CN114354607A - Luminosity three-dimensional flaw detection method based on spiral phase contrast filtering algorithm - Google Patents
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Abstract
The invention discloses a luminosity three-dimensional flaw detection method based on a spiral phase contrast filtering algorithm, which comprises a data acquisition part and a data processing part, wherein the data acquisition part is used for acquiring luminosity three-dimensional flaw data; the data acquisition part comprises: obliquely emitting light emitted by the light source to the surface of a Lambert reflecting object to form diffuse reflection; the resolution required by object defects is calculated, and the proper multiplying power and the camera size of the required objective lens are calculated; shooting and storing a reflected light intensity image by the objective lens and the camera group; the light source is respectively placed on a plurality of directions of the object to obliquely irradiate the object, and a plurality of reflected light intensity graphs corresponding to the plurality of directions are acquired. The invention comprises the following steps: 1. spiral match filtering is added into the luminosity stereo algorithm, the flaw position edge is enhanced, the surface background of the object is removed, background interference is avoided, and the image contrast is enhanced. 2. Compared with the existing threshold segmentation background and flaw method, the method has more obvious background removing effect. 3. And gamma compression is introduced to process the image, so that the image is smoother and the human visual characteristics are compensated.
Description
Technical Field
The invention relates to the fields of spiral phase contrast filtering algorithm in the field of phase imaging and luminosity three-dimensional algorithm in machine vision, in particular to a luminosity three-dimensional flaw detection method based on the spiral phase contrast filtering algorithm.
Background
In the last century, in order to perform label-free imaging on an object, zernike developed a phase contrast microscopic imaging technology, Gabor proposed holography, researchers developed a spiral phase contrast filtering imaging method combining phase contrast microscopy and holography, and an image generated by the label-free technology contains refractive index information of the thickness and the surface structure of the object, enhances and highlights the edge of the surface structure of the object, removes and balances the surface background, so that the edge of the surface structure has higher contrast with the background, and simultaneously avoids background interference caused by a strong background.
In recent years, it has become increasingly important to capture the "look" of an object from an image. By "appearance" is generally meant a model that is able to predict the image of an object under all possible fields of view and lighting conditions. The appearance of an object is adequately sampled because it does vary arbitrarily in shape and reflectivity, requiring images combined from various views and light sources, which is impractical in most cases. Fortunately, objects in the real world often exhibit regularity, which can be exploited to greatly reduce the number of images required. Therefore, effective (or near effective) constraints are chosen and strong enough to be used in practical systems, which is crucial for appearance capture. Currently, there are stereovision inspection works that have shown that under known lighting conditions, camera viewing angle determination is feasible to recover a definite appearance model from the changing image. This is an important special case appearance capture because it relies only on photometric cues to avoid solving the correspondence problem. This is also important because such explicit appearance models have proven to be useful for visual tasks such as flaw detection. However, in the field of defect detection, the image processed by the photometric stereo method needs background elimination and balanced smoothing, only the defect position is highlighted, and the background and the defect position form the sharpest contrast. The general approach to achieve this is to set a threshold to separate the background from the defect, but this approach sometimes causes misjudgment due to the close pixel values of the background and the defect, and the background and the defect cannot be completely separated. Therefore, a photometric stereo flaw detection method based on a spiral phase contrast filtering algorithm is urgently needed to solve the problem existing in the prior art.
In order to solve the technical problems, a new technical scheme is especially provided.
Disclosure of Invention
The invention aims to provide a luminosity three-dimensional flaw detection method based on a spiral phase contrast filtering algorithm, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a luminosity three-dimensional flaw detection method based on a spiral phase contrast filtering algorithm comprises a data acquisition part and a data processing part;
the data acquisition part comprises the following steps:
step a, obliquely emitting light emitted by a light source to the surface of a Lambertian reflecting object;
c, forming diffuse reflection on the surface of the object by the incident light;
step c, counting the required resolution of object flaws, and calculating the required lens magnification and the number of camera pixels according to the required resolution;
d, placing the lens and the camera combination right above the object to form orthogonal projection and collect reflected light carrying the surface structure information of the object;
step e, connecting the camera with a computer, shooting through camera software on the computer and storing the intensity map of the reflected light;
and f, respectively placing the light sources on a plurality of directions of the object to obliquely irradiate the object, repeating the steps a to e for a plurality of times, and acquiring a plurality of reflected light intensity maps corresponding to the plurality of directions.
