products is extremely high, and the good products and the defective products have certain difference in some places, and the inconsistency is the defect of the defective products. Therefore, the invention firstly collects good products, utilizes the traditional image method to manufacture defect samples and generate corresponding labels, then constructs a double-input convolution network, the first half part of the network is a twin network for extracting special certificates, the second half part of the network is a network characteristic fusion and outputs results, the output result is a thermodynamic diagram, the larger the numerical value of the thermodynamic diagram is, the larger the difference of the two samples at the position is, and finally, the traditional method is used for setting threshold values and other treatments to calculate whether the products have defects, the corresponding defect sizes and the like.
The specific operation method and steps of the invention are as follows:
step 1: collecting template good product data, fusing small defects and good products obtained from a defect library by a traditional method to obtain product defect data, and generating corresponding labeling data.
Step 2: and constructing a double-input network model, wherein the first half part of the network is a twin network based on a residual error network, and the second half part of the network is a feature fusion and extraction network.
And step 3: and putting the training data into the network, inputting the network into good products and corresponding generated defect samples, and outputting the network into a label to train the model.
And 4, step 4: the learned network model has the capability of comparing the difference between the two samples, so the network model is called a comparison network, the output of the network is the data difference degree of the two input samples, and whether the product has defects, the sizes of the positions of the defects and the like are obtained through a certain threshold value and a certain traditional algorithm.
By adopting the technical scheme, the defect detection can be realized by comparing the difference degree between two products output by the network, so that the time cost of artificial marking is saved for the production detection of the template product, the early preparation work of model training is reduced, the fast switching detection of the product in the working production is realized, and the production efficiency of the product is improved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A template defect detection method is characterized by comprising the following steps:
step 1, collecting template good product data: acquiring product defect data by fusing small defects and good products acquired from a defect library, and generating corresponding marking data;
step 2, constructing a double-input network model: the first half of the network is a twin network based on a residual error network, and the second half is a feature fusion and extraction network;
step 3, putting training data into the network: inputting good products and corresponding defect sample generation and outputting as a label by a network, and training a model;
step 4, obtaining a comparison network through training and learning, wherein the comparison network has the capability of comparing the difference between two samples, and the output of the network is the data difference degree of the two input samples;
and 5, outputting the difference degree between the two products through a comparison network to further realize defect detection.
2. The template defect method of claim 1, wherein the defect detection comprises the presence and location, size, etc. of the defect.
3. The template defect detection method of claim 2, wherein the comparison network outputs a thermodynamic diagram, wherein a larger value in the thermodynamic diagram indicates a larger difference between the two samples.
4. The template defect detection method according to claim 3, wherein whether a product has defects, corresponding defect sizes and the like are calculated by setting a threshold and the like.
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| CN202010483881.2A CN111709920A (en) | 2020-06-01 | 2020-06-01 | Template defect detection method |
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| CN202010483881.2A CN111709920A (en) | 2020-06-01 | 2020-06-01 | Template defect detection method |
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Cited By (2)
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|---|---|---|---|---|
| CN112907543A (en) * | 2021-02-24 | 2021-06-04 | 胡志雄 | Product appearance defect detection method based on random defect model |
| CN113160200A (en) * | 2021-04-30 | 2021-07-23 | 聚时科技(上海)有限公司 | Industrial image defect detection method and system based on multitask twin network |
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| CN112907543A (en) * | 2021-02-24 | 2021-06-04 | 胡志雄 | Product appearance defect detection method based on random defect model |
| CN112907543B (en) * | 2021-02-24 | 2024-03-26 | 胡志雄 | Product appearance defect detection method based on random defect model |
| CN113160200A (en) * | 2021-04-30 | 2021-07-23 | 聚时科技(上海)有限公司 | Industrial image defect detection method and system based on multitask twin network |
| CN113160200B (en) * | 2021-04-30 | 2024-04-12 | 聚时科技(上海)有限公司 | Industrial image defect detection method and system based on multi-task twin network |
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Application publication date: 20200925 |