CN109815822A - Inspection figure components target identification method based on Generalized Hough Transform - Google Patents
Inspection figure components target identification method based on Generalized Hough Transform Download PDFInfo
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Abstract
The present invention relates to a kind of inspection figure components target identification method based on Generalized Hough Transform, comprising: step A: choosing the reference point in inspection, after the completion of selection, positions to each reference point；Step B: after positioning to reference point, R-table matrix is established according to reference point number and distance；Step C: the space Hough is established according to the R-table matrix；Step D: maximal peak point is chosen in the space Hough as optimal reference point, forms reference picture；Step E: finding reference coordinate in inspection figure using Generalized Hough Transform method, image is extracted from inspection figure according to reference coordinate, and carry out mutual information calculating using the image and reference picture extracted, to judge whether part lacks.By the present invention in that being identified with Generalized Hough Transform to the part in inspection figure, quickly can accurately it be judged whether part in inspection figure lacks, to realize unmanned plane during inspection to the real-time detection of equipment.
Description
Technical field
The present invention relates to image identification technical field more particularly to zero, a kind of inspection figure based on Generalized Hough Transform
Part target identification method.
Background technique
With the fast development of computer technology, target identification technology has rapidly developed into a kind of very important
Tool and means, application range are also increasingly wider.However since the related components such as nut handwheel target is by environment, shooting
Angle and own situation influence, and are generally difficult to be expressed with analytic expression, so that being identified to it as a unusual difficult task.It arrives
So far, researcher is directed to nut handwheel related components target identification, primarily directed to ideally, front shooting
Nut handwheel related components are identified that main method to be applied is that round Hough transform detects nut handwheel correlation zero
Part inner circle, color segmentation go out components position.For the nut on the big machinery of the real air to surface shooting of unmanned plane inspection
The identification of handwheel related components, it is fewer and fewer, and also the relevant judgement of components missing is also seldom.
China Patent Publication No.: CN103635169A discloses a kind of defect detecting system, comprising: image processing unit,
Defect detection unit and image-display units, wherein the image processing unit is configured to obtain the form of absorbent commodity
Image, the morphological image of the absorbent commodity show the shape after the absorbent commodity processing in each step of multiple steps
State, the defect detection unit are configured to detect the absorbability after processing based on the morphological image obtained by image processing unit
Article whether there is rejected region, which, which is configured to work as, detects absorbent commodity by defect detection unit
The image of absorbent commodity after showing processing when rejected region.It can be seen that the detection system has the following problems:
First, the detection system is only applied to assembly line, and by the position of fixed product, whether detection components quality
Meet regulation, can not accomplish accurate detection outdoors；
Second, the detection system is used only camera and carries out Image Acquisition to the fixed part in placement position, when product is put
When putting uneven or product space and changing, the image of actual acquisition can not be registrated with benchmark image, detection effect is not
It is ideal；
Third, the detection method can not be handled the edge of image, cause it that can not carry out to the shape of part
Precisely judgement；
4th, the detection system only determines part by the comparison of morphological image when being detected for defect
Whether defect or loss are occurred, and testing result is not accurate.
Summary of the invention
For this purpose, the present invention provides a kind of inspection figure components target identification method based on Generalized Hough Transform, to gram
The problem of clothes can not carry out accurate real-time detection outdoors in the prior art.
Compared with prior art, the beneficial effects of the present invention are by the present invention in that with Generalized Hough Transform to inspection
Part in figure is identified, quickly can accurately be judged whether part in inspection figure lacks, to realize nobody
Machine is during inspection to the real-time detection of equipment.
Further, the method for the invention can be chosen inside the edge image recognized when choosing reference point
Point also can choose the point of non-edge image, by increasing the selection range of reference point, increase the inspection of the method for the invention
Survey range.
In particular, the method is as a reference point by the coordinate for choosing template picture top left corner pixel, this method can be made
For detecting the part and part that is affected by environment and changing shape of irregular shape, the method is further improved
Recognition efficiency.