Preferably, the data processing section includes the steps of:
step 1, calibrating the direction of a light source: first orthogonal projection scenePlacing a smooth ball in the system, shooting a reflected light intensity map under the conditions of 4 different light source directions, placing the 4 intensity maps into MATLAB to calculate and fit the circle boundary of the smooth ball, and positioning the coordinate (xc) of the circle centeri，yci) I is an index of 4 images;
and 2, respectively positioning the highlight positions of the surfaces of the spheres on the 4 graphs, wherein the coordinates of the highlight positions are expressed as (hxc)i，hyci) And the direction of the light source is reflected by the highlight position of the surface;
and 3, calculating a normal vector of the surface of the smooth sphere according to the following formula:
n:nx＝hxci-xci，ny＝hyci-yci，
step 4, estimating the illumination intensity: an illumination parameter cost function is defined by the following formula:
Step 5, performing object surface structure reflectivity rho obtained by photometric stereojSum normal vector nj：ρj＝||bj||，
Step 6, judging the defect type and the characteristics of the position of the defect type in the image according to the two-dimensional image, and measuring the third channel data n (3) of the defect type according to the obtained normal amount;
step 7, writing a spiral phase plate function
Step 8, writing a 4f system, placing n (3) as an object at the position of an object plane of the 4f system, performing spiral phase contrast imaging, and solving a complex function U at the image plane:
f represents a Fourier transform;
step 9, taking the amplitude A of the image plane complex function U:
step 10, carrying out gamma compression on the obtained amplitude A to obtain a final object flaw image Iout：Iout＝BAγ. B is a constant, gamma is a correction parameter, and a fraction less than 1 is taken and is called the encoding gamma value.
Preferably, the formula of the direction of the light source is: l is 2nzn-R, wherein R represents a camera direction of (0,0,1) and coordinates of (0,0, -1) if viewed from the camera perspective;
preferably, the minimization formula of the cost function is:
preferably, the first and second electrodes are formed of a metal,
is a non-linear least squares problem that can be solved by the Levenberg-Marquardt algorithm.
Preferably, for all eiHas Ij＝LTbjThen b can be obtained according to the least square methodj＝(LLT)-1LIj。
Preferably, the plurality of orientations of the object in step f refers to selecting a plurality of angles in the 360 ° direction of the object, for example, four angles of 0 °, 90 °, 180 °, and 270 °.
Preferably, said plurality of steps f refers to 3 or more, preferably 4.
Preferably, the step f is to place the light source on the front, back, left, and right of the object respectively to irradiate the object obliquely, repeat the above steps a to e four times, and acquire 4 reflected light intensity maps corresponding to the 4 orientations.
Compared with the prior art, the invention has the beneficial effects that:
1. spiral match filtering is added into the luminosity stereo algorithm, the flaw position edge is enhanced, the surface background of the object is removed, background interference is avoided, and the image contrast is enhanced.
2. Compared with the existing threshold segmentation background and flaw method, the method has more obvious background removing effect.
3. And gamma compression is introduced to process the image, so that the image is smoother and the human visual characteristics are compensated.
Drawings
FIG. 1 is a schematic diagram of a photometric stereo imaging optical set providing illumination with a light source tilted at a 0 position above an object from a camera perspective.
FIG. 2 is a schematic diagram of a photometric stereo imaging optical set providing illumination with a light source tilted at a 90 position above an object from a camera perspective.
FIG. 3 is a schematic diagram of a photometric stereo imaging optical set providing illumination with a light source tilted 180 above an object from a camera perspective.
FIG. 4 is a schematic diagram of a photometric stereo imaging optical set providing illumination of a light source tilted at a 270 position above an object from a camera perspective.
Fig. 5 is a diagram illustrating a lambertian reflection characteristic.
FIG. 6 is a schematic view of the light source direction calibration.
Fig. 7 is a schematic diagram of a spiral phase plate.
FIG. 8 is a camera shot intensity chart of the light source irradiating the steel material of the automobile door at the 0 degree position.
FIG. 9 is a camera shot intensity chart of the light source irradiating the steel material of the automobile door at the 90 degree position.
FIG. 10 is a camera shot intensity chart of the light source irradiating the steel material of the automobile door at the 180 degree position.
FIG. 11 is a photograph of the camera intensity of the light source irradiating the steel material of the door of the automobile at 270 deg.
Fig. 12 is an image of the 4 shot intensity maps of fig. 8 to 11 after being processed by the photometric stereo algorithm.
Fig. 13 is the image of fig. 12 after being processed by the spiral phase contrast filtering algorithm.
FIG. 14 is the gamma compressed image of FIG. 13.
Fig. 15 is a photograph intensity chart of the light source illuminating the hoop camera at the 0 ° position.
Fig. 16 is a photograph intensity diagram of a light source illuminating a hoop camera at a 90 ° position.
