CN102914545A - Gear defect detection method and system based on computer vision - Google Patents
Gear defect detection method and system based on computer vision Download PDFInfo
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Abstract
The invention relates to the technical field of image processing and provides a gear defect detection method and system based on computer vision. The method comprises the following steps of: acquiring a source image of a detected gear acquired by an image acquisition card through a peripheral component interconnect (PCI) slot; performing preprocessing operation comprising grey level transformation, filtering and noise reduction and threshold segmentation on the source image of the detected gear, and obtaining gear parameters of the detected gear; and performing morphological parameter analysis on the gear parameters of the detected gear, and acquiring an analysis result of the teeth where the defects possibly exist. The gear defect detection method is high in measurement speed and high in precision, the functional requirements of the conventional gear parameter measurement are met, the production efficiency and production automation degree can be greatly improved in the large-scale industrial production process, the measurement precision requirement in the actual production can be met, the measurement time is only several seconds, the requirement on the measurement speed in industrial measurement can be met, and the method has high theoretical and practical values.
Description
Technical field
The invention belongs to technical field of image processing, relate in particular to a kind of gear defects detection method and system based on computer vision.
Background technology
Gear has been brought into play very important effect as a kind of typical transmission of power device in the course of industrial development, the quality of its quality directly affects the performance of engineering goods.Therefore, in the process of Gear Production manufacturing, must carry out strict detection and control to product quality.Because the geometric features of gear itself, for tooth collapse, the measuring process of the defective such as hypodontia, tooth are askew is complicated, conventional measuring equipment such as the complex structures such as three coordinate measuring machine, Error of Gears somascope, expensive, also higher to tester's requirement.The development of modern manufacturing industry is had higher requirement to the defects detection of gear product.
At present, gear testing method based on computer vision needs generally at first to determine that the central coordinate of circle of gear is as benchmark, therefore, the establishment of central coordinate of circle directly has influence on the precision of gear tooth parameter measurement, in traditional image method detects, center of circle Spot detection algorithm has the edge that generally needs edge detection method to determine gear, then adopts gravity model appoach, median method and Hough converter technique to determine the center of circle.
Front two kinds of algorithms require image distribution more even, otherwise produce larger error; Rear a kind of algorithm needs pointwise ballot, record, and the used time is more, and precision is also not high enough.
Summary of the invention
The object of the present invention is to provide a kind of gear defects detection method based on computer vision, be intended to solve efficient and the lower problem of precision of the gear defects detection algorithm that prior art provides.
The present invention is achieved in that a kind of gear defects detection method based on computer vision, and described method comprises the steps:
Obtain the source images of the tested gear that image pick-up card collects by the PCI slot;
The source images of the tested gear that acquires is comprised the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, obtain the gear parameter of tested gear;
Gear parameter to the tested gear that obtains carries out the Morphologic Parameters analysis, obtains the analysis result that comprises the number of teeth that may have defective.
As a kind of improved plan, described source images to the tested gear that acquires comprises the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, and the step that obtains the gear parameter of tested gear specifically comprises the steps:
24 source color images of the tested gear that collects are carried out greyscale transform process, obtain 8 gray scale gear graph pictures;
Utilize median filtering algorithm, described 8 gray scale gear graphs are looked like to carry out noise removal;
Utilize Image binarizing algorithm, the gray scale gear graph is looked like to carry out Threshold segmentation, the gear that obtains separating and background image.
As a kind of improved plan, described gear parameter to the tested gear that obtains carries out the Morphologic Parameters analysis, and the step of obtaining the analysis result that comprises the number of teeth that may have defective specifically comprises the steps:
Utilize filling algorithm, the hole of the gear graph picture that described Binarization methods is obtained is filled;
Binary image after the hole filling as target, is determined to calculate central coordinate of circle, root radius and the radius of addendum of described binary image;
According to central coordinate of circle, root radius and the radius of addendum of the described binary image that calculates, tooth top to be separated with tooth root, detection of gear gear teeth defective obtains tooth root image and tooth top image;
By the region labeling method, tooth root image and the tooth top image that obtains carried out defect analysis, the number of teeth of gears is determined the defect area of described tooth root and tooth top image.
As a kind of improved plan, described binary image after hole is filled is as target, and the step of determining to calculate central coordinate of circle, root radius and the radius of addendum of described binary image specifically comprises the steps:
Read in the binary image after hole is filled, the coordinate on the record gear;
Search the solstics set of the described picture centre of distance and closest approach set at described binary image, obtain the coordinate that can comprise respectively the minimal convex polygon of gear;
According to the coordinate of minimal convex polygon, draw out respectively convex polygon;
Described convex polygon is carried out respectively the Circular curve fitting of least square method, the central coordinate of circle of gears, root radius and radius of addendum.
