CN107862675A - A kind of real-time vision detection method for electric bucket tooth missing - Google Patents
A kind of real-time vision detection method for electric bucket tooth missing Download PDFInfo
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Abstract
The invention belongs to mining technique field, specifically a kind of real-time vision detection method for electric bucket tooth missing.Comprise the following steps:Ith, the positive and negative samples of one group of electric bucket tooth are shot in advance;IIth, positive and negative samples HOG features are extracted;IIIth, HOG features are input in SVM grader and trained, obtain decision function;IVth, the picture of live captured in real-time is inputted, HOG feature extractions are carried out by detection window, then by svm classifier, complete the Preliminary detection to electric bucket tooth;Vth, accurate detection of the SC constraint completions to electric bucket tooth is carried out to the electric bucket tooth testing result of acquisition;Comprise the following steps that, a. image preprocessings and contours extract;B. SC features are calculated;C. shape similarity calculates;VIth, the accurate testing result of electric bucket tooth is compared with number of the electric bucket tooth set in advance without bucket tooth when lacking, judges whether electric bucket tooth lacks.Instant invention overcomes Conventional visual detection method because accuracy rate is low caused by false retrieval, missing inspection the problem of, reduces rate of false alarm.
Description
Technical field
The invention belongs to mining technique field, specifically a kind of real-time vision detection method for electric bucket tooth missing.
Background technology
When power shovel is in digging operation, because of the interphase interaction of electric bucket tooth and material, electric bucket tooth is sheared and squeezed
Active force is pressed, and bears certain shock loading, the electric bucket tooth being fixed on originally on toothholder on scraper bowl is easily broken off or loosened
Come off.
After electric bucket tooth comes off, frictional resistance during power shovel operation is not only increased, causes scraper bowl to be cut when excavating material
Difficulty, accelerate the abrasion of scraper bowl, influence the service life of scraper bowl;What is more important power shovel made of potassium steel or steel alloy
Bucket tooth, hardness are far above ore, if hard electric bucket tooth together enters in stone crusher with ore materials, will cause rubble
Machine damages, and destroys whole digging --- crushing production line, and the entire production line maintenance difficulty is big, time-consuming, costly, once it is raw
Producing line is stopped work, and will cause huge economic loss;What is more important, the electric bucket tooth to come off are likely to draw into stone crusher
Send out stone crusher mechanical accident, the serious personal safety for even influencing whether operating personnel.And mine excavator is bulky, work
Make environment complexity, be not easy to the state that excavator operating personnel monitor electric bucket tooth by way of manually observing.
Being come off context of detection in electric bucket tooth, domestic and foreign scholars and relevant enterprise have done substantial amounts of research-and-development activity,
And form the product of correlation.2016, the electric bucket tooth based on sample that Ser Nam Lim etc. have studied complete set came off
Vision detection system, it extracts the image pattern of power shovel scraper bowl in motion process first, is first positioned using these sample informations
The approximate location of electric bucket tooth, then result is modified using frame difference method and optical flow method, comprehensively utilizes template again afterwards
Match with the method for tooth trace fitting to be accurately positioned electric bucket tooth target.Finally, it is special with reference to the related gray scale of electric bucket tooth image
Sign just may determine that whether electric bucket tooth comes off.But from the point of view of the service condition of various regions, there is the ratio of rate of false alarm still
Compare high.
The content of the invention
In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is that providing one kind is used for power shovel
The real-time vision detection method of bucket tooth missing.