Fig. 17 is a diagram of the shooting intensity of the light source illuminating the hoop camera at the 180 ° position.
Fig. 18 is a photograph of the intensity of the light source illuminating the hoop camera at the 270 ° position.
Fig. 19 is an image of the 4 shot intensity maps of fig. 15 to 18 after being processed by a photometric stereo algorithm.
Fig. 20 is the image of fig. 19 after being processed by the spiral phase contrast filtering algorithm.
FIG. 21 is the gamma compressed image of FIG. 20.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the attached drawings of the specification, the invention provides a technical scheme that: a luminosity three-dimensional flaw detection method based on a spiral phase contrast filtering algorithm comprises the following steps:
firstly, a data acquisition part.
1. The light emitted by the light source is obliquely incident on the surface of the lambertian reflecting object.
2. The incident light forms diffuse reflection on the surface of the object.
3. And counting the required resolution of the object defects, and calculating the required lens magnification and the number of camera pixels according to the required resolution.
4. The lens and the camera are combined and placed right above the object to form orthogonal projection to collect reflected light carrying the surface structure information of the object.
5. The camera is connected with a computer, and the intensity map of the reflected light is shot and stored through camera software on the computer.
6. The light source is respectively placed in 4 directions of the front, the back, the left and the right of the object to obliquely irradiate the object, the steps 1 to 5 are repeated four times, and 4 reflected light intensity graphs corresponding to the 4 directions are acquired (if necessary, a plurality of reflected light intensity graphs can be acquired, but at least 3 reflected light intensity graphs are acquired and are uniformly distributed on the 360-degree direction of the object).
And secondly, a data processing part.
8. Calibrating the light source direction: firstly, placing a smooth ball in an orthogonal projection scene, shooting a reflected light intensity map under the conditions of 4 different light source directions, putting 4 intensity maps into MATLAB to calculate and fit a circle boundary of the smooth ball, and positioning a circle center coordinate (xc)i，yci) And i is an index of 4 images.
9. The surface highlights of the sphere on the 4 graphs are located separately, and the coordinates of the highlights are represented as (hxc)i，hyci) And the direction of the light source is reflected by the highlight position of the surface.
10. Calculating the normal vector n of the smooth sphere surfacex＝hxci-xci，ny＝hyci-yci，
11. Light source direction L2 nzn-R, R indicates that the camera direction is (0,0,1), and the coordinates are (0,0, -1) when viewed from the camera perspective.
12. Estimating the illumination intensity: an illumination parameter cost function is defined,
Minimization of cost function
14.
This is a non-linear least squares problem that can be solved by the Levenberg-Marquardt algorithm.
15. For all eiHas Ij＝LTbjThen b can be obtained according to the least square methodj＝(LLT)-1LIj。
16. Object surface structure reflectivity rho for executing photometric stereo solutionjSum normal vector nj：ρj＝||bj||，
17. Since only two-dimensional images are needed to determine the defect type and its position in the image, the obtained normal amount is taken as the third channel data n (3).
18. Writing a spiral phase plate function
19. Writing a 4f system, placing n (3) as an object at the position of an object plane of the 4f system, performing spiral phase contrast imaging, and solving a complex function U at an image plane:
f denotes fourier transform.
20. Taking the amplitude A of the image plane complex function U:
21. carrying out gamma compression on the obtained amplitude A to obtain a final object flaw image Iout：Iout＝BAγ. B is a constant, gamma is a correction parameter, and a fraction less than 1 is taken and is called the encoding gamma value.
The image forming apparatus of the present invention includes: 4 light sources with consistent light intensity, object to be measured, objective lens, industrial camera set and computer
Wherein the objective lens and the industrial camera are connected in combination, and the sensor of the industrial camera is located at the focal plane position of the objective lens. The computer is connected with the industrial camera through a data line and is used for storing the 4 reflection intensity images shot by the camera into the computer and carrying out algorithm processing on the collected images on the computer.
As shown in fig. 1, the method comprises the following steps:
s1, from the camera view angle, 4 light sources with consistent light intensity are respectively placed in four directions of 0 degree, 90 degrees, 180 degrees and 270 degrees of an object to be detected for illumination. The 4 light sources are sequentially turned on to irradiate the object to be detected, and only one light source is turned on at a time.
And S2.4, the light emitted by the light sources is sequentially incident on the surface of the object to be measured to form diffuse reflection.
And S3, the objective lens and the camera group are positioned right above the object to be detected and used for sequentially collecting reflected light transmitted from the object, and 4 signals are sequentially formed on the camera.
