CN206897873U - A kind of image procossing and detecting system based on detection product performance - Google Patents
A kind of image procossing and detecting system based on detection product performance Download PDFInfo
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- CN206897873U CN206897873U CN201720382285.9U CN201720382285U CN206897873U CN 206897873 U CN206897873 U CN 206897873U CN 201720382285 U CN201720382285 U CN 201720382285U CN 206897873 U CN206897873 U CN 206897873U
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- 238000001514 detection method Methods 0.000 title claims abstract description 52
- 238000007781 pre-processing Methods 0.000 claims abstract description 29
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- 230000007935 neutral effect Effects 0.000 claims abstract description 19
- 230000004044 response Effects 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 abstract description 11
- 238000003709 image segmentation Methods 0.000 abstract description 6
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Abstract
The utility model discloses a kind of image procossing and detecting system based on detection product performance, including image pre-processing module, image segmentation module, feature extraction module, data preprocessing module, neural network learning module and the neutral net detection module being sequentially connected；Image procossing and detecting system provided by the utility model based on detection product performance, can improve product quality detection efficiency.
Description
Technical field
Industrial Robot Technology field is the utility model is related to, it is more particularly to a kind of based on detection product performance
Image procossing and detecting system.
Background technology
In modern industry, the production of many products (such as electronics and device, hardware device parts) is complete on transfer matic
Into, each production link is directed to the quality testing of product, and some enterprises put into a large amount of manpowers, by the way of naked eyes detect come
Product quality is controlled, but because some human factors, product quality are difficult to ensure that.To improve detection efficiency and ensureing product matter
Amount, the automatic detection that the vision progress product quality of people is replaced using machine vision technique are the effective ways solved the problems, such as.
Machine vision due to can quick obtaining bulk information, and be easy to same design information and control information and integrated, because
In modern manufacturing production process, machine vision is widely used in quality testing, production control etc. for this.Regarded relative to human eye
Feel, machine vision has very big advantage and development prospect, therefore machine vision is developed rapidly in recent years, its extensive use
In every field such as medical treatment, industry, agricultural, military affairs, traffic.
In China, until middle and later periods nineties machine vision technique is just gradually recognized and understood, at present, used in system
Make machine vision in industry and still suffer from the problem of product quality detection efficiency is low, therefore, it is difficult to promote.
Utility model content
In view of the deficienciess of the prior art, the purpose of this utility model is to provide the image based on detection product performance
Processing and detecting system, can improve product quality detection efficiency.
To achieve the above object, the utility model provides following technical scheme：
A kind of image procossing and detecting system based on detection product performance, including the image preprocessing mould being sequentially connected
Block, feature extraction module, data preprocessing module and neutral net detection module；
Described image pretreatment module is used to correct the original image when the gray level of original image is more than preset value
Gray level, and send corresponding revise signal to the feature extraction module；
The feature extraction module is used to receive the revise signal, and is more than in the gray scale of the revised image
Corresponding characteristic vector signal is sent during preset value to the data preprocessing module；
The data preprocessing module is used to receive the characteristic vector signal, by the characteristic vector signal and preset value
It is compared, and sends corresponding preprocessed signal to the neutral net detection module；
The neutral net detection module is used to classify to product in response to the preprocessed signal.
As a kind of embodiment, described image pretreatment module includes comparing unit, and the comparing unit is used for
The gray level of original image corrects the gray level of the original image when being more than preset value, and sends corresponding revise signal extremely
The feature extraction module.
As a kind of embodiment, the feature extraction module includes comparing unit, and the comparing unit is used to receive
The revise signal, and send corresponding characteristic vector signal extremely when the gray scale of the revised image is more than preset value
The data preprocessing module.
As a kind of embodiment, the data preprocessing module includes comparing unit, and the comparing unit is used to connect
The characteristic vector signal is received, the characteristic vector signal and preset value are compared, and sends corresponding pretreatment letter
Number to the neutral net detection module.
As a kind of embodiment, the neutral net detection module includes comparing unit, and the comparing unit is used for
The preprocessed signal and preset value are compared, and product classified according to result of the comparison.
The utility model is compared to the beneficial effect of prior art：
The utility model provides a kind of image procossing and detecting system based on detection product performance, including image is located in advance
Manage module, feature extraction module, data preprocessing module and neutral net detection module；This four modules are respectively by one
Key element and preset value are compared, for example, the gray level of original image and preset value are compared by image pre-processing module；Most
Product is classified eventually, so as to improve product quality detection efficiency.
Brief description of the drawings
Fig. 1 is the image procossing provided by the utility model based on detection product performance and a block diagram of detecting system；
Fig. 2 is the image procossing provided by the utility model based on detection product performance and another block diagram of detecting system.
In figure：100th, image pre-processing module；200th, image segmentation module；300th, feature extraction module；400th, data are pre-
Processing module；500th, neural network learning module；600th, neutral net detection module.
Embodiment
Below in conjunction with accompanying drawing, and other technical characteristic above-mentioned to the utility model and advantage are carried out clearly and completely
Description, it is clear that described embodiment is only section Example of the present utility model, rather than whole embodiments.