Further, the method for the invention makes the space of its space size Yu inspection picture when establishing the space Hough
Size is identical, in this way, it is subsequent image is compared when, directly image can be compared, saved in contrast images
When adjustment of image size time, to improve the service efficiency of the method.
Further, when choosing reference coordinate in inspection image, the coordinate of template upper left angle point is chosen, is selected by unified
The position of coordinate is taken, so that the relative position of unified each image, improves the service efficiency of the method.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow diagrams of the inspection figure components target identification method of Generalized Hough Transform；
Fig. 2 is the registration figure after the embodiment of the present invention is registrated live inspection figure and reference map；
Fig. 3 is that the embodiment of the present invention schemes the edge graph after progress edge extracting to registration；
Fig. 4 is the Objective extraction figure that the embodiment of the present invention extracts target part in specified region.
Specific embodiment
In order to which objects and advantages of the present invention are more clearly understood, the present invention is further retouched below with reference to embodiment
It states；It should be appreciated that specific embodiment described herein is used only for explaining the present invention, it is not intended to limit the present invention.
Below in conjunction with attached drawing, the forgoing and additional technical features and advantages are described in more detail.
Refering to Figure 1, it is the present invention is based on the inspection figure components target identification methods of Generalized Hough Transform
Flow diagram, comprising:
Step A: choosing the reference point in inspection, after the completion of selection, positions to each reference point；
Step B: after positioning to reference point, R-table matrix is established according to reference point number and distance；
Step C: the space Hough is established according to the R-table matrix；
Step D: maximal peak point is chosen in the space Hough as optimal reference point, forms reference picture；
Step E: finding reference coordinate in inspection figure using Generalized Hough Transform method, according to reference coordinate from inspection figure
Middle extraction image, and mutual information calculating is carried out using the image and reference picture extracted, to judge whether part lacks.
Specifically, the selection of reference point can be any point in template image inward flange image in the step A,
Position including non-edge point is typically chosen the central point of edge image.Due to the edge of nut handwheel related components, because
Environment influences, and the shape that detected is usually and irregular, so the coordinate for finally choosing template picture top left corner pixel is ginseng
Examination point.
Specifically, the method for establishing the space Hough in the step C are as follows: for each side in inspection edge picture
Edge point (xi,yi), the corresponding discrete value k of gradient, and k ∈ [1,30] are calculated by above-mentioned rule, for each in template edge picture
A marginal point fallen into k will calculate the corresponding reference coordinate of marginal point met in inspection edge picture according to R-table
(x'c,y'c), establish the space Hough H.
Specifically, in the space Hough, selection peak-peak point methods include: in the step D
Step E1: the selected point X in edge image in inspection figurer=(xr,yr), in edge image boundary selected point X=
(x, y) calculates point X at this timerPhasor difference r:r=X between Xr-X。
Step E2: set the angle of r and x-axis asPoint XrDistance to boundary point X is r, and is orientated to the boundary of boundary point X
θ is calculated:
The possible value range of θ is divided into discrete k kind state i Δ θ, i=1,2,3...k and is denoted as θk=k Δ θ, wherein Δ θ
To define θkDirectioin parameter angle step；
Step E3: the reference point locations opening relationships look-up table R determining to all convenient points, if on a zone boundary
Point (x, y) all has parameter r θk, then r θkFor the shape feature of the boundary curve；
Step E4: being respectively calculated each point on zone boundary, finds out { A (xr,yr) in maximum value r*:
Specifically, when choosing reference coordinate in inspection image, correspondence mappings are Prototype drawing in the step E
The coordinate of the upper left angle point of picture.