And S4, sequentially transmitting 4 signals on the camera to a computer through a data line to form 4 images.
In use, the invention: 1. spiral match filtering is added into the luminosity stereo algorithm, the flaw position edge is enhanced, the surface background of the object is removed, background interference is avoided, and the image contrast is enhanced. 2. Compared with the existing threshold segmentation background and flaw method, the method has more obvious background removing effect. 3. And gamma compression is introduced to process the image, so that the image is smoother and the human visual characteristics are compensated.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A luminosity three-dimensional flaw detection method based on a spiral phase contrast filter algorithm is characterized by comprising a data acquisition part and a data processing part;
the data acquisition part comprises the following steps:
step a, obliquely emitting light emitted by a light source to the surface of a Lambertian reflecting object;
b, forming diffuse reflection on the surface of the object by the incident light;
step c, counting the required resolution of object flaws, and calculating the required lens magnification and the number of camera pixels according to the required resolution;
d, placing the lens and the camera combination right above the object to form orthogonal projection and collect reflected light carrying the surface structure information of the object;
step e, connecting the camera with a computer, shooting through camera software on the computer and storing the intensity map of the reflected light;
and f, respectively placing the light sources on a plurality of directions of the object to obliquely irradiate the object, repeating the steps a to e for a plurality of times, and acquiring a plurality of reflected light intensity maps corresponding to the plurality of directions.
The data processing section includes the steps of:
step 1, calibrating the direction of a light source: firstly, placing a smooth ball in an orthogonal projection scene, shooting a reflected light intensity map under the conditions of 4 different light source directions, putting 4 intensity maps into MATLAB to calculate and fit a circle boundary of the smooth ball, and positioning a circle center coordinate (xc)t，yct) I is an index of 4 images;
and 2, respectively positioning the highlight positions of the surfaces of the spheres on the 4 graphs, wherein the coordinates of the highlight positions are expressed as (hxc)i，hyci) And the direction of the light source is reflected by the highlight position of the surface;
and 3, calculating a normal vector of the surface of the smooth sphere according to the following formula:
step 4, estimating the illumination intensity: an illumination parameter cost function is defined by the following formula:
Step 5, performing object surface structure reflectivity rho obtained by photometric stereojSum normal vector nj：ρj＝||bj||，
Step 6, judging the defect type and the characteristics of the position of the defect type in the image according to the two-dimensional image, and measuring the third channel data n (3) of the defect type according to the obtained normal amount;
step 7, writing a spiral phase plate function
Step 8, writing a 4f system, placing n (3) as an object at the position of an object plane of the 4f system, performing spiral phase contrast imaging, and solving a complex function U at the image plane:
f represents a Fourier transform;
step 9, taking the amplitude A of the image plane complex function U:
step 10, carrying out gamma compression on the obtained amplitude A to obtain a final object flaw image Iout：IoutBA γ. B is a constant, gamma is a correction parameter, and a fraction less than 1 is taken and is called the encoding gamma value.
2. The photometric stereo flaw detection method based on the spiral phase contrast filtering algorithm according to claim 1, characterized in that: the direction formula of the light source is as follows: l is 2nzn-R, where R denotes the camera direction as (0,0,1) and coordinates as (0,0, -1) if viewed from the camera perspective.
3. The photometric stereo flaw detection method based on the spiral phase contrast filtering algorithm according to claim 1, characterized in that: the minimization formula of the cost function is:
4. The photometric stereo flaw detection method based on the spiral phase contrast filtering algorithm according to claim 3, characterized in that:
is a non-linear least squares problem that can be solved by the Levenberg-Marquardt algorithm.
5. The photometric stereo flaw detection method based on the spiral phase contrast filtering algorithm according to claim 4, characterized in that: for all eiHas Ij＝LTbjThen b can be obtained according to the least square methodj＝(LLT)-1LIj。
6. The photometric stereo flaw detection method based on the spiral phase contrast filtering algorithm according to claim 1, characterized in that: the multiple orientations of the object in step f refer to selecting multiple angles in the 360-degree direction of the object, and take four angles of 0 degrees, 90 degrees, 180 degrees and 270 degrees as examples.
7. The photometric stereo defect detection method based on the helical phase contrast filtering algorithm according to claim 6, characterized in that: the plurality of steps f refers to 3 or more, preferably 4.
8. The photometric stereo flaw detection method based on the helical phase contrast filtering algorithm according to claim 7, characterized in that: and step f specifically, placing the light source on the front, back, left and right of the object respectively to obliquely irradiate the object in 4 directions, repeating the steps a to e four times, and acquiring 4 corresponding reflected light intensity maps in 4 directions.
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