As a kind of improved plan, the central coordinate of circle of the described binary image that described basis calculates, root radius and radius of addendum, tooth top is separated with tooth root, detection of gear gear teeth defective, the step that obtains tooth root image and tooth top image specifically comprises the steps:
In binary image, draw one take certain numerical value between root radius and radius of addendum as radius and calculate auxiliary circle;
Arrange one and cover the plate image, described illiteracy plate image size is identical with gear graph picture size, and the radius of described illiteracy plate image equals half of radius of addendum and root radius sum;
Gear graph picture and described illiteracy plate image after the picture calculating auxiliary circle are done difference operation, obtain tooth top figure image;
Gear graph after described illiteracy plate image and the picture calculating auxiliary circle is looked like to do difference operation, obtain tooth root figure image.
As a kind of improved plan, described by the region labeling method, tooth root image and the tooth top image that obtains carried out defect analysis, the number of teeth of gears, determine that the step of the defect area of described tooth root and tooth top image specifically comprises:
Respectively tooth root image and the tooth top image that obtains carried out zone marker, obtain tooth root image and tooth top image that pseudo-color mode shows;
Tooth root image and tooth top image that pseudo-color mode is shown carry out respectively number and the calculating of single area;
Tooth top and tooth root area are added up, asked for respectively the mean value of tooth top area and tooth root area;
By setting the mode of area threshold, determine tooth root and tooth top defect area.
The purpose of another embodiment of the present invention is to provide a kind of gear defects detection system based on computer vision, and described system comprises:
The source images acquisition module is used for obtaining by the PCI slot source images of the tested gear that image pick-up card collects;
The pretreatment operation module is used for the source images of the tested gear that acquires is comprised the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, obtains the gear parameter of tested gear;
The Morphologic Parameters analysis module is used for the gear parameter of the tested gear that obtains is carried out the Morphologic Parameters analysis, obtains the analysis result that comprises the number of teeth that may have defective.
As a kind of improved plan, described pretreatment operation module specifically comprises:
The greyscale transform process module, 24 source color images that are used for the tested gear that will collect carry out greyscale transform process, obtain 8 gray scale gear graph pictures;
The noise removal module is used for utilizing median filtering algorithm, and described 8 gray scale gear graphs are looked like to carry out noise removal;
The Threshold segmentation module is used for utilizing Image binarizing algorithm, and the gray scale gear graph is looked like to carry out Threshold segmentation, the gear that obtains separating and background image.
As a kind of improved plan, described Morphologic Parameters analysis module specifically comprises:
The hole packing module is used for utilizing filling algorithm, and the hole of the gear graph picture that described Binarization methods is obtained is filled;
Center of circle radius calculation module as target, determines to calculate central coordinate of circle, root radius and the radius of addendum of described binary image for the binary image after hole is filled;
Tooth root image and tooth top image obtain module, are used for central coordinate of circle, root radius and radius of addendum according to the described binary image that calculates, and tooth top is separated with tooth root, and detection of gear gear teeth defective obtains tooth root image and tooth top image;
The defect analysis module is used for by the region labeling method tooth root image and the tooth top image that obtains being carried out defect analysis, and the number of teeth of gears is determined the defect area of described tooth root and tooth top image;
Described center of circle radius calculation module specifically comprises:
The coordinate record module is used for reading in the binary image after hole is filled, the coordinate on the record gear;
Point is searched module, is used for searching the solstics set of the described picture centre of distance and closest approach set at described binary image, obtains the coordinate that can comprise respectively the minimal convex polygon of gear;
Drafting module is used for the coordinate according to minimal convex polygon, draws out respectively convex polygon;
The Fitting Calculation module is for the Circular curve fitting that described convex polygon is carried out respectively least square method, the central coordinate of circle of gears, root radius and radius of addendum;
Described tooth root image and tooth top image obtain module and specifically comprise:
Calculate the auxiliary circle drafting module, be used at binary image, draw one take certain numerical value between root radius and radius of addendum as radius and calculate auxiliary circle;
Cover plate image setting module, be used for arranging one and cover the plate image, described illiteracy plate image size is identical with gear graph picture size, and the radius of described illiteracy plate image equals half of radius of addendum and root radius sum;
The first difference operation module, the gear graph picture and the described illiteracy plate image that are used for drawing after the calculating auxiliary circle are done difference operation, obtain tooth top figure image;
The second difference operation module is used for described illiteracy plate image and the gear graph after drawing the calculating auxiliary circle looks like to do difference operation, obtains tooth root figure image;
Described defect analysis module specifically comprises:
The zone marker module is used for respectively tooth root image and the tooth top image that obtains being carried out zone marker, obtains tooth root image and tooth top image that pseudo-color mode shows;
Computing module is used for the tooth root image that pseudo-color mode is shown and the tooth top image carries out respectively number and single area calculates;
Statistical module is used for tooth top and tooth root area are added up, and asks for respectively the mean value of tooth top area and tooth root area;
Determination module is used for determining tooth root and tooth top defect area by setting the mode of area threshold.