The present invention takes following technical scheme:A kind of real-time vision detection method for electric bucket tooth missing, the side
Method can detect the missing of bucket tooth in real time in power shovel operation process, and bucket tooth target, realization pair are identified by using SVM combinations SC
The detection of electric bucket tooth missing, comprises the following steps:
Ith, the positive and negative samples of one group of electric bucket tooth are shot in advance;
IIth, positive and negative samples HOG features are extracted;
IIIth, HOG features are input in SVM grader and trained, obtain decision function;
IVth, the picture of live captured in real-time is inputted, HOG feature extractions are carried out by detection window, then by svm classifier,
Complete the Preliminary detection to electric bucket tooth;
Vth, accurate detection of the SC constraint completions to electric bucket tooth is carried out to the electric bucket tooth testing result of acquisition;Specific step
Rapid as follows, a. image preprocessings and contours extract;B. SC features are calculated;C. shape similarity calculates;
VIth, by the accurate testing result of electric bucket tooth compared with electric bucket tooth set in advance is without the number of bucket tooth when lacking
Compared with judging whether electric bucket tooth lacks.
The positive and negative samples of electric bucket tooth are shot in the step I in advance, cover electric bucket tooth in all works of operative scenario
Make situation, and preset number of the electric bucket tooth without bucket tooth when coming off, the positive and negative samples of monodentate should be obtained during shooting.
The step II includes:
Step 1, the positive and negative samples to one group of electric bucket tooth of collection carry out image preprocessing, and image preprocessing includes ash
Degreeization and Gamma are corrected;
Step 2, the image gradient for calculating electric bucket tooth positive and negative samples;
Step 3, gradient direction statistic histogram is calculated, obtain HOG features.
The gray scale for the image that the main purpose of Gamma corrections is correction in the case where image is excessively bright or excessively dark, to increase
Strong contrast, reduces the influence to image of illumination variation and shade, and in step II, Gamma correction calculation formula (1) are as follows
Y (x, y)=I (x, y)γFormula (1)
Wherein I (x, y) represents gray scale of the input picture in point (x, y) place pixel, and Y (x, y) represents the pixel after correction
The gray value of point, it is contemplated that the noise of camera sensor is directly proportional to the square root of illumination, therefore takes γ=0.5, with maximum limit
The reduction noise of degree, and improve the contrast of image.
The calculation formula of the image gradient size and Orientation for the electric bucket tooth positive and negative samples extracted is as follows:
Gx(x, y)=H (x+1, y)-H (x-1, y) formula (2)
Gy(x, y)=H (x, y+1)-H (x, y-1) formula (3)
Wherein GX(x,y)、GY(x, y) represents the Grad of x directions and y directions epigraph respectively, and H (x, y) is represented in image
The gray value of certain point, G (x, y) represent the size of image point gradient vector, and α (x, y) represents the direction of this gradient vector.
Gradient direction statistic histogram is calculated, obtains HOG features, specific implementation method comprises the following steps:
A) cell factory cell size is selected, because detection window pixel size is 64 × 128, herein by detection window point
For 128 cell factories, the size of each cell factory is 8 × 8 pixels.
B) each cell factory inside gradient direction histogram is calculated, computational methods are as follows:
The range set of gradient direction is arrived into 180 degree for 0, and gradient direction is divided into 9 sections, then counts the cell factory
The number that different gradient directions occur in interior each pixel, gradient direction occurs on each each pixel of cell factory is counted
, it is necessary to be multiplied by a weight coefficient to each pixel during number, the size that the weight coefficient is each pixel gradient, example are taken
Such as, the gradient direction of some pixel is 10 degree, and its gradient magnitude is 1.5, then the number that this o'clock occurs in 0 to 20 degree sections is
1 × 1.5 time.The calculation formula (6) of each pixel occurrence number in each gradient direction section is as follows:
Wherein, α (x, y) be cell factory in certain point gradient direction, binkFor k-th section, Vk(x, y) represents picture
The number that vegetarian refreshments occurs in k-th gradient direction section.
By above-mentioned calculating process, in each cell factory, by the gradient magnitude of each pixel according to different gradients
Direction is counted, and the HOG characteristic vectors of the cell factory are precisely formed.
C) several adjacent cell factories are formed into a description block block, to cell factory all in description block
Statistic histogram combination of eigenvectors form the characteristic vector of description block together, by that analogy, calculate all description blocks
HOG characteristic vectors.