Reference picture 2, the utility model provide a kind of image procossing and detecting system based on detection product performance, including
Image pre-processing module 100, feature extraction module 300, data preprocessing module 400 and the neutral net inspection being sequentially connected
Survey module 600；Image pre-processing module 100 is used for the ash that original image is corrected when the gray level of original image is more than preset value
Level is spent, and sends corresponding revise signal to feature extraction module 300；Feature extraction module 300 is used to receive revise signal,
And corresponding characteristic vector signal is sent when the gray scale of revised image is more than preset value to data preprocessing module
400；Data preprocessing module 400 is used to receive characteristic vector signal, and characteristic vector signal and preset value are compared, and
Corresponding preprocessed signal is sent to neutral net detection module 600；Neutral net detection module 600 is used in response to pretreatment
Signal is classified to product.
Here, image pre-processing module 100, feature extraction module 300, data preprocessing module 400 and neutral net
Detection module 600 has a key element compared with preset value respectively, is the gray level of original image, revised image respectively
Gray scale, characteristic vector signal and preprocessed signal.Wherein, any link in this four processes, be all by one it is default will
Then element exports a result of the comparison with preset value compared with, principle therein is similar to voltage comparator, by voltage and
Preset value is compared and exports a result of the comparison, belongs to prior art.
Reference picture 1, based on this hardware structure, it is possible to achieve following function：
Pretreatment module, which uses, to be handled the gray level amendment under environmental light intensity difference and noise smoothing, improves image
Grey-scale contrast, realize the matching of detection image and template image；Image segmentation module 200 employs Threshold sementation, leads to
Cross and area-of-interest AOI progress threshold segmentations are defined to system, make quality testing region more targeted；Feature
Abstraction module 300 defines respective algorithms by product quality defect species and extracts image feature vector, improves product quality detection
Efficiency.
It is significant to note that the use of S function is f (x)=1/ (1+e-x) normalize to characteristic vector between 0 to 1
It is prior art.
Next, to image pre-processing module 100, image segmentation module 200, feature extraction module 300, data prediction
Module 400, neural network learning module 500 and neutral net detection module 600 illustrate one by one.
Image procossing and detecting system provided by the utility model based on detection product performance, the image preprocessing of use
Gray level amendment, the method for smooth noise pre-process to image.This process is realized by image pre-processing module 100.
Image procossing and detecting system provided by the utility model based on detection product performance, using thresholding method pair
Image is split, then will production first according to the appropriate gray level thresholding (threshold value) of the Feature Selection one of detected product
Each pixel grey scale in product image is compared with it, more than redistributing with maximum gray scale (255), less than thresholding for thresholding
Distribution with minimal gray (0), can thus form a new bianry image, and successfully object is manifested from background
Come.This process is realized by image segmentation module 200.
Image procossing and detecting system provided by the utility model based on detection product performance, on the basis of image segmentation
On gradation of image information is measured, produce one group of feature, these combinations of features together, be formed characteristic vector,
The information content that the binary map and artwork of examined product image include is generally very big, it is impossible to directly provides it to BP neural network
Detection judges, it is therefore necessary to some features is extracted from binary map and artwork, system definition will be likely to occur comprising quality problems
The rectangular area of scope be referred to as area-of-interest AOI (area of interest, AOI), extract background light level value, AOI
The size of hot spot, hot spot forms BP neural network from 4 features with a distance from bias light in the maximum gradation value and AOI in AOI
Input feature value.This process is realized by feature extraction module 300.
Image procossing and detecting system provided by the utility model based on detection product performance, characteristic extracting module obtain
Data use S function f (x)=1/ (1+e-x) be normalized between 0 to 1, to input Processing with Neural Network.This mistake
Journey is realized by data preprocessing module 400.
Image procossing and detecting system provided by the utility model based on detection product performance, product quality defect species
More, true defect and Artifact are not easy to distinguish.Therefore Feature Selection need to be depending on specific requirement.The quality inspection such as in glass production
Glass blocks (product) is usually divided into two stages and checked according to the species and product needs of defect by personnel.First rank
The defects of section is the detection to single glass defect, and its target is discovery glass, including：Bubble, it is mingled with, light distortion, viscous tin, draws
Wound, drawing lines.Second stage is that monolithic glass is classified on the basis of detecting in the first stage.Therefore the detection of individual defect is
The basis of classification classification.Quality Inspector extremely paid close attention to the size of individual defect, is generally represented with major diameter.It is for bubble
Maximum axial distance, it is the ultimate range of point-to-point transmission in heart defect curl for irregular be mingled with, and to linear
Defect then refers to line length.Major diameter must be calculated during detection.The neural network learning of software first has to determine BP nerve nets
The optimum structure of network.Wherein, input layer number depends on the dimension of input feature value, and input feature value is in the system
4 dimensions, institute's input layer number are 4.Output layer nodes can typically be equal to pattern class number, it is also possible to the coding of output node
Represent each pattern class.Due to typically only two kinds of the quality problems type of product：It is qualified with it is unqualified.It is accordingly, it can be determined that defeated
Go out layer unit number for 1, it, which is exported, represents that product is qualified when being 0, export for 1 when represent that product is unqualified.Hidden layer node number
Requirement, input-output unit typically with problem number and number of training have direct relation, the system is using implicit
Node layer number evaluation method is：
Wherein, HNFor optimal node in hidden layer；NIFor input layer number；NOFor output layer nodes；NPTo train sample
This number.
