CN109596620A - Product surface shape defect detection method and system based on machine vision - Google Patents
Product surface shape defect detection method and system based on machine vision Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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Abstract
The present invention proposes a kind of product surface shape defect detection method based on machine vision, comprising the following steps: the sample image and target image of product to be detected are obtained by Image Acquisition；The sample image and target image are pre-processed, obtain the binary map of sample image and target image respectively；The binary map of binary map and the target image to the sample image carries out difference operation, obtains poor shadow figure and defective value, judges whether the target image is defective according to the defective value.The quality of the contactless automatic testing product picture on surface of the present invention, is not required to fetch faulty goods and compares one by one, greatly reduces the workload of staff.And this method is easy to extend, and if desired also checks color, and color contrast algorithm need to only be added in the image comparison and analysis in image processing module.
Description
Technical field
The present invention relates to product defects detection field, in particular to a kind of product surface shape defect based on machine vision
Detection method and system field of image processing.
Background technique
Industrial products surface can be printed on many decorative pattern, pattern and some necessary care labels, when these patterns, mark
It is signed with defect, that whether can all generate adverse effect to the aesthetics of product, comfort or practicability.Traditional detection
Method is typically all artificial detection, it has, and low efficiency, at high cost, accuracy rate is general, real-time is low, and general artificial detection can also
Sampling observation method is taken, inevitably has fish that has escape the net in this way, reduces accuracy rate, and the method based on machine vision will overcome significantly
These drawbacks.The detection in face of well-regulated surface profile is substantially about the method for this respect so far, such as wall
The detection of the surface defects of products such as paper, steel, solar battery, and it is then seldom to the detection on random pattern surface.
Chinese invention patent 200910232677.7 discloses a kind of copper strip surface quality that view-based access control model is bionical intelligently inspection
Survey device and method.But since the probability of surface defects of products appearance is less than 5%, current Computer Vision Detection method pair
The operations such as each frame image obtained is all split, surface defects characteristic extracts, spend a large amount of time in normal picture
In processing, the real-time of system is reduced, the deficiencies such as that there are detection efficiencies is not high, Classifcation of flaws accuracy is low.
Summary of the invention
Based on the above issues, the purpose of the present invention aims to solve at least one of described technological deficiency.The present invention proposes one kind
Product surface shape defect detection method based on machine vision, method includes the following steps:
The sample image and target image of product to be detected are obtained by Image Acquisition；
The sample image and target image are pre-processed, obtain the two-value of sample image and target image respectively
Figure；
The binary map of binary map and the target image to the sample image carries out difference operation, obtains poor shadow figure and lacks
Value is fallen into, judges whether the target image is defective according to the defective value.
Preferably, described that the sample image and target image are pre-processed, sample image and target are obtained respectively
The step of binary map of image includes:
Gray proces are carried out to the sample image and target image, treated sample image and target image are passed through
Median filtering is denoised；
Whether the image after judging denoising is target image；
If so, finding out sample image respectively to sample image and target image progress hough change detection after denoising
With the boundary of target image, boundary set is obtained；
The boundary of the boundary set of target image and sample image is subjected to matching treatment, image carries out to treated
The edge detection of canny operator respectively obtains the binary map of sample image and target image.
Preferably, the described the step of boundary of the boundary set of target image and sample image is carried out matching treatment, includes:
The boundary of the boundary set of target image and sample image is compared, then target image is rotated and contracted
Operation is put, the boundary on the boundary and sample image that make target image coincide.
Preferably, described the step of carrying out gray proces to the sample image and target image, includes:
Gray proces are carried out to the sample image and target image by weighted mean method.
Preferably, described to judge that the whether defective step of the target image includes: according to the defective value
Target image is defective if defective value is greater than threshold value, and otherwise, target image does not have defect.
The embodiment of the present invention also provides a kind of product surface shape defect detection system based on machine vision, the system
Include:
Image capture module obtains the sample image and target image of product to be detected；
Image processing module pre-processes the sample image and target image, obtains sample image and mesh respectively
The binary map of logo image；
The binary map of image analysis module, binary map and the target image to the sample image carries out difference operation,
Poor shadow figure and defective value are obtained, judges whether the target image is defective according to the defective value.
It preferably, further include memory module, the memory module connects described image analysis module, for storing image point
Analyse the analysis result and difference shadow figure of module.
Preferably, image capture module shoots product to be detected by industrial camera, obtains the sample graph of product to be detected
Picture and target image.
Scheme in compared with the existing technology, advantages of the present invention:
The product surface shape defect detection method based on machine vision that the embodiment of the present invention proposes, using Image Acquisition
Module capturing sample image and target image carry out edge by hough variation and canny algorithm in image processing module
Detection obtains the binary map of sample image and target image, obtains defective value, contactless automatic testing product table by calculating
The quality of face pattern, is not required to fetch faulty goods and compares one by one, greatly reduces the workload of staff.And this method is easy to expand
Exhibition, if desired also checks color, only color contrast need to be added in the image comparison and analysis in image processing module
Algorithm.
