CN109886265A - A detection method of door limiter based on Adaboost and template matching - Google Patents

A detection method of door limiter based on Adaboost and template matching Download PDF

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CN109886265A
CN109886265A CN201910027016.4A CN201910027016A CN109886265A CN 109886265 A CN109886265 A CN 109886265A CN 201910027016 A CN201910027016 A CN 201910027016A CN 109886265 A CN109886265 A CN 109886265A
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CN109886265B (en
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刘峰
余义
干宗良
崔子冠
唐贵进
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Nanjing Post and Telecommunication University
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Abstract

The car door limiter detection method based on Adaboost and template matching that the present invention provides a kind of, the nut feature treated in the picture of measuring car door stop including the use of Adaboost classifier are identified and are positioned；According to the positional relationship between the top nut and character feature of the picture of the positioning result of nut feature and car door limiter to be measured in the picture of car door limiter to be measured, the character zone tentatively treated on measuring car door stop picture carries out coarse positioning；Make the optimal Template library of character using double iterative legal system, while acquiring the optimal threshold of template matching；The edge feature for extracting car door limiter picture and template to be measured identifies these characters using improved fast Template Matching method, completes several steps of car door limiter detection and identification to be measured；Improve the detection speed and accuracy of car door limiter detection.

Description

A kind of car door limiter detection method based on Adaboost and template matching
Technical field
The invention belongs to industrial part detection technique fields, and in particular to a kind of vehicle based on Adaboost and template matching Door stop detection method.
Background technique
Traditional industrial part detection is to observe by artificial eye to judge whether part mistake occurs with recognition methods Situations such as, the short time, interior this perhaps can have great accuracy, but with the large-scale production of modern factories, for a long time Working, lower human eye is so inevitable that generate fatigue, so that accuracy declines to a great extent, oneself cannot be met current industry Production requirement, and the technologies such as computer technology, artificial intelligence and pattern-recognition are greatly developed and are answered extensively in recent years With, these technologies have also penetrated into industrial production industry, in this background, industrial part detection and knowledge based on machine vision Other technology is come into being.
Currently, both at home and abroad based on machine vision industrial part detection mainly with based on template matching recognition methods and base In statistical model recognition methods based on.Template matching method is also referred to as nearest neighbour method, is most important side in pattern-recognition nonparametric Method and most common method, are widely used in character machining and recognizer, it has, and algorithm is simple, classification is intuitive, it is fast to calculate Speed, should be readily appreciated that with realize the characteristics of.The method of template matching can substantially be divided into three classes: the first kind is based on gray level image Template matching method, the second class are the template matching methods based on feature, and third class is based on to image understanding and explained Method of completing the square.The outer research emphasis to template matching of Current Domestic mainly in matched speed and precision, has been studied more Method also concentrates on preceding two classes matching process.Template matching method of the first kind based on gray level image be using piece image as The process of the corresponding position of template and the method search pattern by comparing pixel-by-pixel on another piece image.Since template provides Image information it is more complete, the template matching algorithm based on gray level image can be than the template matching algorithm based on feature preferably Adapt to the unfavorable factors such as weak feature, picture noise and image blur.But for some realtime graphic matching tasks or presence The heterologous images match task of critical noisy and tonal distortion interference, often there is also operational efficiency for existing template matching algorithm Slowly, the problems such as reliability is insufficient when not pre-processing.Therefore research calculating speed is fast, template matching algorithm of good reliability has Very important meaning.Template matching of second class based on feature includes the template of global invariant features and local invariant feature Match.Due to global invariant features be not easy obtain and indistinguishable foreground and background, using global invariant features algorithm very It is few to be used.The local invariant feature of image refers to the visual angle change in image, rotationally-varying, change of scale, illumination variation, figure As the local feature remained unchanged under the various transformation such as fuzzy and compression of images.Based on the images match of local invariant feature It calculates complexity to decrease compared with the relevant algorithm of gray scale is based on, anti-noise anti-interference ability is stronger.Currently based on the mould of local feature Plate matching algorithm is widely used with its higher robustness, which mainly includes that feature detection and feature describe two sides Face.Widely used method has an affine method of Harris in feature detection field at present, the affine method of Hessian, MSER method, DOG method, IBR method, EBR method etc..The more than comprehensive analysis performance of several feature detection algorithms, followed by feature describes, and feature is detected Obtained region describes son using local invariant feature and carries out feature description.Wherein most classic local invariant feature description Have SIFT and DAISY description, and MROGH, MRRID, LIOP and HRI-CSLTP describe son be Recent study it is the most popular and Description of greatest concern.Third class needs to establish sample database based on the recognizer of statistical model, then by a series of Method extracts sample characteristics, finally using the method training classifier of machine learning, is finally completed identification process.The algorithm is compared It is that there is good robustness in the advantage of template matching algorithm.
Summary of the invention
To solve above-mentioned the technical problems existing in the prior art, the present invention provides one kind to be based on Adaboost and template Matched car door limiter detection method, this method are different from the template matching method in traditional industry piece test, but first Obtained classifier is trained to car door limiter picture, recycling Adaboost is acquired after car door limiter illumination to light source with preceding Nut on car door limiter is positioned and identified, the positional relationship pair of car door limiter top nut and character is then utilized Character feature carries out coarse positioning on car door limiter, and the edge of character feature on car door limiter is extracted on the basis of coarse positioning Information finally identifies car door limiter type using improved fast Template Matching method, corresponding method very good solution because There is hollow character on car door limiter and is difficult to detect character information to be difficult to the problem of judging car door limiter type.
The present invention solves its technical problem and is achieved through the following technical solutions:
A kind of car door limiter detection method based on Adaboost and template matching, comprising the following steps:
Step S1, picture of the acquisition to car door limiter, using Adaboost classifier to the car door limiter figure of acquisition Nut in piece is identified and is positioned；
Step S2, the recognition result of nut, combined standard number in the car door limiter picture obtained according to the step S1 According to the positional relationship between character on car door limiter top nut in library and car door limiter, the word on measuring car door stop is treated It accords with region and carries out coarse positioning；
Step S3, make the optimal Template library of character on car door limiter using double iterative legal system, while acquiring template Optimal threshold with character on car door limiter；
Step S4, after completing step S3, the character used in the picture and optimal Template library of car door limiter to be measured is extracted The edge feature of template identifies the character in car door limiter picture to be measured using fast Template Matching method, completes to measuring car The detection and identification of door stop.