Embodiment 1
The present embodiment can be detected to whether the handwheel components in field device lack, including three parts: be based on
SURF feature is become to being registrated of inspection figure and reference map, the registration figure edge extracting based on Canny operator and based on broad sense Hough
The components target identification changed；When being detected to field device, first with SURF algorithm to collected inspection figure and base
Quasi- figure is registrated, and is extracted after the completion of registration using edge of the Canny operator to registration figure, to export edge image；It is defeated
Using Generalized Hough Transform to edge image and template image progress mutual information calculating after the completion of out, and according to calculating
The size of association relationship can determine whether to lack.It is understood that detection method described in the present embodiment cannot be only used for pair
Whether handwheel lacks and is judged in equipment, can also in image nut or other parts whether lack and judge, as long as
Its specified working condition can be reached by meeting the present embodiment each section.
Specifically, first part is registrated inspection figure with reference map by SURF feature in the present embodiment, including
Euclidean distance sequence between SURF feature extraction, characteristic point is calculated with the screening of directrix Euclidean distance and affine matrix, comprising:
Step 1.1: to image carry out SURF feature extraction, comprising establish integral image, construction approximation Hessian matrix,
Tectonic scale space and precise positioning feature point；
Step 1.2: the feature extracted using SURF algorithm, SURF operator have scale, rotational invariance, and right
There is very strong robustness in picture noise, light variation, affine deformation etc., so for identical object in inspection figure and reference map
Body, the Feature Descriptor extracted be very close to, with distance metric.SURF feature is carried out to inspection figure and reference map
After extraction, use Euclidean distance method as similarity measurement；
Step 1.3: making inspection figure and reference map parallel arranged, demarcate matching double points in inspection figure and reference map respectively
Position and its corresponding connection straight line carry out the degree of registration linear distance according to the matching double points of characteristic point Euclidean distance obtained
Amount because inspection figure and reference map are shot using same camera, the format and size for the photo shot be it is the same,
Measure still selects Euclidean distance method, with the programmed screening for carrying out registration point pair near directrix average value；
Step 1.4: the matching double points selected to finishing screen calculate affine change according to the position of each in the picture
Matrix is changed, the registration by inspection figure to reference map is finally realized using the matrix；
By taking the screening for matching directrix Euclidean distance method to carry out registration pair, and the calculating for carrying out affine matrix is used to
Registration, although as shown in Fig. 2, result is not high without the registration accuracy in the case of missing, be also able to achieve generally without mistake
Really match alignment request.
Specifically, second part of the present invention is extracted by edge of the Canny operator to image, it include Gauss filter
The smooth input picture of wave device, gradient magnitude image and the calculating of angular image, the non-maximum restraining of gradient magnitude image, dual threshold
Processing and linking parsing, comprising:
Step 2.1: Gaussian filter smoothing processing inspection figure and Prototype drawing are utilized, due in Canny detection process, having pair
The derivative calculations process of image, and the calculated result of derivative does not have robustness for noise, it is very sensitive to noise, so wanting
It is smoothed, just not will cause the amplification of noise figure in this way, noise is more, and imaginary point can be such that false edge becomes with regard to more
More, adverse effect will cause to the extraction at edge, but smothing filtering and edge detection are conflicting both sides, because flat
Although sliding filtering can effectively inhibit noise jamming, also the edge of image can be made to thicken, this side after allowing for
There is uncertainty in edge positioning operation, according to many practical engineering experiences of forefathers as a result, Gaussian filter can be with
It accurately detects to position on noise remove and side and a preferable half-way house is provided between the two paradox；
Step 2.2: the edge of image is extracted, it is slower in the transformation along the pixel value in edge direction,
And perpendicular in the normal direction of the edge direction pixel value variation just more acutely because generally related to object it
Between, the color change between scene, between region etc., differential operator, which provides one kind to calculate the variation on this edge, to be had