In embodiments of the present invention, obtain the source images of the tested gear that image pick-up card collects by the PCI slot; The source images of the tested gear that acquires is comprised the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, obtain the gear parameter of tested gear; Gear parameter to the tested gear that obtains carries out the Morphologic Parameters analysis, obtains the analysis result that comprises the number of teeth that may have defective.Not only measuring speed is fast but also precision is high for the embodiment of the invention, satisfied the functional requirement that the gear conventional parameter is measured, the automaticity that can greatly enhance productivity and produce in the industrial processes in enormous quantities, can satisfy the measuring accuracy requirement in the production reality, and Measuring Time only is several seconds, can satisfy in the commercial measurement the requirement of measuring speed, have larger theory and practical value.
Description of drawings
Fig. 1 is the realization flow figure based on the gear defects detection method of computer vision that the embodiment of the invention provides;
Fig. 2 is the pretreatment operation that the source images to the tested gear that acquires that the embodiment of the invention provides comprises greyscale transformation, filtering and noise reduction and Threshold segmentation, obtains the realization flow figure of the gear parameter of tested gear;
Fig. 3 is the realization synoptic diagram of the noise that brings of removal of images collection that the embodiment of the invention provides;
Fig. 4 a is that the medium filtering that the embodiment of the invention provides is processed front gear image synoptic diagram;
Fig. 4 b is that the medium filtering that the embodiment of the invention provides is processed backgear image synoptic diagram;
Fig. 5 a is the greyscale transformation histogram that the embodiment of the invention provides;
Fig. 5 b is the binaryzation gear graph picture that the embodiment of the invention provides;
Fig. 6 is that the gear parameter to the tested gear that obtains that the embodiment of the invention provides carries out the Morphologic Parameters analysis, obtains the realization flow figure of the analysis result that comprises the number of teeth that may have defective;
Fig. 7 is that the hole that the embodiment of the invention provides is filled backgear image synoptic diagram;
Fig. 8 be the embodiment of the invention provide hole is filled after binary image as target, determine to calculate the realization flow figure of central coordinate of circle, root radius and the radius of addendum of described binary image;
Fig. 8 a is the minimum polygon synoptic diagram that comprises gear that the embodiment of the invention provides;
Fig. 8 b is the circular synoptic diagram according to coordinate fitting that the embodiment of the invention provides;
Fig. 9 is central coordinate of circle, root radius and the radius of addendum of the described binary image that calculates of basis that the embodiment of the invention provides, tooth top is separated with tooth root, detection of gear gear teeth defective, the realization flow figure of acquisition tooth root image and tooth top image;
Figure 10 a is the synoptic diagram in the deletion target gear edge zone that provides of the embodiment of the invention;
Figure 10 b is the illiteracy plate image synoptic diagram that the embodiment of the invention provides;
Figure 10 c is that gear graph picture and the described illiteracy plate image that the embodiment of the invention provides made difference operation effect synoptic diagram;
Figure 10 d is the gear graph picture that provides of the embodiment of the invention and cover the plate image and make pseudo color image synoptic diagram behind the difference operation;
Figure 10 e is that illiteracy plate image and the gear graph that the embodiment of the invention provides looks like to do difference operation effect synoptic diagram;
Figure 10 f is that the illiteracy plate image that provides of the embodiment of the invention and gear graph look like to do pseudo color image synoptic diagram behind the difference operation;
Figure 11 is the region labeling method of passing through that the embodiment of the invention provides, and tooth root image and the tooth top image that obtains carried out defect analysis, and the number of teeth of gears is determined the specific implementation process flow diagram of the defect area of described tooth root and tooth top image;
Figure 12 a is the tooth top image synoptic diagram behind the mark that provides of inventive embodiments;
Figure 12 b is the area histogram of each tooth top image of providing of inventive embodiments;
Figure 13 is the structured flowchart based on the gear defects detection system of computer vision that the embodiment of the invention provides;
Figure 14 is the structured flowchart of the pretreatment operation module that provides of the embodiment of the invention;
Figure 15 is the structured flowchart of the Morphologic Parameters analysis module that provides of the embodiment of the invention;
Figure 16 is the structured flowchart of the center of circle radius calculation module that provides of the embodiment of the invention;
Figure 17 is the structured flowchart that the tooth root image that provides of the embodiment of the invention and tooth top image obtain module;
Figure 18 is the structured flowchart of the defect analysis module that provides of the embodiment of the invention.
Embodiment
In order to make purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, is not intended to limit the present invention.
The realization flow figure based on the gear defects detection method of computer vision that Fig. 1 shows that the embodiment of the invention provides, its concrete step is as described below:
In step S101, obtain the source images of the tested gear that image pick-up card collects by the PCI slot.
In embodiments of the present invention, be inserted with image pick-up card in the PCI of computing machine slot, image pick-up card connects video camera, and video camera is made a video recording to the gear that is placed on the workbench.
In step S102, the source images of the tested gear that acquires is comprised the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, obtain the gear parameter of tested gear.
In embodiments of the present invention, the gear parameter of this tested gear is gear graph picture and background image.
In step S103, the gear parameter of the tested gear that obtains is carried out the Morphologic Parameters analysis, obtain the analysis result that comprises the number of teeth that may have defective.
Following have detailed enforcement to describe, do not repeat them here, but not in order to limit the present invention.