Further, in the step S1, the preparation process of the Adaboost classifier is to prepare needed for training first Positive and negative samples, the quantitative proportions of the positive and negative samples is 1: 2-1: 4, and wherein positive sample is the spiral shell on car door limiter to be measured Female front picture, negative sample is not by taking the picture of nut in the nut reverse side picture and production process of car door limiter to be measured And only take picture two parts composition of partial nut, the vector description file of the required positive and negative samples of creation training；Again By OpenCV open source Cooley several Weak Classifiers of local binary patterns feature training, and forms the Adaboost and classify by force Device.
Further, in the step S3, make the optimal Template library of character on car door limiter using double iterative legal system And the calculation method of the optimal threshold of template matching character, specifically includes the following steps:
Step S3.1, the total L of i-th kind of character picture as much as possible composition pictures Pic are first obtainedi, to every picture, Utilize the method and pictures Pic of template matchingiIn other pictures match one by one, obtained L-1 similarity be added after Arrive the sum of similarity, it can obtain the sum of L similarity { sim1, sim2, sim3..., simL}；
Step S3.2, { sim in step S3.1 is taken1, sim2, sim3..., simLIn the smallest preceding Num of the sum of similarityspe A picture forms pictures spe, the maximum preceding Num of the sum of similaritygenA picture forms pictures gen, by picture set spe The original template library Temp of corresponding model car door limiter is merged into geni；
Step S3.3, the original template library Temp obtained respectively with the step S3.2iIn all templates and pictures PiciIn each picture do template matching, due to TempiIn have Numgen+NumspeA template, so PiciIn each figure Num all can be obtained in piecegen+NumspeA similarity value, taking its maximum value is template library TempiWith pictures PiciThe phase of the picture Like confidence level, then PiciL similar confidence levels can be obtained in middle L picture, form confidence level setThis confidence level indicates PiciCharacter on middle L picture is that the confidence level of i-th kind of character is big It is small；
Step S3.4, using the step S3.1 and step S3.2 method, the mould of character on other vehicle limiters is obtained Plate library Tempj(j ≠ i and j=1,2,3 ...), by TempjInstead of the Temp in the step S3.3iThe side of step S3.3 is pressed afterwards Method and pictures PiciIn all pictures do template matching and obtain pictures PiciIn each character picture be jth kind character Confidence level set
If the template library Temp of step S3.5, the described step S3.2iAnd Tempi(j ≠ i and j=1,2,3 ...) can be incited somebody to action Pictures PiciIn each picture be all identified as correct character types, i.e. i-th kind of character, then confidence level setMinimum value be greater than set(j ≠ i and j=1,2,3 ...) Maximum value, further set pictures PiciMatching error rateWherein NumWrongiFor pictures PiciIn be identified as the number of pictures of non-i-th kind of character, i.e. confidence level setIn be less than template matching threshold TiElement number, NumWrongjFor character pictures PiciIn be identified as the number of pictures of jth (j ≠ i and j=1,2,3 ...) kind character, i.e. confidence level setIt is greater than the matching threshold T of template in (j ≠ i and j=1,2,3 ...)iElement number, seek Optimal Template library TempiProcess be exactly to seek optimal threshold TiWith optimum N umgen、NumspeSo that matching error rate WiIt is the smallest Process；
Step S3.6, Num in first fixing step S3.5gen、NumspeValue, seek threshold value TiSo that template library Tempi? With error rateMinimum, finally in the hope of threshold value TiThe midpoint for the segment being in As optimal threshold, after acquiring the optimal threshold, if template library Temp at this timeiMatching error rate WiEqual to zero or it is less than 0.0001, then Numgen、NumspeIt is regarded as optimal, then template library completes, optimal threshold TiAlso it has acquired, otherwise Fixed threshold TiIf confidence level setIn be less than optimal threshold TiConfidence level number be greater than 0.2*L, then the generality of pattern of descriptive parts is not enough, and increases NumgenAfter continue calculate matching error rate WiIf confidence level setIn be less than optimal threshold TiConfidence level number be less than 0.02*L, the then particularity of pattern of descriptive parts It is not enough, increases NumspeAfter continue calculate matching error rate WiIf matching error rate W at this timeiEqual to zero or less than 0.0001, Then Numgen、NumspeIt is regarded as optimal, then template library completes, optimal threshold TiAlso it has acquired, has otherwise repeated this Step is until WiEqual to zero or less than 0.0001；
Step S3.7, the final Num acquired according to the step S3.6gen、Numspe, to i-th kind of character in step S3.1 Picture set Pici, the sum of similarity { sim1, sim2, sim3..., simLIn the smallest preceding NumspeA character picture composition figure Piece collection spe, the maximum preceding Num of the sum of similaritygenA character picture forms pictures gen, and picture set spe and gen are merged At Tempi, TempiThe template library of i.e. finally formed i-th kind of character, and the T that the step S3.6 is finally acquirediAs i-th kind Character is used for the optimal threshold of template matching.
Further, the step S4, specifically includes the following steps:
Step S4.1, the position of car door limiter top nut is obtained according to step S3, further according to car door limiter top nut The position of character on car door limiter is obtained with the positional relationship of character, and character region is intercepted out；
Step S4.2, the gradient map of calculating character region picture extracts marginal information using dual-threshold voltage；
Step S4.3, setting the character number on car door limiter to be measured has numofcha, on all car door limiters Character type shares numofkind kind, by the method for the template library step S4.2 of the obtained numofkind kind character of step S3 Edge is extracted, the character zone gradient map then obtained respectively with step S4.2 does template matching, selects template library matching result In recognition result of the most suitable numofcha character as car door limiter model to be measured.
Preferably, the vector description file of training positive and negative samples, specific steps are created are as follows:
Training sample is divided into positive and negative samples, and in the training process, the quantitative proportion of positive and negative samples is 1: 2-1: 4, described Negative sample is not by taking the picture of nut and only shooting in the nut reverse side picture and production process of car door limiter to be measured Picture two parts to partial nut are constituted, and are guaranteed the character pair picture on other model car door limiters and produced The ratio for the wrong picture being likely to occur in journey is 2: 1-4: 1, and the format of all pictures is converted to bmp format, and by institute There is the dimension of picture of positive sample to turn to long 65 pixels, the size of wide 65 pixel.