The method of power in Practical Project, the detection at edge is carried out with the single order of image or second dervative, it is determined whether have marginal point can
Whether on the slope with the method for first derivative, that is, to judge this point, then, Second Derivative Methods can be used for light and shade judgement,
Namely judge an edge pixel point belong to it is bright on one side or it is dark while the single order local derviation ,ed using image, i.e., it is limited
The determination of difference progress image gradient amplitude and direction；
Step 2.3: the inhibition of non-maximum, above-mentioned processing are carried out to each partial gradient region in global gradient map
Obtained global gradient is not meant to real edge, at this time in order to determine edge it is necessary to each in global gradient map
A partial gradient region carries out the inhibition of non-maximum, thus retain the maximum point of partial gradient, the point in image, corresponding figure
As the value in gradient magnitude matrix is bigger, illustrate that its gradient value is bigger, this belongs to one of image enchancing method, can not table
Show that this point is exactly marginal point, non-maxima suppression is the pith in Canny edge detection method, is briefly exactly to find
Local maximum in gradient image retains the pixel value of the point in corresponding original image, and non-maximum point is corresponding
The gray value of point set 0；
Step 2.4: handled using threshold method to reduce pseudo-edge point, the proposition of threshold method is in order to further
Extract true marginal point, if only using a threshold value, lower than the value point value all can zero setting, threshold value is too low at this time
Pseudo-edge just will appear, and excessively high actual marginal point can be deleted accidentally, in order to improve this case, use two threshold values of height；
Step 2.5: after threshold process, forming longer edge line and being attached property is then needed to analyze, look in the picture
Weak pixels all in image are connected to the point, are formed most by the edge pixel point also not visited to one using 8 connectivity
Whole edge image.
By Canny operator to the edge extracting of picture after registration as shown in figure 3, can be obtained according to Fig. 3, by using
Image edge after Canny operator extraction is also apparent.
Part III of the present invention be the components target identification based on Generalized Hough Transform, including reference point locations selection,
R-table is established, the space Hough is established, peak value positioning and mutual information calculate, comprising:
Step 3.1: the selection of reference point, reference point can be any point in template edge image, including non-edge
The position of point, is typically chosen the central point of template edge image, since the edge of handwheel part is under the influence of environment, detected
Shape it is usually and irregular, so the coordinate for finally choosing template picture top left corner pixel is reference point；
Step 3.2: with template edge image total edge points and distance reference amount, establishing R-table matrix；
Step 3.3: for each marginal point in inspection edge picture, it is corresponding discrete to calculate gradient by above-mentioned rule
Value, for each in template edge picture fall into marginal point, will according to R-table calculate inspection edge picture in accord with
The corresponding reference coordinate of the marginal point of conjunction, establishes the space Hough, and space size is identical as inspection picture space size；
Step 3.4: finding the maximal peak point in the space Hough, that is, search out optimal reference point.
Step 3.5: utilizing Generalized Hough Transform method, reference coordinate is found in inspection image, correspondence mappings are template
The coordinate of upper left angle point the picture of template size, and and template image are extracted in inspection image with the reference coordinate position
Mutual information calculating is done, judges whether part lacks according to the size of the association relationship calculated.
In order to verify the reliability of this part method, the present embodiment to reference map carry out template extraction and first to reference map into
Row identification is identified very accurate handwheel recognition result after registration using Generalized Hough Transform method using obtained template
As shown in figure 4, can be obtained according to Fig. 4, this part method is also very accurate to the identification of target in the picture.When handwheel missing, benefit
The extracted region of template size is carried out with the location information that Generalized Hough Transform obtains, and does mutual information calculating with template picture,
It may determine that whether handwheel lacks.
So far, it has been combined preferred embodiment shown in the drawings and describes technical solution of the present invention, still, this field
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this
Under the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to the relevant technologies feature, these
Technical solution after change or replacement will fall within the scope of protection of the present invention.