As an alternative embodiment of the invention, the source images to the tested gear that acquires that Fig. 2 shows that the embodiment of the invention provides comprises the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, obtain the realization flow figure of the gear parameter of tested gear, its concrete step is as described below:
In step S201,24 source color images of the tested gear that collects are carried out greyscale transform process, obtain 8 gray scale gear graph pictures.
In this step, because the gear graph of camera acquisition similarly is coloured image, need to carries out gray processing when the digitizing and process in order to improve computing velocity.In conjunction with the susceptibility principle of human eye to color, we have adopted method of weighted mean, that is:
Y=ω
R*R+ω
G*G+ω
B*B
ω wherein
R, ω
G, ω
BBe respectively color component R, G, the corresponding weight of B, Y is the pixel value of gray-scale map corresponding point.
Because human eye is the highest to the susceptibility of green, the susceptibility of redness is taken second place, minimum to the susceptibility of blueness, therefore make ω
Gω
Rω
B, will obtain more rational gray level image.General parameter commonly used is set to ω
R=0.30, ω
G=0.59, ω
B=0.11 o'clock, the pixel value that obtains gray level image was 256 grades.
In step S202, utilize median filtering algorithm, described 8 gray scale gear graphs are looked like to carry out noise removal.
Wherein, for the noise that the removal of images collection brings, this paper selects to adopt the method for medium filtering.It is based on the nonlinear signal processing technology of a kind of energy establishment noise of sequencing statistical theory, its ultimate principle is to adopt a moving window that contains odd number point, replaces the gray-scale value of window center point pixel with the intermediate value of the gray-scale value of each point in the window.After the window of an at first definite odd number pixel, interior each pixel of window are lined up by the gray scale size, use the center pixel value of the gray-scale value replacement window of intermediate position, its process as shown in Figure 3.
Be formulated as: g (x, y)=median{f (i-k, j-l), (k, l) ∈ W}
Wherein, W is selected template, and selected template window size is generally selected the windows such as 3 * 3,5 * 5 or 7 * 7.Image before and after medium filtering is processed is shown in Fig. 4 a and Fig. 4 b.
In step S203, utilize Image binarizing algorithm, the gray scale gear graph is looked like to carry out Threshold segmentation, the gear that obtains separating and background image.
In this step, binary conversion treatment is to utilize object and the difference of its background on gamma characteristic that will extract in the image, image is considered as having the combination in the two classes zones (target and background) of different grey-scale, and its key is to select rational segmentation threshold.
When the gray-scale value of a pixel surpasses this threshold value, just can say that this pixel belongs to the interested target of people, otherwise then belong to background parts.
In the gear graph picture, only have a target and background, and the intensity profile of target and background is all more even, its histogram often is rendered as bimodal curve so, shown in Fig. 5 (a).Select two peak-to-peak a certain threshold value T, just can the pixel of image be divided into greater than T pixel group (target) and less than pixel group (background) two parts of T.In order to improve the adaptive ability of system, this paper takes the iteration threshold method, at first select an approximate threshold value as the initial value of estimated value, then continuously improve this estimated value, namely utilize initial value spanning subgraph picture, and select new threshold value according to the characteristic of subimage, and then utilize new threshold value to come split image, this segmentation result is better than the effect with the initial threshold split image.If original image is f (x, y), image is g (x, y) after the binaryzation, and threshold value is T, then:
Wherein, value is that 1 part represents the target gear, and value is that 0 part represents background.Image is shown in Fig. 5 (b) after the binary conversion treatment.
As an alternative embodiment of the invention, the gear parameter to the tested gear that obtains that Fig. 6 shows that the embodiment of the invention provides carries out the Morphologic Parameters analysis, obtain the realization flow figure of the analysis result that comprises the number of teeth that may have defective, its concrete step is as described below:
In step S301, utilize filling algorithm, the hole of the gear graph picture that described Binarization methods is obtained is filled.
In this step, according to the image pattern analysis of collection in worksite, owing to have hole in the binaryzation backgear image, therefore need filling algorithm that the hole in the gear graph picture is eliminated.
Its specific practice is: at first read in pretreated bianry image (first negate), initiation parameter.Point beginning in of polygonal region is put until the border with given color picture from inside to outside.If the border is with a kind of color appointment, but then seed fill algorithm individual element ground is processed until run into border color.Seed fill algorithm four connected domains commonly used and eight connected domain technology are carried out padding.Any point in the zone arrives any pixels in the zone by upper and lower, left and right, upper left, lower-left, upper right and eight directions in bottom right.So repeatedly, until to the entire image been scanned.
Area filling is finished, and the image after it is processed as shown in Figure 7.
In step S302, the binary image after the hole filling as target, is determined to calculate central coordinate of circle, root radius and the radius of addendum of described binary image.
Wherein, the gear center of circle is as the benchmark of gear tooth parameter measurement, determines that accurately the center of circle of tested gear is directly connected to measuring accuracy, and concrete step is following is described in detail for it.