D) histogram in description block is normalized, calculation formula (7) is as follows:
Wherein v represents characteristic vector, and ε represents the number of a very little, can be taken as 0.000001, it is therefore an objective to prevent divisor
For 0 situation, the characteristic vector of all description blocks, which joins end to end, after normalization can form HOG features.
The calculation procedure of the step III is as follows:
1) sample set { x of given training is seti,yi, i=1,2...n, x ∈ Rn, y ∈ { ± 1 }, x represents sample in formula
Characteristic vector, y represent category label, if required optimal hyperlane is:H:wTX+b=0;
2) Lagrange multiplier α value is calculated, calculation formula is as follows:
Bring α values into formula (9), formula (10) can obtain hyperplane ω, b optimal solution;
WhereinTherefore optimal classification plane ω is obtained*·x+b*=0, and optimal decision function is:
The step IV includes:
Step 1) setting camera captures image temporal interval, the working region real-time grasp shoot figure using camera to electric bucket tooth
Picture, and by the image transmitting captured to computer, to read image that camera captures in real time and to be pre-processed;
Step 2) is detected to the every piece image captured, and the detection window used is being schemed according to the step-length of 10 pixels
Slided as in until covering entire image, often slides the HOG features for once just extracting image in detection window, then, by positive and negative
The decision function that template is obtained by SVM classifier training is judged characteristic vector, to determine the image in window
Whether it is electric bucket tooth target, and will determine that result is identified in the picture for positive window, the like, wait slip to detect
The window for the expression same target that overlaps is combined again after end, completes the Preliminary detection to electric bucket tooth.
The step V includes:
A. the image in detection window is pre-processed and extracts profile, comprised the following steps:
1) bianry image is converted the image into, calculation formula (12) is as follows:
Wherein, T1、T2For threshold value set in advance, scope is 0~255.
2) boundary tracking algorithm is used, extracts objective contour, realizes that step is combined shown in figure (8), grey parts table in figure
Show the pixel that gray value is 1, white portion represents the pixel that gray value is 0, during calculating, from upper left side point b0Start, from
Left-to-right, the point b that gray value is 1 is found from top to bottom0And its left dorsal sight spot c0, from c0Start along b08 neighborhood directions
Clockwise search is carried out, until running into the point b that next gray value is 11, this seasonal c1Equal to b1That 8 neighborhood before
Middle background dot, by that analogy, until correcting action, it accurately can obtain the profile of bianry image.
B. SC features are calculated, are comprised the following steps:
1) by extracting profile and the point on profile being sampled, a position for containing profile up-sampling point is obtained
Data set P={ the p of information1,p2...pn, some point p that first selected point is concentrated1(x1,y1) polar limit is used as, calculate
Remaining n-1 point pn(xn,yn) and p1(x1,y1) vectorial size and Orientation is formed, similarly, then with other point works in set P
Angle and distance information is calculated successively for polar origin, the matrix R and A of accurate available two n × (n-1) size, its
The distance and angle information of all sampled points on the profile are stored, calculation formula is as follows:
2) all range information r are normalized using experience densimetry, and to the r after normalizationnorCarry out
Logarithmic transformation, each point (x, y) in rectangular coordinate system is accurately mapped to log-polar (logr, θ) in, normalization is public
Formula (15) is as follows:
3) polar coordinate system as shown in Figure 10, is divided into m × n subinterval, wherein m is distance parameter, and n joins for angle
Number, the number for falling into profile point in each subinterval (dash area in such as Figure 10) is counted afterwards, so as to obtain SC statistics Nogatas
Figure;Similarly, respectively using difference as origin, the SC features of whole profile can be calculated.