The above description is only a preferred embodiment of the present invention, is not intended to restrict the invention；For those skilled in the art
For member, the invention may be variously modified and varied.All within the spirits and principles of the present invention, it is made it is any modification,
Equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (7)
1. a kind of inspection figure components target identification method based on Generalized Hough Transform, which is characterized in that by broad sense Hough
Changing image recognition methods is applied to equipment routing inspection figure to be measured in real time, comprising:
Step A: choosing the reference point in inspection, after the completion of selection, positions to each reference point；
Step B: after positioning to reference point, R-table matrix is established according to reference point number and distance；
Step C: the space Hough is established according to the R-table matrix；
Step D: maximal peak point is chosen in the space Hough as optimal reference point, forms reference picture；
Step E: finding reference coordinate in inspection figure using Generalized Hough Transform method, is mentioned from inspection figure according to reference coordinate
Image is taken, and carries out mutual information calculating using the image and reference picture extracted, to judge whether part lacks.
2. the inspection figure components target identification method according to claim 1 based on Generalized Hough Transform, feature exist
In the selection of reference point includes the point in the point and non-edge image in edge image in the step A.
3. the inspection figure components target identification method according to claim 2 based on Generalized Hough Transform, feature exist
In selection template picture top left corner pixel coordinate is as a reference point in the step A, to detect zero of irregular shape
Part.
4. the inspection figure components target identification method according to claim 1 based on Generalized Hough Transform, feature exist
In establishing the method in the space Hough in the step C are as follows: for each marginal point (x in inspection edge picturei,yi), by upper
It states rule and calculates the corresponding discrete value k of gradient, and k ∈ [1,30], the edge in k is fallen into for each in template edge picture
Point will calculate the corresponding reference coordinate (x' of marginal point met in inspection edge picture according to R-tablec,y'c), it establishes
The space Hough H.
5. the inspection figure components target identification method according to claim 4 based on Generalized Hough Transform, feature exist
In the size of the space the Hough H is identical as inspection picture space size, is compared by unified each picture size with reducing
The time spent in journey.
6. the inspection figure components target identification method according to claim 1 based on Generalized Hough Transform, feature exist
In choosing peak-peak point methods in the space Hough in the step D includes:
Step E1: the selected point X in edge image in inspection figurer=(xr,yr), edge image boundary selected point X=(x,
Y), point X is calculated at this timerPhasor difference r:r=X between Xr-X。
Step E2: set the angle of r and x-axis asPoint XrTo boundary point X distance be r, and to the boundary of boundary point X be orientated θ into
Row calculates:
The possible value range of θ is divided into discrete k kind state i Δ θ, i=1,2,3...k and is denoted as θk=k Δ θ, wherein Δ θ is fixed
Adopted θkDirectioin parameter angle step；
Step E3: the reference point locations opening relationships look-up table R determining to all convenient points, if the point on a zone boundary
(x, y) all has parameter r θk, then r θkFor the shape feature of the boundary curve；
Step E4: being respectively calculated each point on zone boundary, finds out { A (xr,yr) in maximum value r*:
7. the inspection figure components target identification method according to claim 1 based on Generalized Hough Transform, feature exist
In in the step E, when choosing reference coordinate in inspection image, correspondence mappings are the upper left angle point of template image
Coordinate.
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| CN113269767A (en) * | 2021-06-07 | 2021-08-17 | 中电科机器人有限公司 | Batch part feature detection method, system, medium and equipment based on machine vision |
| WO2023124233A1 (en) * | 2021-12-31 | 2023-07-06 | 杭州海康机器人股份有限公司 | Circle searching method and apparatus, electronic device and storage medium |
| CN114549438A (en) * | 2022-02-10 | 2022-05-27 | 浙江大华技术股份有限公司 | Reaction kettle buckle detection method and related device |
| CN116051629A (en) * | 2023-02-22 | 2023-05-02 | 常熟理工学院 | Autonomous navigation robot-oriented high-precision visual positioning method |
| CN116051629B (en) * | 2023-02-22 | 2023-11-07 | 常熟理工学院 | Autonomous navigation robot-oriented high-precision visual positioning method |
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