In step S303, according to central coordinate of circle, root radius and the radius of addendum of the described binary image that calculates, tooth top to be separated with tooth root, detection of gear gear teeth defective obtains tooth root image and tooth top image.
Wherein, calculate center, root radius and the radius of addendum of image middle gear by above step after, just can detection of gear gear teeth defective, concrete step is following is described in detail for it.
In step S304, by the region labeling method, tooth root image and the tooth top image that obtains carried out defect analysis, the number of teeth of gears is determined the defect area of described tooth root and tooth top image.
Wherein, tooth top figure and tooth root image for above step obtains behind region labeling method marking image, just can obtain the actual geometric parameter of gear, and concrete step is following is described in detail for it.
As a specific embodiment of the present invention, Fig. 8 shows binary image after the embodiment of the invention provides hole is filled as target, determine the realization flow figure of central coordinate of circle, root radius and the radius of addendum of the described binary image of calculating, its concrete step is as described below:
In step S401, read in the binary image after hole is filled, the coordinate on the record gear.
In step S402, search the solstics set of the described picture centre of distance and closest approach set at described binary image, obtain the coordinate that can comprise respectively the minimal convex polygon of gear.
In step S403, according to the coordinate of minimal convex polygon, draw out respectively convex polygon.
In step S404, described convex polygon is carried out respectively the Circular curve fitting of least square method, the central coordinate of circle of gears, root radius and radius of addendum.
In conjunction with Fig. 8 a and Fig. 8 b, followingly provide its concrete realization:
(1) reads in bianry image.
(2) will process the gear that obtains in the binary map as target, the coordinate on the record gear.
(3) by finding out at edge image apart from the set in solstics, center, acquisition can comprise the coordinate of the minimal convex polygon of gear.
(4) draw out convex polygon according to coordinate, show.And carry out the class roundness measurement.Method is:
(5) utilize above-mentioned coordinate figure to utilize least square method to carry out the circular curve match.
(6) circle according to match calculates the center of circle (x
0, y
0) and radius of addendum r
H
(7) same reason is exactly point on the dedendum circle apart from the set of center closest approach, and it is carried out least square fitting, can obtain root radius rL.
(8) determine respectively inner circle and cylindrical after the match.Its formula is:
Wherein, Fig. 8 a is the minimum polygon that can comprise gear, according to the circle of its coordinate fitting shown in Fig. 8 b.
The central coordinate of circle of the described binary image that the basis that Fig. 9 shows the embodiment of the invention to be provided calculates, root radius and radius of addendum, tooth top is separated with tooth root, detection of gear gear teeth defective, obtain the realization flow figure of tooth root image and tooth top image, its concrete step is as described below:
In step S501, in binary image, draw one take certain numerical value between root radius and radius of addendum as radius and calculate auxiliary circle.
In this step, read in image, initiation parameter, for convenience of calculation, deletion target gear edge zone keeps pure gear target image among the former figure.The result is shown in Figure 10 a.
In edge image, take gear center hole as the center of circle, draw one take certain value between root radius and radius of addendum as radius and calculate auxiliary circle.Suppose that radius of addendum is rH, root radius is rL.Then auxiliary radius of a circle is:
r
M＝α×r
H+β×r
L
Wherein α and β are respectively the coefficient of point circle and dedendum circle, satisfy condition:
0＜α＜1,0＜β＜1, and alpha+beta=1.
In step S502, arrange one and cover the plate image, described illiteracy plate image size is identical with gear graph picture size, and the radius of described illiteracy plate image equals half of radius of addendum and root radius sum.
Wherein, a secondary blank image is set, big or small rooted tooth wheel image size is identical, take central point as the center of circle, with r
hBe circular plate that covers of radius, wherein pixel value is 1 in the circle, and an ancient official title's pixel value is 0.Suppose α=0.5, β=0.5, then obtainable illiteracy plate image radius size is:
In step S503, gear graph picture and described illiteracy plate image after the picture calculating auxiliary circle are done difference operation, obtain tooth top figure image.
Suppose that it is I that gear graph looks like
Gear, the masking-out image is I
Mask, tooth top figure image I then
OutBe expressed as:
I
H=I
gear-I
mask
Operation effect is shown in Figure 10 c, and wherein white portion represents tooth top, and wherein Figure 10 d is the pseudo color image after its actual computation.
In step S504, the gear graph after described illiteracy plate image and the picture calculating auxiliary circle is looked like to do difference operation, obtain tooth root figure image.
To cover plate figure image subtraction gear graph picture and obtain the tooth root image:
I
L＝I
mask-I
gear
The image operation effect is shown in Figure 10 e, and wherein Figure 10 f is the pseudo color image after its true calculating.
Figure 11 shows the region labeling method of passing through that the embodiment of the invention provides, tooth root image and the tooth top image that obtains carried out defect analysis, the number of teeth of gears is determined the specific implementation process flow diagram of the defect area of described tooth root and tooth top image, and its concrete step is as described below:
In step S601, respectively tooth root image and the tooth top image that obtains carried out zone marker, obtain tooth root image and tooth top image that pseudo-color mode shows.