Calculation formula (16) is as follows:
hi(k)={ q ≠ pi:(q-pi) ∈ bin (k) formula (16)
Wherein k=1,2 ... K, K=m × n, represent k-th of component in histogram, PiRepresent that point concentration is used as pole
The point of coordinate limit, q represent other points, (q-pi) ∈ bin (k) represent relative to pi, point q belongs to k-th of subinterval;
C. shape similarity calculates, and formula (17) is as follows:
Wherein piRepresent the SC histograms using point i as origin, q on a profile pjRepresent on another profile q using point j as
The SC histograms of origin;Cost=C (pi,qj) represent the distance between the two statistic histograms, that is, of two profiles
With cost;hiAnd h (k)j(k) two shape histogram p are represented respectivelyiAnd qjThe value of middle kth level, it is known that Ci,jSpan exist
Between 0 to 1, the similarity of its value and two profiles is inversely proportional, i.e. Ci,jSmaller, two profiles are more similar, Ci,jIt is bigger, two wheels
It is wide more dissimilar.
According to formula (17), a point P on profile p is first calculated1With on sample profile q Matching power flow a little, i.e.,
All points on the upper point of profile p and profile q are matched, the matrix of 1 × n size can be obtained, retain this square
The minimum value of element is as point P in battle array1Matching power flow, have found P equivalent on profile q1The point most matched, with identical
Method calculates all-pair on profile p and, in q Matching power flow, accurately can equally obtain the matrix of 1 × n size, calculate
The sum of whole elements in this matrix, a single non-vector index is accurately can obtain, in order to describe the two profiles
Between similitude.
In the step VI, judge that the method whether electric bucket tooth lacks is as follows, for power shovel, bucket tooth on its scraper bowl
Number be changeless, therefore can judge whether bucket tooth takes off as an index according to this invariant of bucket tooth number
Fall, if a known scraper bowl has N number of bucket tooth, if bucket tooth does not come off, then the number M for the bucket tooth being detected in the picture
N should be equal to, only need to compare M and N difference it may determine that whether bucket tooth comes off.
Compared with prior art, the present invention is completed to the Preliminary detection of electric bucket tooth simultaneously by classification of the SVM to HOG features
Using constraints of the SC to Preliminary detection result, realize the accurate detection to electric bucket tooth, overcome Conventional visual detection method because
The problem of accuracy rate caused by false retrieval, missing inspection is low, reduces rate of false alarm.
Brief description of the drawings
Fig. 1 is electric bucket tooth target detection flow chart;
Fig. 2 is HOG feature calculation flow charts;
Fig. 3 is form fit algorithm flow chart;
Fig. 4 is bucket tooth positive and negative samples collection schematic diagram
Fig. 5 is the HOG features and angular divisions schematic diagram of individual cells unit;
Fig. 6 is cell factory and block division signal;
Fig. 7 is the visualization HOG features of bucket tooth;
Fig. 8 is bianry image contour-tracking algorithm;
Fig. 9 is the binaryzation result and Contour tracing result of bucket tooth;
Figure 10 is distance and angle calculation.
Embodiment
The embodiment of the present invention is described in further detail below in conjunction with the accompanying drawings.
As shown in figure 1, Fig. 1 is the system basic framework figure of the electric bucket tooth detection method based on SVM and SC;Based on SVM
Trained, obtained by extracting the HOG features of positive and negative samples and inputting in SVM grader with SC electric bucket tooth detection method
Decision function, HOG feature extractions are carried out to the picture of live captured in real-time, and HOG is classified using decision function, to reach pair
The Preliminary detection of electric bucket tooth, and row constraint is entered to the Preliminary detection result of electric bucket tooth with reference to SC, complete to electric bucket tooth
Accurate detection, the number of detected electric bucket tooth is obtained, pass through electric bucket tooth number more set in advance and accurate inspection
The number of the electric bucket tooth measured, you can judge whether electric bucket tooth lacks.Illustrate:
A kind of real-time vision detection method for electric bucket tooth missing, this method are special by the HOG for extracting positive and negative samples
Levy and be input in SVM grader and train, obtain decision function and to being carried out to the picture to live captured in real-time to power shovel
The Preliminary detection of bucket tooth, and row constraint is entered to the Preliminary detection result of electric bucket tooth with reference to SC, reach to the accurate of electric bucket tooth
Detection.Comprise the following steps:
Step I, the positive and negative samples of electric bucket tooth are shot, wherein, for positive sample, it is desirable to which positive sample should be as far as possible
Only comprising bucket tooth in itself;And for negative sample, then required without too many, it is any to choose an equal amount of region in background image
, while require that the quantity of negative sample will be more than the quantity of positive sample.And electric bucket tooth is preset without bucket tooth when coming off
Number.