Detailed description of the invention
The invention will be further described with reference to the accompanying drawings and embodiments:
Fig. 1 is a kind of flow diagram of the product surface shape defect detection method based on machine vision of the present invention.
Fig. 2 is the specific flow chart of step S2 in Fig. 1 of the present invention.
Fig. 3 is a kind of structural schematic diagram of the product surface shape defect detection system based on machine vision of the present invention.
Specific embodiment
Above scheme is described further below in conjunction with specific embodiment.It should be understood that these embodiments are for illustrating
The present invention and be not limited to limit the scope of the invention.Implementation condition used in the examples can be done such as the condition of specific producer into
One successive step, the implementation condition being not specified are usually the condition in routine experiment.
The product surface shape defect detection method based on machine vision that this application discloses a kind of, please refers to shown in Fig. 1
For a kind of flow diagram of the product surface shape defect detection method based on machine vision of the present invention, comprising the following steps:
Step S1: the sample image and target image of product to be detected are obtained by Image Acquisition.It is of the invention wherein
In one embodiment, product is transported to by conveyer belt and formulates position, CMOS industrial camera is used to shoot the image of product, together
When, it is used using LED come light filling.Specifically, product should be transported to Position Approximate by conveyer belt every time, while conveyer belt and camera
Relative position is also fixed as far as possible, error brought by the rotation, scaling when can reduce picture match in this way.
Step S2: pre-processing sample image and target image, obtains the two of sample image and target image respectively
Value figure.
Gray proces are carried out to the collected sample image of step S1 and target image first, then by median filtering into
Row denoising, makes it be suitable for subsequent processing.
Hough variation is first carried out to the image after denoising and canny algorithm carries out edge detection, finds out production to be detected
Then the edge of product rule is rotated, is scaled, cut and match after sequence of operations target image and sample image, finally
Two figures are subjected to difference operation and opening operation, obtain poor shadow figure and defective value, when defective value be more than threshold value be then it is defective, otherwise then
Zero defect.
In a wherein embodiment of the invention, please refer to shown in Fig. 2, step S2 is specifically further comprising the steps of:
Step S21: carrying out gray proces to sample image and target image, to treated sample image and target image
It is denoised by median filtering.Median filtering method uses a kind of common Nonlinear Smoothing Filter, the basic principle is that handle
The intermediate value of each point value its major function that replaces is to allow in one field of the value of any in the digital picture or Serial No. point
The pixel that the difference of surrounding pixel gray value is bigger changes to take the value close with the pixel value of surrounding, so as to eliminate isolated make an uproar
Sound point.
Step S22: whether the image after judging denoising is target image；If so, thening follow the steps S23.
Step S23: to the sample image and target image progress hough change detection after denoising, sample graph is found out respectively
The boundary of picture and target image obtains boundary set.If the image after denoising is target image, the target image after denoising is carried out
Hough change detection finds out the boundary (rectangle, diamond shape, circle, ellipse) of image, obtains boundary set.
Hough transformation with the transformation between two coordinate spaces by a space with same shape curve or
Straight line is mapped on a point of another coordinate space and forms peak value, so that the problem of detection arbitrary shape is converted into statistics
Spike problem.It is required when recognition matrix, circle, ellipse using different parameters.
Step S24: the boundary of the boundary set of target image and sample image is subjected to matching treatment, to treated image
The edge detection for carrying out canny operator, respectively obtains the binary map of sample image and target image.
It is described by the progress of the boundary of the boundary set of target image and sample image in a wherein embodiment of the invention
Include: with the step of processing
The boundary of the boundary set of target image and sample image is compared, then target image is rotated and contracted
Operation is put, the boundary on the boundary and sample image that make target image coincide.
Step S3: the binary map of binary map and target image to sample image carries out difference operation, obtains poor shadow figure and lacks
Value is fallen into, judges whether target image is defective according to defective value.
In a wherein embodiment of the invention, image capture module shoots product to be detected by industrial camera, obtains
The sample image and target image of product to be detected, it is collected with the camera in image capture module after above-mentioned steps S3
Picture judges whether there is subsequent product, if so, then repeating the above steps 1 to step 3.
It please refers to Fig. 3 and show a kind of structure of the product surface shape defect detection system based on machine vision of the present invention
Schematic diagram, the detection system include: image capture module 10, for obtaining the sample image and target image of product to be detected；
Image processing module 11 obtains sample image and target for pre-processing to the sample image and target image respectively
The binary map of image；Image analysis module 30, for the binary map of binary map and the target image to the sample image
Difference operation is carried out, obtains poor shadow figure and defective value, and judge whether the target image is defective according to the defective value.
In a wherein embodiment of the invention, image capture module 10 includes industrial camera 11 and transmission belt 12, industry
Camera 11 obtains the sample image and target image of product to be detected for shooting product to be detected.Specifically, conveyer belt 12 will
Product to be detected, which is transported to, formulates position, and CMOS industrial camera 12 shoots the image of product to be detected, obtains the sample of product to be detected
This image and target image, wherein specifically, the dark bright degree of each acquired image can also be made by LED come light filling
It is similar；Product is transported to Position Approximate, while the phase of conveyer belt 12 and industrial camera 11 every time in transmission process by conveyer belt 12
It is also fixed as far as possible to position, error brought by the rotation, scaling when can reduce picture match in this way.