In step S602, tooth root image and tooth top image that pseudo-color mode is shown carry out respectively number and the calculating of single area.
In step S603, tooth top and tooth root area are added up, ask for respectively the mean value of tooth top area and tooth root area.
In step S604, by setting the mode of area threshold, determine tooth root and tooth top defect area.
The concrete implementation procedure of above-described embodiment is as described below:
Tooth top image and tooth root image for above step obtains behind region labeling method marking image, just can obtain the actual geometric parameter of gear.
The purpose of zone marker is to enclose identical mark to the pixel that links together, and different coordinator is enclosed different marks.Zone marker occupies very important status in binary Images Processing.Join domains each is different by this processing distinguish, and then just the number of the connected region in the image are counted, and can be analyzed each regional geometric parameter.Its concrete steps are:
(1) the scanning bianry image runs into when not having tagged target picture number (looking like in vain number) P, adds a new mark (label);
(2) add identical mark for the picture number of link together with P (being identical coordinator);
(3) add identical mark with the picture number that links together as number of labelling further for all;
(4) until link together all be added mark as number.Such coordinator just has been added identical mark;
(5) turn back to the first step, again search the new tagged picture number that do not have, repeat above-mentioned each step.
(6) show as Figure 12 a shown in the pseudo-colours mode that through the tooth top image behind the mark wherein, each tooth top figure should be marked with color among Figure 12 a, and is unmarked among the figure to distinguish different tooth tops, but not in order to limit the present invention.
(7) calculate number and the area of tooth top according to different mark;
Wherein tooth top number n is the maximal value of mark, and the area of each tooth top is the number of connected region pixel, is formulated as:
n=max(label)
area(i)=find(I==i)
(8) individual tooth top area is added up, its histogram represents that shown in Figure 12 b by finding out among the figure, tooth top number n is 20.
(9) ask for the mean value of tooth top area
Be formulated as:
(10) judge defect area.Judge defect area by setting threshold, judgment rule is:
(11) output analysis result.
Therefore, can calculate in this way the number of teeth of gear, and defect area is found out.
In this embodiment, utilize the same method can be in the hope of the geometric parameter in tooth root zone.
As one embodiment of the present of invention, calculate radius of addendum and root radius after, can determine module, specific as follows:
(1) modulus m' according to a preliminary estimate.Formula is:
m=(r
H+r
L)/n
Wherein, n is the number of teeth.
(2) preliminary selected master gear modulus with relatively approaching.Generally may have 2,3 such standard modules and exist, count, m1, m2, m3 also sorts by size, and is assumed to be m1〉m2〉m3.
(3) with the standard module table in modulus compare, through judging the actual modulus can obtain gear.
By above step, the standard module m of gear just can decide.
Followingly provided a concrete example technical scheme of the embodiment of the invention described:
For the validity of verification method, we test, and the CCD number of pixels that native system adopts is 1280 * 1040, and the image-generating unit area is 4.068mm * 3.686mm.Its experimentation may further comprise the steps:
(1) the K value is demarcated
Adopt a standard gauge block, the measurement to measuring system under the prerequisite that does not change measurement parameter is demarcated than K=L/L0, and wherein L is the computer picture size of this gauge block, represents with the pixel number, and L0 is the standard size of this gauge block, and unit is millimeter.
(2) measure
Part image to be measured enters into demarcates good measuring system, and the picture size that records part is L1 (pixel count).Then the measurement size of this part is L2=L1K.
(3) judge
Standard of comparison size and measurement size, whether the size of judging workpiece within the tolerance permissible range, is judged thereby measured size is carried out the result.
The structured flowchart based on the gear defects detection system of computer vision that Figure 13 shows that the embodiment of the invention provides for convenience of explanation, has only provided the part relevant with the embodiment of the invention among the figure.
Source images acquisition module 11 obtains the source images of the tested gear that image pick-up card collects by the PCI slot; The source images of 12 pairs of tested gears that acquire of pretreatment operation module comprises the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, obtains the gear parameter of tested gear; The gear parameter of 13 pairs of tested gears that obtain of Morphologic Parameters analysis module carries out the Morphologic Parameters analysis, obtains the analysis result that comprises the number of teeth that may have defective.
As a specific embodiment of the present invention, as shown in figure 14, described pretreatment operation module 12 specifically comprises:
24 source color images of the tested gear that greyscale transform process module 121 will collect carry out greyscale transform process, obtain 8 gray scale gear graph pictures; Noise removal module 122 is utilized median filtering algorithm, and described 8 gray scale gear graphs are looked like to carry out noise removal; Threshold segmentation module 123 is utilized Image binarizing algorithm, and the gray scale gear graph is looked like to carry out Threshold segmentation, the gear that obtains separating and background image.