Step II, the HOG features of electric bucket tooth positive and negative samples are extracted, are comprised the following steps:
Step 1, in view of shooting photo when, the picture quality caused by the influence of various factors is low, causes detection accurate
The problem of really rate is low, the picture that need to be shot to camera pre-process, including gray processing and Gamma corrections.
The gray scale for the image that the main purpose of Gamma corrections is correction in the case where image is excessively bright or excessively dark, to increase
Strong contrast, reduces the influence of illumination variation and shade to image, and calculation formula (1) is as follows
Y (x, y)=I (x, y)γFormula (1)
Wherein I (x, y) represents gray scale of the input picture in point (x, y) place pixel, and Y (x, y) represents the pixel after correction
The gray value of point.It is directly proportional in view of the noise of camera sensor and the square root of illumination, therefore γ=0.5 is taken, with maximum limit
The reduction noise of degree, and improve the contrast of image.
Step 2, the gradient magnitude for calculating extracted electric bucket tooth positive and negative samples and direction, calculation formula formula are as follows:
Gx(x, y)=H (x+1, y)-H (x-1, y) formula (2)
Gy(x, y)=H (x, y+1)-H (x, y-1) formula (3)
Wherein GX(x,y)、GY(x, y) represents the Grad of x directions and y directions epigraph respectively, and H (x, y) is represented in image
The gray value of certain point, G (x, y) represent the size of image point gradient vector, and α (x, y) represents the direction of this gradient vector.
Step 3, gradient direction statistic histogram is calculated, obtain HOG features, specific implementation method comprises the following steps:
A) size of cell factory (cell) is selected, because detection window pixel size is 64 × 128, herein by detection window
It is divided into 128 cell factories, the size of each cell factory is 8 × 8 pixels;
B) each cell factory inside gradient direction histogram is calculated, computational methods are as follows:
The range set of gradient direction is arrived into 180 degree for 0, and gradient direction is divided into 9 sections, then counts the cell factory
The number that different gradient directions occur in interior each pixel, gradient direction occurs on each each pixel of cell factory is counted
, it is necessary to be multiplied by a weight coefficient to each pixel during number, the size that the weight coefficient is each pixel gradient, example are taken
Such as, the gradient direction of some pixel is 10 degree, and its gradient magnitude is 1.5, then the number that this o'clock occurs in 0 to 20 degree sections is
1 × 1.5 time.The calculation formula (6) of each pixel occurrence number in each gradient direction section is as follows:
Wherein, α (x, y) be cell factory in certain point gradient direction, binkFor k-th section, Vk(x, y) represents picture
The number that vegetarian refreshments occurs in k-th gradient direction section.
By above-mentioned calculating process, in each cell factory, by the gradient magnitude of each pixel according to different gradients
Direction is counted, and the HOG characteristic vectors of the cell factory are precisely formed.
C) several adjacent cell factories are formed into a description soon (block), to cell list all in description block
The statistic histogram combination of eigenvectors of member forms the characteristic vector of description block together, by that analogy, calculates all description blocks
HOG characteristic vectors;
4) histogram in description block is normalized, calculation formula (7) is as follows:
Wherein v represents characteristic vector, and ε represents the number of a very little, it is therefore an objective to prevents the situation that divisor is 0.Normalizing
The characteristic vector of all description blocks, which joins end to end, after change can form HOG features.