Further, detailed process is as follows by the step S4.2:
With the gradient of Sobel operator calculating character region picture, the gradient of each pixel on character zone picture is obtained Value and direction, set two threshold θs1And θ2, gradient value is less than θ1The gradient value of pixel be set to zero, gradient value is greater than θ2Pixel gradient value retain, when gradient value is between θ1And θ2Pixel around gradient value existing for 80 connected regions it is big In θ1The number of pixel be more than 24, i.e., gradient value is greater than θ around corresponding points1Pixel density be greater than 0.3, then protect Gradient value is stayed, gradient value is also otherwise set to zero.
Further, the step S4.3, detailed process is as follows:
If the first character of car door limiter to be measured has several possibility, by the gradient map in several Character mother plate library point Template matching, which is not done, when i-th Prototype drawing is matched with picture to be measured with car door limiter character zone gradient map to be measured uses mould Plate figure is matched on picture to be measured with the continuous sliding window of step-length 1, in j-th of window, by the preceding r edge pixel point of template Do that corresponding that corresponding templates are calculated is similar on this position of picture to be measured to the pixel of corresponding position on picture to be measured Angle value, calculation formula are
WhereinIndicate the preceding r edge pixel of i-th template template in j-th of window on picture to be measured Point does the corresponding similarity value being calculated to the pixel of corresponding position on picture to be measured,It is the m of i-th template The transverse gradients value size of a edge pixel point,Be m-th of edge pixel point of i-th template longitudinal gradient value it is big It is small, PxmIt is the transverse gradients value size of corresponding position pixel on picture to be measured, PymIt is corresponding position pixel on picture to be measured Longitudinal gradient value size；R iteration since 1, if the preceding r side that i-th Prototype drawing obtains on j-th of window of picture to be measured The similarity value of edge pixelMeet predetermined condition, then r=r+1, continues to calculateOtherwise it abandons described Position to be measured, then i-th Prototype drawing is slided into+1 window of picture jth to be measured and continues to calculate since r=1 The required predetermined condition that meets is
Wherein T is the optimal threshold for the template matching that the step S3 is obtained,It is exactly for j-th of window of picture to be measured The minimum of character representated by i-th template can confidence level, be initialized as T, i.e., Continuous iteration generates, Numofpix is the edge pixel number sum of i-th template, and rmax is the last of a upper match window or a upper template One match window is actually added into matched template edge total number of pixels, and jmax is the last match window sequence of a upper template Number；To first character, every kind of possible Character mother plate library can all obtain a final matching similarity, take these similarities Middle maximum value, if it is greater than T, as the recognition result of this character, otherwise it is assumed that without matching result, on car door limiter Each character is handled one by one, to obtain the recognition result of all characters on car door limiter.
Further, the threshold θ1、θ2Calculation method be θ when original state1=50, θ2=200, utilize described pair After threshold method extracts marginal information, the number numofpixel of edge pixel point is calculated, it is assumed that the pixel of character zone picture is total Number is S, then extracts the edge pixel dot density after marginal information in picture and beIf ρ < 0.03, portion Divide the gradient value zero setting of edge pixel point, it is corresponding to reduce θ2If ρ > 0.1, non-edge pixels point in part is identified for edge picture Vegetarian refreshments, so corresponding improve θ2, until 0.03≤ρ≤0.1, θ at this time1And θ2As final θ1And θ2。
Compared with prior art, the invention has the benefit that
(1) present invention most probably goes out in the actual productions such as shooting angle variation because single mode plate is difficult to solve illumination variation Existing problem, therefore the method for use template library carries out template matching, and most based on a kind of utilization double iterative production developed The method of good template library, while the optimal threshold of template matching is acquired, so that car door limiter detection accuracy gets a promotion.
(2) while treating measuring car door stop picture using template library and carry out template matching, program runtime can be with The quantity increase of template linearly increase, therefore method provided by the invention makes matching speed obtain great promotion, from And car door limiter detection speed is got a promotion.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the flow chart in step S3 production optimal Template library of the invention；
Fig. 3 is the flow chart of the template matching of step S4 of the invention.
Specific embodiment
Below by specific embodiment, the invention will be further described, and it is not limit that following embodiment, which is descriptive, Qualitatively, this does not limit the scope of protection of the present invention.
As shown in Figure 1, the car door limiter detection method of the invention based on Adaboost and template matching, it will be with forward direction Light source plays the collected gray scale picture comprising car door limiter of the mode shone as process object, and main includes to utilize Adaboost classifier obtains the exact position of car door limiter top nut and carries out coarse positioning, production character mould to character feature Plate library and character is carried out template matching and to identify this three big main process.Its specific embodiment is as follows:
Step 1) prepares positive and negative samples, and ratio is between 1:2-1:4, and wherein positive sample is the spiral shell on car door limiter to be measured Female front picture, negative sample is not by taking the picture of nut in the nut reverse side picture and production process of car door limiter to be measured And only take picture two parts composition of partial nut, the vector description file of the required positive and negative samples of creation training, tool Body step are as follows:
Training sample is divided into positive and negative samples, generally keeps positive and negative samples ratio between 1:2-1:4 in the training process, Prepare complete car door limiter top nut picture 6247 to open as positive sample, negative sample 21288 is opened.Wherein negative sample is by be measured The picture of nut is not taken in the nut reverse side picture and production process of car door limiter and only takes partial nut Picture two parts are constituted, and guarantee to be likely to occur in character pair picture and production process on other model car door limiters Wrong picture ratio be 2:1-4:1.The format of all pictures is all converted into bmp format, and by all positive samples Dimension of picture is normalized to the size of long wide 65 pixel of 65 pixels.
Step 2) passes through OpenCV open source Cooley several Weak Classifiers of local binary patterns (LBP) feature training and composition Adaboost strong classifier；
Step 3) loads corresponding classifier and the nut in car door limiter picture to be detected is positioned and identified.