As a specific embodiment of the present invention, as shown in figure 15, described Morphologic Parameters analysis module 13 specifically comprises:
As a specific embodiment of the present invention, as shown in figure 16, described center of circle radius calculation module 132 specifically comprises:
Coordinate record module 1321 is read in the binary image after hole is filled, the coordinate on the record gear; Point is searched module 1322 and is searched the solstics set of the described picture centre of distance and closest approach set at described binary image, obtains the coordinate that can comprise respectively the minimal convex polygon of gear; Drafting module 1323 is drawn out respectively convex polygon according to the coordinate of minimal convex polygon; 1324 pairs of described convex polygons of the Fitting Calculation module carry out respectively the Circular curve fitting of least square method, the central coordinate of circle of gears, root radius and radius of addendum;
As a specific embodiment of the present invention, as shown in figure 17, described tooth root image and tooth top image obtain module 133 and specifically comprise:
Calculate auxiliary circle drafting module 1331 in binary image, draw one take certain numerical value between root radius and radius of addendum as radius and calculate auxiliary circle; Cover plate image setting module 1332 an illiteracy plate image is set, described illiteracy plate image size is identical with gear graph picture size, and the radius of described illiteracy plate image equals half of radius of addendum and root radius sum; Gear graph picture and described illiteracy plate image that the first difference operation module 1333 will be drawn after the calculating auxiliary circle are done difference operation, obtain tooth top figure image; The second difference operation module 1334 looks like to do difference operation with described illiteracy plate image and the gear graph that draw to calculate after the auxiliary circle, obtains tooth root figure image;
As a specific embodiment of the present invention, as shown in figure 18, described defect analysis module 134 specifically comprises:
Above-mentioned only for system embodiment of the present invention, the function of its each module realizes not repeating them here as described in the above-mentioned embodiment of the method, but not in order to limit the present invention.
The embodiment of the invention is based on the gear tooth parameter measurement system of machine vision, it is by the camera collection image, pass in the computing machine through capture card, in conjunction with the powerful data-handling capacity of computing machine, utilize image processing techniques to realize the non-cpntact measurement of gear parameter.Not only measuring speed is fast but also precision is high for the embodiment of the invention, satisfied the functional requirement that the gear conventional parameter is measured, the automaticity that can greatly enhance productivity and produce in the industrial processes in enormous quantities, can satisfy the measuring accuracy requirement in the production reality, and Measuring Time only is several seconds, can satisfy in the commercial measurement the requirement of measuring speed, have larger theory and practical value.
The above only is preferred embodiment of the present invention, not in order to limiting the present invention, all any modifications of doing within the spirit and principles in the present invention, is equal to and replaces and improvement etc., all should be included within protection scope of the present invention.
Claims (9)
1. the gear defects detection method based on computer vision is characterized in that described method comprises the steps:
Obtain the source images of the tested gear that image pick-up card collects by the PCI slot;
The source images of the tested gear that acquires is comprised the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, obtain the gear parameter of tested gear;
Gear parameter to the tested gear that obtains carries out the Morphologic Parameters analysis, obtains the analysis result that comprises the number of teeth that may have defective.
2. the gear defects detection method based on computer vision according to claim 1, it is characterized in that, described source images to the tested gear that acquires comprises the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, and the step that obtains the gear parameter of tested gear specifically comprises the steps:
24 source color images of the tested gear that collects are carried out greyscale transform process, obtain 8 gray scale gear graph pictures;
Utilize median filtering algorithm, described 8 gray scale gear graphs are looked like to carry out noise removal;
Utilize Image binarizing algorithm, the gray scale gear graph is looked like to carry out Threshold segmentation, the gear that obtains separating and background image.
3. the gear defects detection method based on computer vision according to claim 2, it is characterized in that, described gear parameter to the tested gear that obtains carries out the Morphologic Parameters analysis, and the step of obtaining the analysis result that comprises the number of teeth that may have defective specifically comprises the steps:
Utilize filling algorithm, the hole of the gear graph picture that described Binarization methods is obtained is filled;
Binary image after the hole filling as target, is determined to calculate central coordinate of circle, root radius and the radius of addendum of described binary image;
According to central coordinate of circle, root radius and the radius of addendum of the described binary image that calculates, tooth top to be separated with tooth root, detection of gear gear teeth defective obtains tooth root image and tooth top image;
By the region labeling method, tooth root image and the tooth top image that obtains carried out defect analysis, the number of teeth of gears is determined the defect area of described tooth root and tooth top image.
4. the gear defects detection method based on computer vision according to claim 3, it is characterized in that, described binary image after hole is filled is as target, and the step of determining to calculate central coordinate of circle, root radius and the radius of addendum of described binary image specifically comprises the steps:
Read in the binary image after hole is filled, the coordinate on the record gear;
Search the solstics set of the described picture centre of distance and closest approach set at described binary image, obtain the coordinate that can comprise respectively the minimal convex polygon of gear;
According to the coordinate of minimal convex polygon, draw out respectively convex polygon;
Described convex polygon is carried out respectively the Circular curve fitting of least square method, the central coordinate of circle of gears, root radius and radius of addendum.