Step III, HOG features are input in SVM grader and trained, obtain the decision function of this feature, calculate step
It is rapid as follows:
1) sample set { x of given training is seti,yi, i=1,2...n, x ∈ Rn, y ∈ { ± 1 }, x represents sample in formula
Characteristic vector, y represent category label, if required optimal hyperlane is:H:wTX+b=0
2) Lagrange multiplier α value is calculated, calculation formula (8) is as follows:
Bring α values into formula (9), formula (10) can obtain hyperplane ω, b optimal solution
WhereinTherefore optimal classification plane ω is obtained*·x+b*=0, and optimal decision function is:
Step IV, the picture of live captured in real-time is inputted, HOG feature extractions are carried out by detection window, then pass through SVM points
Class, the Preliminary detection to electric bucket tooth is completed, is comprised the following steps:
Step 1) setting camera captures image temporal interval, and the working region of electric bucket tooth is grabbed in real time using infrared camera
Image is clapped, and by the image transmitting captured into computer specified folder, to read the image that camera is captured in real time
And pre-processed.
After step 2) pre-processes to image, detection window is slided until covering in the picture according to certain step-length
Lid entire image, the HOG characteristic vectors for once just extracting image in window are often slided, then, by the decision-making letter obtained by training
Count to judge characteristic vector, to determine whether the image in window is electric bucket tooth target, if bucket tooth target, then sentence
Disconnected result will determine that result is identified in the picture for positive window to be otherwise negative just, the like, wait slip to examine
Survey after terminating and be again combined the window for the expression same target that overlaps, complete the Preliminary detection to electric bucket tooth.
Step V, using the method for form fit, compare similar between the image in detection window and bucket tooth template samples
Property degree, is added to SVM testing results, to the electric bucket tooth of acquisition using this similarity evaluation index as a kind of constraints
Testing result carries out SC constraints, completes the accurate detection to electric bucket tooth;
A. the image in detection window is pre-processed and extracts profile, comprised the following steps:
1) bianry image is converted the image into, calculation formula (12) is as follows:
Wherein, T1、T2For threshold value set in advance, scope is 0~255.
2) boundary tracking algorithm is used, extracts objective contour, realizes that step is combined shown in figure (8), grey parts table in figure
Show the pixel that gray value is 1, white portion represents the pixel that gray value is 0, during calculating, from upper left side point b0Start, from
Left-to-right, the point b that gray value is 1 is found from top to bottom0And its left dorsal sight spot c0, from c0Start along b08 neighborhood directions
Carry out clockwise search, it is known that run into the point b that next gray value is 11, this seasonal c1Equal to b1That 8 neighborhood before
Middle background dot, by that analogy, until correcting action, it accurately can obtain the profile of bianry image.
B. SC features are calculated, are comprised the following steps:
1) by extracting profile and the point on profile being sampled, one can be obtained and contain profile up-sampling point
Data set P={ the p of positional information1,p2...pn, some point p that first selected point is concentrated1(x1,y1) polar limit is used as,
Calculate remaining n-1 point pn(xn,yn) and p1(x1,y1) vectorial size and Orientation is formed, similarly, then with other in set P
Point calculates angle and distance information as polar origin successively, the matrix R of accurate available two n × (n-1) size and
A, which stores the distance and angle information of all sampled points on the profile.Calculation formula is as follows:
2) to ensure that point as much as possible all falls in a coordinate system, all range information r are entered using experience densimetry
Row normalized, and to the r after normalizationnorLogarithmic transformation is carried out, accurately by each point (x, y) in rectangular coordinate system
It is mapped to log-polar (logr, θ) in, normalization formula (15) is as follows:
3) polar coordinate system as shown in Figure 10, is divided into m × n subinterval, wherein m is distance parameter, and n joins for angle
Number, the number for falling into profile point in each subinterval (dash area in such as Figure 10) is counted afterwards, so as to obtain SC statistics Nogatas
Figure.Similarly, respectively using difference as origin, the SC features of whole profile can be calculated.Calculation formula (16) is as follows:
hi(k)={ q ≠ pi:(q-pi) ∈ bin (k) formula (16)
Wherein k=1,2 ... K, K=m × n, represent k-th of component in histogram, PiRepresent that point concentration is used as pole
The point of coordinate limit, q represent other points, (q-pi) ∈ bin (k) represent relative to pi, point q belongs to k-th of subinterval.