Step 4) is according to the recognition result of nut in the car door limiter picture that is previously obtained, vehicle in combined standard database Positional relationship in door stop top nut and car door limiter between character, can be to show type on preliminary judgement car door limiter Number character zone Position Approximate, and these characters are identified using improved efficient template matching method, to realize that car door limits The purpose of position device detection, detailed process is as follows:
A) as shown in Fig. 2, first making template library before template matching, the word of several correct model car door limiter pictures is intercepted Accord with region picture, wherein every kind of character all will intercept and come out, then use double iterative method select certain character picture several as The Prototype drawing of corresponding character, and corresponding Character mother plate library is added, the method for generating various Character mother plate libraries, i.e. double iterative method It is as follows:
I. car door limiter shares 4 kinds of vehicles, and each vehicle respectively has 4 car doors, and each car door has a vehicle door spacing Device, therefore 16 kinds of car door limiters are shared, every kind of car door limiter is distinguished using character.First obtain i-th kind of word as much as possible Accord with the total L of picture composition pictures Pici, to every picture, utilize the method and pictures Pic of template matchingiIn other pictures It matches one by one, L-1 obtained similarity obtains the sum of similarity after being added, the sum of L similarity available in this way {sim1, sim2, sim3..., simL}；
Ii. { sim is taken1, sim2, sim3..., simLIn the smallest preceding Num of the sum of similarityspeA picture forms pictures Spe, the maximum preceding Num of the sum of similaritygenA picture forms pictures gen, and picture set spe and gen are merged into corresponding type The template library Temp of number car door limiteri, wherein the effect of pictures spe be in view of with template library do template matching to The particularity of mapping piece, i.e., in view of some pictures to be matched be it is distinguished, the effect of pictures gen be in view of with Template library does the generality of the picture to be measured of template matching, i.e., most of picture to be matched is similar；
Iii. Temp is used respectivelyiAll templates and pictures Pic in template libraryiIn each picture do template matching, Due to TempiIn have Numgen+NumspeA template, so PiciIn each picture Num all can be obtainedgen+NumspeA similarity Value, taking its maximum value is template library TempiWith pictures PiciThe similar confidence level of the picture, then PiciMiddle L picture is available L similar confidence levels, form confidence level setThis confidence level indicates PiciOn middle L picture Character be i-th kind of character confidence level size；
Iv. the template library Temp of other characters is similarly obtained using previous step i and step iij(j ≠ i and j=1,2, 3 ...), by TempjInstead of the Temp in previous step iiiiAfterwards by the method for step iii and pictures PiciIn all pictures It does template matching and obtains pictures PiciIn each character picture be jth kind character confidence level set
V. if template library TempiAnd Tempj(j ≠ i and j=1,2,3 ...) can be by pictures PiciIn each figure Piece is all identified as correct character types, i.e. i-th kind of character, then confidence level setMinimum Value is greater than setThe maximum value of (j ≠ i and j=1,2,3 ...), further sets pictures Pici Matching error rateWherein NumWrongiFor pictures PiciIn be identified as non-i-th The number of pictures of kind character, i.e. confidence level setIn be less than template matching threshold TiElement Number, NumWrongjFor character pictures PiciIn be identified as jth (j ≠ i and j=1,2,3 ...) kind character number of pictures, That is confidence level setIt is greater than the matching threshold T of template in (j ≠ i and j=1,2,3 ...)iMember Plain number seeks optimal Template library TempiProcess be exactly to seek optimal threshold TiWith optimum N umgen、NumspeSo that matching is wrong Accidentally rate WiThe smallest process；
Vi. Num is first fixedgen、NumspeValue, seek threshold value TiSo that template library TempiMatching error rateMinimum, finally in the hope of threshold value TiThe midpoint for the segment being in is as best Threshold value, after acquiring the optimal threshold, if template library Temp at this timeiMatching error rate WiEqual to zero or less than 0.0001, then Numgen、NumspeIt is regarded as optimal, then template library completes, optimal threshold TiAlso it has acquired, otherwise fixed threshold TiIf confidence level setIn be less than optimal threshold TiConfidence level number be greater than 0.2*L, then say The generality of bright template is not enough, and increases NumgenAfter continue calculate matching error rate WiIf confidence level setIn be less than optimal threshold TiConfidence level number be less than 0.02*L, the then particularity of pattern of descriptive parts It is not enough, increases NumspeAfter continue calculate matching error rate WiIf matching error rate W at this timeiEqual to zero or less than 0.0001, Then Numgen、NumspeIt is regarded as optimal, then template library completes, optimal threshold TiAlso it has acquired, has otherwise repeated this Step is until WiEqual to zero or less than 0.0001；
Vii. the final Num acquired according to previous stepgen、Numspe, to i-th kind of character picture set Pic in step ii, phase Like the sum of degree { sim1, sim2, sim3..., simLIn the smallest preceding NumspeA character picture forms pictures spe, similarity The sum of maximum preceding NumgenA character picture forms pictures gen, and picture set spe and gen are merged into Tempi, TempiJust It is the final template library of i-th kind of character.And the T that previous step finally acquiresiThe best threshold that as i-th kind of character is used for template matching Value.
B) position of car door limiter top nut is obtained according to step 3), further according to car door limiter top nut and character Positional relationship obtains the Position Approximate of car door limiter face character, so as to intercept out character region.