5. the gear defects detection method based on computer vision according to claim 3, it is characterized in that, the central coordinate of circle of the described binary image that described basis calculates, root radius and radius of addendum, tooth top is separated with tooth root, detection of gear gear teeth defective, the step that obtains tooth root image and tooth top image specifically comprises the steps:
In binary image, draw one take certain numerical value between root radius and radius of addendum as radius and calculate auxiliary circle;
Arrange one and cover the plate image, described illiteracy plate image size is identical with gear graph picture size, and the radius of described illiteracy plate image equals half of radius of addendum and root radius sum;
Gear graph picture and described illiteracy plate image after the picture calculating auxiliary circle are done difference operation, obtain tooth top figure image;
Gear graph after described illiteracy plate image and the picture calculating auxiliary circle is looked like to do difference operation, obtain tooth root figure image.
6. the gear defects detection method based on computer vision according to claim 3, it is characterized in that, described by the region labeling method, tooth root image and the tooth top image that obtains carried out defect analysis, the number of teeth of gears, determine that the step of the defect area of described tooth root and tooth top image specifically comprises:
Respectively tooth root image and the tooth top image that obtains carried out zone marker, obtain tooth root image and tooth top image that pseudo-color mode shows;
Tooth root image and tooth top image that pseudo-color mode is shown carry out respectively number and the calculating of single area;
Tooth top and tooth root area are added up, asked for respectively the mean value of tooth top area and tooth root area;
By setting the mode of area threshold, determine tooth root and tooth top defect area.
7. gear defects detection system based on computer vision is characterized in that described system comprises:
The source images acquisition module is used for obtaining by the PCI slot source images of the tested gear that image pick-up card collects;
The pretreatment operation module is used for the source images of the tested gear that acquires is comprised the pretreatment operation of greyscale transformation, filtering and noise reduction and Threshold segmentation, obtains the gear parameter of tested gear;
The Morphologic Parameters analysis module is used for the gear parameter of the tested gear that obtains is carried out the Morphologic Parameters analysis, obtains the analysis result that comprises the number of teeth that may have defective.
8. the gear defects detection system based on computer vision according to claim 7 is characterized in that, described pretreatment operation module specifically comprises:
The greyscale transform process module, 24 source color images that are used for the tested gear that will collect carry out greyscale transform process, obtain 8 gray scale gear graph pictures;
The noise removal module is used for utilizing median filtering algorithm, and described 8 gray scale gear graphs are looked like to carry out noise removal;
The Threshold segmentation module is used for utilizing Image binarizing algorithm, and the gray scale gear graph is looked like to carry out Threshold segmentation, the gear that obtains separating and background image.
9. the gear defects detection system based on computer vision according to claim 7 is characterized in that, described Morphologic Parameters analysis module specifically comprises:
The hole packing module is used for utilizing filling algorithm, and the hole of the gear graph picture that described Binarization methods is obtained is filled;
Center of circle radius calculation module as target, determines to calculate central coordinate of circle, root radius and the radius of addendum of described binary image for the binary image after hole is filled;
Tooth root image and tooth top image obtain module, are used for central coordinate of circle, root radius and radius of addendum according to the described binary image that calculates, and tooth top is separated with tooth root, and detection of gear gear teeth defective obtains tooth root image and tooth top image;
The defect analysis module is used for by the region labeling method tooth root image and the tooth top image that obtains being carried out defect analysis, and the number of teeth of gears is determined the defect area of described tooth root and tooth top image;
Described center of circle radius calculation module specifically comprises:
The coordinate record module is used for reading in the binary image after hole is filled, the coordinate on the record gear;
Point is searched module, is used for searching the solstics set of the described picture centre of distance and closest approach set at described binary image, obtains the coordinate that can comprise respectively the minimal convex polygon of gear;
Drafting module is used for the coordinate according to minimal convex polygon, draws out respectively convex polygon;
The Fitting Calculation module is for the Circular curve fitting that described convex polygon is carried out respectively least square method, the central coordinate of circle of gears, root radius and radius of addendum;
Described tooth root image and tooth top image obtain module and specifically comprise:
Calculate the auxiliary circle drafting module, be used at binary image, draw one take certain numerical value between root radius and radius of addendum as radius and calculate auxiliary circle;
Cover plate image setting module, be used for arranging one and cover the plate image, described illiteracy plate image size is identical with gear graph picture size, and the radius of described illiteracy plate image equals half of radius of addendum and root radius sum;
The first difference operation module, the gear graph picture and the described illiteracy plate image that are used for drawing after the calculating auxiliary circle are done difference operation, obtain tooth top figure image;
The second difference operation module is used for described illiteracy plate image and the gear graph after drawing the calculating auxiliary circle looks like to do difference operation, obtains tooth root figure image;
Described defect analysis module specifically comprises:
The zone marker module is used for respectively tooth root image and the tooth top image that obtains being carried out zone marker, obtains tooth root image and tooth top image that pseudo-color mode shows;
Computing module is used for the tooth root image that pseudo-color mode is shown and the tooth top image carries out respectively number and single area calculates;
Statistical module is used for tooth top and tooth root area are added up, and asks for respectively the mean value of tooth top area and tooth root area;
Determination module is used for determining tooth root and tooth top defect area by setting the mode of area threshold.
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