The system uses additional guide vanes, and in the change of each weights and threshold value plus the next item up is proportional to previous change
The value of amount, and new weights and threshold value are produced according to back propagation, BP neural network can be avoided to be fallen into learning process
In local minimum, accelerate pace of learning.
Wherein, k is frequency of training；Mc is factor of momentum, typically takes 0.95 or so.
In the case where given accuracy requires MSE≤104, BP neural network restrains to 28 samples of selection by 503 study
Afterwards, obtained weights and threshold value write-in file are saved, this is the learning outcome of network.
System is by being handled the image of detected product to obtain characteristic vector, and this vector is as input network
New model.This process is realized by neural network learning module 500.
Image procossing and detecting system provided by the utility model based on detection product performance, neutral net detection are exactly
The new model for inputting network is identified and classified by calling the learning outcome of BP neural network, i.e., it is special to product image
Sign is detected, and exports testing result, is finally handled accordingly by execution machine to being detected vial.
Particular embodiments described above, the purpose of this utility model, technical scheme and beneficial effect are carried out to enter one
The detailed description of step, it will be appreciated that the foregoing is only specific embodiment of the utility model, be not used to limit this reality
With new protection domain.Particularly point out, to those skilled in the art, it is all the spirit and principles of the utility model it
It is interior, any modification, equivalent substitution and improvements done etc., it should be included within the scope of protection of the utility model.
Claims (5)
1. a kind of image procossing and detecting system based on detection product performance, it is characterised in that including the image being sequentially connected
Pretreatment module (100), feature extraction module (300), data preprocessing module (400) and neutral net detection module
(600)；
Described image pretreatment module (100) is used to correct the original image when the gray level of original image is more than preset value
Gray level, and send corresponding revise signal to the feature extraction module (300)；
The feature extraction module (300) is used to receive the revise signal, and big in the gray scale of the revised image
Corresponding characteristic vector signal is sent when preset value to the data preprocessing module (400)；
The data preprocessing module (400) is used to receive the characteristic vector signal, by the characteristic vector signal and presets
Value is compared, and sends corresponding preprocessed signal to the neutral net detection module (600)；
The neutral net detection module (600) is used to classify to product in response to the preprocessed signal.
2. image procossing and detecting system according to claim 1 based on detection product performance, it is characterised in that described
Image pre-processing module (100) includes comparing unit, and the comparing unit is used to be more than preset value in the gray level of original image
The gray level of original image described in Shi Xiuzheng, and corresponding revise signal is sent to the feature extraction module (300).
3. image procossing and detecting system according to claim 1 based on detection product performance, it is characterised in that described
Feature extraction module (300) includes comparing unit, and the comparing unit is used to receive the revise signal, and in the amendment
The gray scale of image afterwards sends corresponding characteristic vector signal to the data preprocessing module (400) when being more than preset value.
4. image procossing and detecting system according to claim 1 based on detection product performance, it is characterised in that described
Data preprocessing module (400) includes comparing unit, and the comparing unit is used to receive the characteristic vector signal, by the spy
Sign vector signal and preset value are compared, and send corresponding preprocessed signal to the neutral net detection module
(600)。
5. image procossing and detecting system according to claim 1 based on detection product performance, it is characterised in that described
Neutral net detection module (600) includes comparing unit, and the comparing unit is used to enter the preprocessed signal and preset value
Row compares, and product is classified according to result of the comparison.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108210186A (en) * | 2018-01-29 | 2018-06-29 | 靳霞 | Puerpera's assistant recovery device |
| CN108459030A (en) * | 2018-02-08 | 2018-08-28 | 东华大学 | One kind being applied to non-planar plastic smooth surface flaw on-line measuring device and method |
| CN112547528A (en) * | 2021-03-01 | 2021-03-26 | 华鹏飞股份有限公司 | Logistics sorting method and system based on classification identification |
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2017
- 2017-04-12 CN CN201720382285.9U patent/CN206897873U/en not_active Expired - Fee Related
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108210186A (en) * | 2018-01-29 | 2018-06-29 | 靳霞 | Puerpera's assistant recovery device |
| CN108210186B (en) * | 2018-01-29 | 2020-02-25 | 张燕 | Auxiliary recovery device for lying-in woman |
| CN108459030A (en) * | 2018-02-08 | 2018-08-28 | 东华大学 | One kind being applied to non-planar plastic smooth surface flaw on-line measuring device and method |
| CN112547528A (en) * | 2021-03-01 | 2021-03-26 | 华鹏飞股份有限公司 | Logistics sorting method and system based on classification identification |
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