C) gradient map of calculating character region picture extracts marginal information using dual-threshold voltage, and process is as follows:
With the gradient of Sobel operator calculating character region picture, the gradient value of each pixel and direction on picture are obtained, Set two threshold θs1And θ2, θ is less than for gradient value1Pixel, it is believed that it is not character edge pixel, by its gradient Value is set to zero, is greater than θ for gradient value2Pixel, it is believed that it must be edge pixel point, retain its gradient value, for ladder Angle value is between θ1And θ2Pixel, if around it gradient value existing for 80 connected regions be greater than θ1The number of pixel be more than 24, i.e., gradient value is greater than θ around corresponding points1Pixel density be greater than 0.3, then it is assumed that its maximum probability is edge pixel Point, the gradient value for retaining this pixel is constant, and otherwise its gradient value is also set to zero, threshold θ1、θ2Calculation method it is as follows:
I. θ when original state1=50, θ2=200, it can also change as the case may be；
Ii. after extracting marginal information using dual-threshold voltage described above, the number numofpixel of edge pixel point is calculated, Assuming that the total number of pixels of character zone picture is S, then extracting the edge pixel dot density after marginal information in picture isIf ρ < 0.03, part edge pixel gradient value is zeroed out, so should correspond to reduces θ2If ρ > 0.1, then non-edge pixels point in part is identified as edge pixel point, improves θ so should correspond to2；
Iii. iteration runs previous step, until 0.03≤ρ≤0.1, θ at this time1And θ2As final θ1And θ2。
D) as shown in Figure 3, it is assumed that the character number on car door limiter to be measured has numofcha, all car door limiters On character type share numofkind kind, by the side of the template library step c) of the obtained numofkind kind character of step a) Method extracts edge, and the character zone gradient map then obtained respectively with step c) does improved template matching, selects template library Recognition result with numofcha character most suitable in result as car door limiter model to be measured, detailed process is as follows:
I. assume that the first character of car door limiter to be measured has several possibility, we will likely several character mould The gradient map in plate library does template matching with car door limiter character zone gradient map to be measured respectively, i-th Prototype drawing with to mapping When piece matches because having to be larger than Prototype drawing on dimension of picture to be measured, with Prototype drawing on picture to be measured with step-length 1 not Disconnected sliding window matching, in j-th of window, by the picture of corresponding position on the preceding r edge pixel point of template and picture to be measured Vegetarian refreshments, which is cooked, is accordingly calculated similarity value of the corresponding templates on this position of picture to be measured, and calculation formula is as follows:
WhereinIndicate the preceding r edge pixel of i-th template template in j-th of window on picture to be measured Point does the corresponding similarity value being calculated to the pixel of corresponding position on picture to be measured,It is m-th of i-th template The transverse gradients value size of edge pixel point,It is longitudinal gradient value size of m-th of edge pixel point of i-th template, PxmIt is the transverse gradients value size of corresponding position pixel on picture to be measured, PymIt is corresponding position pixel on picture to be measured Longitudinal gradient value size；
Ii.r iteration since 1, if the preceding r edge pixel that i-th Prototype drawing obtains on j-th of window of picture to be measured The similarity value of pointIt meets certain condition, then r=r+1, continues to calculateOtherwise this position is abandoned, it is believed that Certainly not correct respective symbols region at this position, i-th Prototype drawing slide into+1 window of picture jth to be measured continue from R=1 starts to calculate The condition of satisfaction should be corresponded to are as follows:
Wherein T is that step a) makes the optimal threshold acquired when template library,It is exactly i-th for j-th of window of picture to be measured The minimum of character representated by template can confidence level, be initialized as T, i.e.,It is subsequentNo by formula (6) Disconnected iteration generates, and Numofpix is the edge pixel number sum of i-th template, and rmax is a upper match window or one upper The last one match window of template is actually added into matched template edge total number of pixels, and jmax is that a upper template is last Match window serial number.
Iii. to first character, every kind of possible Character mother plate library can all obtain a final matching similarity, take Maximum value in these similarities, if it is greater than T, as the recognition result of this character, otherwise it is assumed that without matching result, for vehicle Each character is not always the case processing on door stop, to obtain the recognition result of all characters on car door limiter.
By above step, completes and the feature locations such as character on car door limiter and edge extracting etc. are operated, it can It is used to differentiate the features such as the character of model type on efficient identification car door limiter, to reach the mesh of car door limiter detection 's.
It should be noted thatThe proof of the corresponding condition met is as follows: formula (5) proves:
It is as follows that each parameter represents meaning:
The edge pixel number sum of Numofpix: the i-th template；
The phase of r edge pixel point and corresponding position pixel on j-th of window of picture to be measured before template Like the size of degree；
Corresponding position pixel on all edge pixel points of template and j-th of window of picture to be measured The size of the similarity of point；