Image processing module 20 connects image capture module 10, the sample image and target obtain to image capture module 10
Image is pre-processed, and obtains the binary map of sample image and target image respectively；Specifically, image processing module 20 is to image
10 acquired image of acquisition module carries out gray processing, is then denoised with median filtering method, allows to adapt to subsequent
Processing；First carry out hough variation and canny algorithm to the image after denoising and carry out edge detection, obtain respectively sample image and
The binary map of target image.
Image analysis module 30 connects image processing module 20, to the two-value for the sample image that image processing module 20 transmits
The binary map of figure and target image carries out difference operation, obtains poor shadow figure and defective value, judges the target according to the defective value
Whether image is defective.In a wherein embodiment of the invention, image analysis module 30 transmits image processing module 20
The binary map of sample image and the binary map of target image first rotated, scaled, cut sequence of operations after make target image
It is matched with sample image, two figures is finally subjected to difference operation and opening operation, obtain poor shadow figure and defective value, when defective value is more than threshold
Value is then to be defective, otherwise then zero defect.
It further include memory module in a wherein embodiment of the invention, which connects image analysis module 30,
For storing the analysis result and difference shadow figure of image analysis module 30.Memory module simultaneously will analysis result and poor shadow figure feedback to visitor
Family end.
The present invention uses product surface shape defect detection method and system based on machine vision, passes through computer and work
The mode that industry camera combines, the quality of contactless automatic testing product picture on surface, without fetching faulty goods one by one
Comparison, greatly reduces the workload of staff.And this method is easy to extend, and if desired also checks color, it only need to be
Color contrast algorithm is added in image comparison and analysis in image processing module.
The above embodiments merely illustrate the technical concept and features of the present invention, and its object is to allow person skilled in the art
It is to can understand the content of the present invention and implement it accordingly, it is not intended to limit the scope of the present invention.All such as present invention essences
The equivalent transformation or modification that refreshing essence is done, should be covered by the protection scope of the present invention.
Claims (8)
1. a kind of product surface shape defect detection method based on machine vision, which comprises the following steps:
The sample image and target image of product to be detected are obtained by Image Acquisition；
The sample image and target image are pre-processed, obtain the binary map of sample image and target image respectively；
The binary map of binary map and the target image to the sample image carries out difference operation, obtains poor shadow figure and defect
Value, judges whether the target image is defective according to the defective value.
2. the product surface shape defect detection method according to claim 1 based on machine vision, which is characterized in that institute
It states and the sample image and target image is pre-processed, respectively the step of binary map of acquisition sample image and target image
Include:
Gray proces are carried out to the sample image and target image, intermediate value is passed through to treated sample image and target image
Filtering is denoised；
Whether the image after judging denoising is target image；
If so, finding out sample image and mesh respectively to sample image and target image progress hough change detection after denoising
The boundary of logo image obtains boundary set；
The boundary of the boundary set of target image and sample image is subjected to matching treatment, image carries out canny calculation to treated
The edge detection of son, respectively obtains the binary map of sample image and target image.
3. the product surface shape defect detection method according to claim 2 based on machine vision, which is characterized in that institute
Stating the step of boundary of the boundary set of target image and sample image is carried out matching treatment includes:
The boundary of the boundary set of target image and sample image is compared, rotation then is carried out to target image and scales behaviour
Make, the boundary on the boundary and sample image that make target image coincide.
4. the product surface shape defect detection method according to claim 2 based on machine vision, which is characterized in that institute
Stating the step of carrying out gray proces to the sample image and target image includes:
Gray proces are carried out to the sample image and target image by weighted mean method.
5. the product surface shape defect detection method according to claim 1 based on machine vision, which is characterized in that institute
It states and judges that the whether defective step of the target image includes: according to the defective value
Target image is defective if defective value is greater than threshold value, and otherwise, target image does not have defect.
6. a kind of product surface shape defect detection system based on machine vision, which is characterized in that the system comprises:
Image capture module obtains the sample image and target image of product to be detected；
Image processing module pre-processes the sample image and target image, obtains sample image and target figure respectively
The binary map of picture；
The binary map of image analysis module, binary map and the target image to the sample image carries out difference operation, obtains
Poor shadow figure and defective value judge whether the target image is defective according to the defective value.
7. the product surface shape defect detection system according to claim 6 based on machine vision, which is characterized in that also
Including memory module, the memory module connects described image analysis module, for storing the analysis result of image analysis module
With poor shadow figure.
8. the product surface shape defect detection system according to claim 6 based on machine vision, which is characterized in that figure
As acquisition module shoots product to be detected by industrial camera, the sample image and target image of product to be detected are obtained.
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Cited By (3)
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| CN110567983A (en) * | 2019-09-09 | 2019-12-13 | 深圳市三宝创新智能有限公司 | Method and system for detecting appearance problem of robot |
| CN111242888A (en) * | 2019-12-03 | 2020-06-05 | 中国人民解放军海军航空大学 | Image processing method and system based on machine vision |
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