Numofpix-r edge pixel point of the template other than preceding r pixel with it is to be measured The size of the similarity of corresponding pixel points on j-th of window of picture；
J-th of window of picture to be measured is exactly the minimum of character representated by i-th template can confidence level, even i-th All edge pixel points for opening template participate in matching, then should haveOtherwise j-th of window affirmative It is not correct characters；
It allows calculating process to continue, then must haveIt sets up,
1) the case where considering r < Numofpix first:
∵
And
∴
∴
Again because of Numofpix-r > 0,
∴
ByDefinition be easy prove,
∴
∴
∴
2) the case where then considering r=Numofpix:
As r=Numofpix, formula (5) degeneration isIt is obvious to set up.
Proof finishes.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered It is considered as protection scope of the present invention.

Claims (8)

1.一种基于Adaboost和模板匹配的车门限位器检测方法，其特征在于，包括以下步骤：1. a door limiter detection method based on Adaboost and template matching, is characterized in that, comprises the following steps: 步骤S1、采集待车门限位器的图片，利用Adaboost分类器对采集的车门限位器图片中的螺母进行识别与定位；Step S1, collecting a picture of the door stopper for waiting, and using the Adaboost classifier to identify and locate the nut in the collected picture of the door stopper; 步骤S2、根据所述步骤S1得到的车门限位器图片中螺母的识别结果，结合标准数据库中车门限位器上螺母与车门限位器上字符之间的位置关系，对待测车门限位器上的字符区域进行粗定位；Step S2, according to the identification result of the nut in the door stopper picture obtained in the step S1, combined with the positional relationship between the nut on the door stopper and the character on the door stopper in the standard database, the door stopper to be tested is Coarse positioning of the character area above; 步骤S3、利用双重迭代法制作车门限位器上字符的最佳模板库，同时求得模板匹配车门限位器上字符的最佳阈值；Step S3, using the double iterative method to make the best template library of the characters on the door limiter, and simultaneously obtain the best threshold value of the characters on the template matching the door limiter; 步骤S4、完成步骤S3后，提取待测车门限位器的图片和最佳模板库中用到的字符模板的边缘特征，利用快速模板匹配方法识别待测车门限位器图片中的字符，完成待测车门限位器的检测与识别。Step S4, after completing step S3, extract the picture of the door limiter to be tested and the edge features of the character template used in the best template library, and use the fast template matching method to identify the character in the picture of the door limiter to be tested, and complete. Detection and identification of door limiters to be tested. 2.根据权利要求1所述的一种基于Adaboost和模板匹配的车门限位器检测方法，其特征在于，所述步骤S1中，所述Adaboost分类器的准备过程为，首先准备训练所需的正、负样本，所述正、负样本的数量比例为1∶2-1∶4，其中正样本为待测车门限位器上的螺母正面图片，负样本由待测车门限位器的螺母反面图片及生产过程中未拍摄到螺母的图片以及只拍摄到部分螺母的图片两部分构成，创建训练所需的正、负样本的向量描述文件；再通过OpenCV开源库利用局部二值模式特征训练若干弱分类器，并组成所述Adaboost强分类器。2. a kind of door limiter detection method based on Adaboost and template matching according to claim 1, is characterized in that, in described step S1, the preparation process of described Adaboost classifier is, at first prepare training required Positive and negative samples, the ratio of the number of positive and negative samples is 1:2-1:4, wherein the positive sample is the front picture of the nut on the door limiter to be tested, and the negative sample is the nut of the door limiter to be tested. The reverse picture and the picture of the nut not captured in the production process and the picture of only part of the nut are composed of two parts, to create the vector description file of the positive and negative samples required for training; then use the local binary mode feature training through the OpenCV open source library Several weak classifiers are composed of the Adaboost strong classifier. 3.根据权利要求1所述的一种基于Adaboost和模板匹配的车门限位器检测方法，其特征在于，所述步骤S3中，利用双重迭代法制作车门限位器上字符的最佳模板库以及模板匹配字符的最佳阈值的计算方法，具体包括以下步骤：3. a kind of door limiter detection method based on Adaboost and template matching according to claim 1, is characterized in that, in described step S3, utilizes double iterative method to make the best template library of character on the door limiter And the calculation method of the optimal threshold of template matching characters, which specifically includes the following steps: 步骤S3.1、先取得尽可能多的第i种字符图片共L张组成图片集Pici，对每张图片，利用模板匹配的方法与图片集Pici中其它图片一一匹配，得到的L-1个相似度相加之后得到相似度之和，即可以得到L个相似度之和{sim1，sim2，sim3，…，simL}；Step S3.1, first obtain as many i-th character pictures as possible to form a picture set Pic i in total of L, and for each picture, use the method of template matching to match other pictures in the picture set Pic i one by one, the obtained L -1 similarities are added to obtain the sum of the similarities, that is, the sum of L similarities can be obtained {sim 1 , sim 2 , sim 3 , ..., sim L }; 步骤S3.2、取步骤S3.1中{sim1，sim2，sim3，…，simL}中相似度之和最小的前Numspe个图片组成图片集spe，相似度之和最大的前Numgen个图片组成图片集gen，将图片集合spe和gen合并成对应型号车门限位器的初始模板库Tempi；Step S3.2, take the first Num spe pictures with the smallest sum of similarity in {sim 1 , sim 2 , sim 3 , ..., sim L } in step S3.1 to form a picture set spe, and the first picture with the largest sum of similarity Num gen pictures form a picture set gen, and the picture sets spe and gen are merged into the initial template library Temp i of the corresponding model door stopper; 步骤S3.3、分别用所述步骤S3.2得到的初始模板库Tempi中的所有模板与图片集Pici中的每一张图片做模板匹配，由于Tempi中有Numgen+Numspe个模板，所以Pici中每一张图片都可得到Numgen+Numspe个相似度值，取其最大值为模板库Tempi与图片集Pici该图片的相似可信度，则Pici中L张图片可得到L个相似可信度，组成可信度集合此可信度表示Pici中L张图片上的字符为第i种字符的可信度大小；Step S3.3, do template matching with all templates in the initial template library Temp i obtained by the step S3.2 and each picture in the picture set Pic i , because there are Num gen +Num spe in Temp i template, so each picture in Pic i can get Num gen +Num spe similarity values, and the maximum value is the similarity reliability between the template library Temp i and the picture set Pic i , then L in Pic i A picture can get L similar credibility to form a credibility set This reliability indicates the reliability that the characters on the L pictures in Pic i are the i-th character; 步骤S3.4、利用所述步骤S3.1和步骤S3.2方法，得到其它车型限位器上字符的模板库Tempj(j≠i且j＝1，2，3，…)，将Tempj代替所述步骤S3.3中的Tempi后按步骤S3.3的方法与图片集Pici中的所有图片做模板匹配得到图片集Pici中的各个字符图片为第j种字符的可信度集合 Step S3.4, using the method of step S3.1 and step S3.2, obtain the template library Temp j (j≠i and j=1, 2, 3, . j replaces Temp i in the step S3.3 and performs template matching with all the pictures in the picture set Pic i according to the method of the step S3.3, and obtains that each character picture in the picture set Pic i is the credible character of the jth character degree set 步骤S3.5、如果所述步骤S3.2的模板库Tempi和Tempj(j≠i且j＝1，2，3，…)能够将图片集Pici中的各个图片都识别成正确的字符类型，即第i种字符，那么可信度集合的最小值大于集合(j≠i且j＝1，2，3，…)的最大值，进一步设图片集Pici的匹配错误率其中NumWrongi为图片集Pici中识别成非第i种字符的图片数目，即可信度集合中小于模板的匹配阈值Ti的元素个数，NumWrongj为字符图片集Pici中识别成第j(j≠i且j＝1，2，3，…)种字符的图片数目，即可信度集合(j≠i且j＝1，2，3，…)中大于模板的匹配阈值Ti的元素个数，求取最佳模板库Tempi的过程就是求取最佳阈值Ti与最佳Numgen、Numspe使得匹配错误率Wi最小的过程；Step S3.5, if the template libraries Temp i and Temp j (j≠i and j=1, 2, 3, . . . ) of the step S3.2 can identify each picture in the picture set Pic i as correct Character type, i.e. the i-th character, then the credibility set The minimum value of is greater than the set (j≠ i and j=1, 2, 3, . Among them, NumWrong i is the number of pictures identified as non-i-th characters in the picture set Pic i , that is, the reliability set NumWrong j is the number of elements that are less than the matching threshold T i of the template, and NumWrong j is the number of pictures identified as the jth (j≠i and j=1, 2, 3,...) character in the character picture set Pic i , which can be trusted degree set ( j ≠ i and j =1, 2, 3, . gen and Num spe make the matching error rate Wi the smallest process; 步骤S3.6、先固定步骤S3.5中Numgen、Numspe的值，求取阈值Ti使得模板库Tempi的匹配错误率最小，最后以求得的阈值Ti处于的区间段的中点作为最佳阈值，求得所述最佳阈值后，若此时模板库Tempi的匹配错误率Wi等于零或小于0.0001，则Numgen、Numspe即可认为是最佳的，则模板库制作完成，最佳阈值Ti也已求得，否则固定阈值Ti，若可信度集合中小于最佳阈值Ti的可信度个数大于0.2*L，则说明模板的一般性不够充分，增大Numgen后继续计算匹配错误率Wi，若可信度集合中小于最佳阈值Ti的可信度个数小于0.02*L，则说明模板的特殊性不够充分，增大Numspe后继续计算匹配错误率Wi，若此时匹配错误率Wi等于零或小于0.0001，则Numgen、Numspe即可认为是最佳的，则模板库制作完成，最佳阈值Ti也已求得，否则重复此步骤直至Wi等于零或小于0.0001；Step S3.6, first fix the values of Num gen and Num spe in step S3.5, and obtain the threshold T i to make the matching error rate of the template library Temp i Minimum, and finally take the midpoint of the interval in which the obtained threshold T i is located as the optimal threshold, and after obtaining the optimal threshold, if the matching error rate Wi of the template library Temp i at this time is equal to zero or less than 0.0001 , then Num gen and Num spe can be considered to be the best, then the template library is completed, and the optimal threshold T i has also been obtained, otherwise the threshold value T i is fixed, if the reliability set The number of confidence levels smaller than the optimal threshold T i is greater than 0.2*L, indicating that the generality of the template is not sufficient. After increasing Num gen , continue to calculate the matching error rate Wi , if the confidence level set The number of confidence levels less than the optimal threshold T i is less than 0.02*L, indicating that the particularity of the template is not sufficient. After increasing Num spe , continue to calculate the matching error rate Wi , if the matching error rate Wi is equal to zero or If it is less than 0.0001, then Num gen and Num spe can be considered as optimal, then the template library is completed, and the optimal threshold T i has also been obtained, otherwise repeat this step until Wi is equal to zero or less than 0.0001; 步骤S3.7、根据所述步骤S3.6求得的最终Numgen、Numspe，对步骤S3.1中第i种字符图片集合Pici，相似度之和{sim1，sim2，sim3，…，simL}中最小的前Numspe个字符图片组成图片集spe，相似度之和最大的前Numgen个字符图片组成图片集gen，将图片集合spe和gen合并成Tempi，Tempi即最终形成的第i种字符的模板库，而所述步骤S3.6最终求得的Ti即为第i种字符用于模板匹配的最佳阈值。Step S3.7, according to the final Num gen and Num spe obtained in step S3.6, for the i-th character picture set Pic i in step S3.1, the sum of the similarity {sim 1 , sim 2 , sim 3 , ..., sim L } The smallest first Num spe character pictures form the picture set spe, the first Num gen character pictures with the largest sum of similarity form the picture set gen, and the picture sets spe and gen are combined into Temp i , Temp i That is, the template library of the i-th character is finally formed, and the T i finally obtained in the step S3.6 is the optimal threshold value of the i-th character used for template matching. 4.根据权利要求1所述的一种基于Adaboost和模板匹配的车门限位器检测方法，其特征在于，所述步骤S4，具体包括以下步骤：4. a kind of door limiter detection method based on Adaboost and template matching according to claim 1, is characterized in that, described step S4, specifically comprises the following steps: 步骤S4.1、根据步骤S3得到车门限位器上螺母的位置，再根据车门限位器上螺母与字符的位置关系得到车门限位器上字符的位置，并将字符所在区域截取出来；Step S4.1, obtain the position of the nut on the door stopper according to step S3, then obtain the position of the character on the door stopper according to the positional relationship between the nut and the character on the door stopper, and cut out the area where the character is located; 步骤S4.2、计算字符区域图片的梯度图，利用双阈值法提取边缘信息；Step S4.2, calculate the gradient map of the character area picture, and extract the edge information by using the double threshold method; 步骤S4.3、设待测车门限位器上的字符个数有numofcha个，所有车门限位器上的字符种类共有numofkind种，将步骤S3得到的numofkind种字符的模板库用步骤S4.2的方法提取边缘，然后分别与步骤S4.2得到的字符区域梯度图做模板匹配，选择模板库匹配结果中最合适的numofcha个字符作为待测车门限位器型号的识别结果。Step S4.3, it is assumed that the number of characters on the door stopper to be tested is numofcha, the character types on all the door stoppers are numofkind, and the template library of numofkind characters obtained in step S3 is used in step S4.2 The method extracts the edges, and then performs template matching with the character region gradient map obtained in step S4.2 respectively, and selects the most suitable numofcha characters in the matching result of the template library as the recognition result of the door limiter model to be tested. 5.根据权利要求2所述的一种基于Adaboost和模板匹配的车门限位器检测方法，其特征在于，创建训练正、负样本的向量描述文件，具体步骤为：5. a kind of door limiter detection method based on Adaboost and template matching according to claim 2, is characterized in that, create the vector description file of training positive and negative samples, and concrete steps are: 训练样本分为正、负样本，在训练过程中，正、负样本的数量比例为1∶2-1∶4，所述负样本由待测车门限位器的螺母反面图片及生产过程中未拍摄到螺母的图片以及只拍摄到部分螺母的图片两部分构成，并且保证其它型号车门限位器上的对应特征图片及生产过程中可能出现的错误图片的比例为2∶1-4∶1，所有图片的格式都转化为bmp格式，并且将所有正样本的图片尺寸化为长65像素，宽65像素的大小。The training samples are divided into positive and negative samples. During the training process, the ratio of the number of positive and negative samples is 1:2-1:4. The picture of the nut and the picture of only part of the nut are composed of two parts, and the ratio of the corresponding feature pictures on other models of door stoppers and the wrong pictures that may occur in the production process is guaranteed to be 2:1-4:1. The format of all images is converted to bmp format, and the image size of all positive samples is 65 pixels long and 65 pixels wide. 6.根据权利要求4所述的一种基于Adaboost和模板匹配的车门限位器检测方法，其特征在于，所述步骤S4.2的具体过程如下：6. a kind of door limiter detection method based on Adaboost and template matching according to claim 4, is characterized in that, the concrete process of described step S4.2 is as follows: 用Sobel算子计算字符区域图片的梯度，得到字符区域图片上各个像素点的梯度值和方向，设定两个阈值θ1和θ2，将梯度值小于θ1的像素点的梯度值置为零，将梯度值大于θ2的像素点梯度值保留，当梯度值介于θ1和θ2的像素点的周围80连通区域存在的梯度值大于θ1的像素点的个数超过24个，即对应点周围梯度值大于θ1的像素点的密度大于0.3，则保留梯度值，否则将梯度值也置为零。Use the Sobel operator to calculate the gradient of the character area image, obtain the gradient value and direction of each pixel on the character area image, set two thresholds θ 1 and θ 2 , and set the gradient value of the pixel whose gradient value is less than θ 1 as 0, the gradient value of the pixel with the gradient value greater than θ 2 is retained. When the gradient value is between θ 1 and θ 2 , there are more than 24 pixels in the connected region with the gradient value greater than θ 1 . That is, the density of the pixels with the gradient value greater than θ 1 around the corresponding point is greater than 0.3, the gradient value is retained, otherwise the gradient value is also set to zero. 7.根据权利要求4所述的一种基于Adaboost和模板匹配的车门限位器检测方法，其特征在于，所述步骤S4.3，具体过程如下：7. a kind of door limiter detection method based on Adaboost and template matching according to claim 4, is characterized in that, described step S4.3, concrete process is as follows: 设待测车门限位器的第一个字符有若干种可能，将若干种字符模板库的梯度图分别与待测车门限位器字符区域梯度图做模板匹配，第i张模板图与待测图片做匹配时，用模板图在待测图片上以步长1不断滑动窗口匹配，在第j个窗口时，将模板的前r个边缘像素点与待测图片上对应位置的像素点做相应计算得到对应模板在待测图片这个位置上的相似度值，计算公式为Assuming that the first character of the door stopper to be tested has several possibilities, the gradient maps of several character template libraries are matched with the gradient map of the character region of the door stopper to be tested as templates respectively. When the image is matched, the template image is used to continuously slide the window matching on the image to be tested with a step size of 1. In the jth window, the first r edge pixels of the template are matched with the pixels of the corresponding position on the image to be tested. Calculate the similarity value of the corresponding template at the position of the image to be tested. The calculation formula is: 其中表示第i张模板在待测图片上第j个窗口时用模板的前r个边缘像素点与待测图片上对应位置的像素点做相应计算得到的相似度值，是第i张模板的第m个边缘像素点的横向梯度值大小，是第i张模板的第m个边缘像素点的纵向梯度值大小，Pxm是待测图片上对应位置像素点的横向梯度值大小，Pym是待测图片上对应位置像素点的纵向梯度值大小；r从1开始迭代，若第i张模板图在待测图片第j个窗口上得到的前r个边缘像素点的相似度值满足预定条件，则r＝r+1，继续计算否则放弃所述待测位置，再将第i张模板图滑动到待测图片第j+1个窗口继续从r＝1开始计算所需满足预定条件为in Represents the similarity value calculated by the first r edge pixels of the template and the pixels at the corresponding positions on the image to be tested when the ith template is in the jth window on the image to be tested, is the lateral gradient value of the mth edge pixel of the ith template, is the vertical gradient value of the m-th edge pixel of the i-th template, Px m is the horizontal gradient value of the pixel at the corresponding position on the image to be tested, and Py m is the vertical gradient value of the pixel at the corresponding position on the image to be tested. Size; r iterates from 1, if the similarity value of the first r edge pixels obtained from the i-th template image on the j-th window of the image to be tested If the predetermined conditions are met, then r=r+1, and the calculation continues Otherwise, abandon the position to be tested, and then slide the i-th template image to the j+1-th window of the image to be tested and continue to calculate from r=1 The predetermined conditions that need to be met are 其中T为所述步骤S3得到的模板匹配的最佳阈值，为待测图片第j个窗口就是第i张模板所代表的字符的最小可置信度，初始化为T，即 不断迭代产生，Numofpix为第i张模板的边缘像素个数总数，rmax为上一个匹配窗口或上一个模板的最后一个匹配窗口实际加入匹配的模板边缘像素总个数，jmax为上一个模板最后的匹配窗口序号；对第一个字符，每种可能的字符模板库都会得到一个最终的匹配相似度，取这些相似度中最大值，若其大于T，则作为此字符的识别结果，否则认为无匹配结果，对于车门限位器上每个字符都逐个进行处理，从而得到车门限位器上所有字符的识别结果。Wherein T is the optimal threshold value of the template matching obtained in the step S3, The jth window of the image to be tested is the minimum confidence level of the character represented by the ith template, which is initialized to T, that is Iteratively generated, Numofpix is the total number of edge pixels of the ith template, rmax is the total number of template edge pixels actually added to the last matching window or the last matching window of the previous template, and jmax is the last template of the previous template. Matching window number; for the first character, each possible character template library will get a final matching similarity, take the maximum value of these similarities, if it is greater than T, it will be used as the recognition result of this character, otherwise it will be regarded as no For the matching result, each character on the door limiter is processed one by one, so as to obtain the recognition results of all characters on the door limiter. 8.根据权利要求6所述的一种基于Adaboost和模板匹配的车门限位器检测方法，其特征在于，所述阈值θ1、θ2的计算方法为，初始状态时θ1＝50、θ2＝200，利用所述双阈值法提取边缘信息后，计算边缘像素点的个数numofpixel，假设字符区域图片的像素总个数为S，则提取边缘信息后图片中的边缘像素点密度为若ρ＜0.03，则部分边缘像素点梯度值置零，对应降低θ2，若ρ＞0.1，则部分非边缘像素点被识别为了边缘像素点，所以对应提高θ2，直至0.03≤ρ≤0.1，此时的θ1和θ2即为最终的θ1和θ2。8 . The method for detecting a door limiter based on Adaboost and template matching according to claim 6 , wherein the calculation methods of the thresholds θ 1 and θ 2 are, in the initial state, θ 1 =50, θ 2 = 200, after using the double threshold method to extract edge information, calculate the number of edge pixels numofpixel, assuming that the total number of pixels in the character area picture is S, then the edge pixel density in the picture after the edge information is extracted is If ρ < 0.03, the gradient value of some edge pixels is set to zero, correspondingly reducing θ 2 , if ρ > 0.1, then some non-edge pixels are identified as edge pixels, so the corresponding increase θ 2 until 0.03≤ρ≤0.1 , θ 1 and θ 2 at this time are the final θ 1 and θ 2 .
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