C. shape similarity calculates, under formula (17) enters:
Wherein piRepresent the SC histograms using point i as origin, q on a profile pjRepresent on another profile q using point j as
The SC histograms of origin;Cost=C (pi,qj) represent the distance between the two statistic histograms, that is, of two profiles
With cost;hiAnd h (k)j(k) two shape histogram p are represented respectivelyiAnd qjThe value of middle kth level.Understand Ci,jSpan exist
Between 0 to 1, the similarity of its value and two profiles is inversely proportional, i.e. Ci,jSmaller, two profiles are more similar, Ci,jIt is bigger, two wheels
It is wide more dissimilar.
According to formula (17), a point P on profile p is first calculated1With on sample profile q Matching power flow a little, i.e.,
All points on the upper point of profile p and profile q are matched, the matrix of 1 × n size can be obtained, retain this square
The minimum value of element is as point P in battle array1Matching power flow, have found P equivalent on profile q1The point most matched.With identical
Method calculates all-pair on profile p and, in q Matching power flow, accurately can equally obtain the matrix of 1 × n size, calculate
The sum of whole elements in this matrix, a single non-vector index is accurately can obtain, in order to describe the two profiles
Between similitude.
Step VI, for power shovel, the number of bucket tooth is changeless on its scraper bowl, therefore can be according to bucket tooth
This invariant of number judges whether bucket tooth comes off as an index, if a known scraper bowl has N number of bucket tooth, if bucket tooth does not have
Have and come off, then the number M for the bucket tooth being detected in the picture should be equal to N, and the difference can that need to only compare M and N is sentenced
Whether disconnected bucket tooth comes off.
Finally it should be noted that:Above example is merely to illustrate the technical scheme of the application rather than to its protection domain
Limitation, although the application is described in detail with reference to above-described embodiment, the those of ordinary skill in the field should
Understand:Those skilled in the art can still carry out a variety of changes after reading the application to the embodiment of application, change or wait
With replacement, but these changes, modification or equivalent substitution, applying within pending claims.
Claims (8)
- A kind of 1. real-time vision detection method for electric bucket tooth missing, it is characterised in that:Comprise the following steps:Ith, the positive and negative samples of one group of electric bucket tooth are shot in advance;IIth, positive and negative samples HOG features are extracted;IIIth, HOG features are input in SVM grader and trained, obtain decision function;IVth, the picture of live captured in real-time is inputted, HOG feature extractions are carried out by detection window, then by svm classifier, complete To the Preliminary detection of electric bucket tooth;Vth, SC constraints are carried out to the electric bucket tooth testing result of acquisition, completes the accurate detection to electric bucket tooth;Specific steps are such as Under, a. image preprocessings and contours extract;B. SC features are calculated;C. shape similarity calculates;VIth, the accurate testing result of electric bucket tooth is compared with number of the electric bucket tooth set in advance without bucket tooth when lacking, Judge whether electric bucket tooth lacks.
- 2. the real-time vision detection method according to claim 1 for electric bucket tooth missing, it is characterised in that:The step Positive and negative samples of shooting electric bucket tooth in advance in rapid I, and preset number of the electric bucket tooth without bucket tooth when coming off, during shooting The positive and negative samples of monodentate should be obtained.
- 3. the real-time vision detection method according to claim 2 for electric bucket tooth missing, it is characterised in that:The step Rapid II includes:Step 1, the positive and negative samples to one group of electric bucket tooth of collection carry out image preprocessing, and image preprocessing includes gray processing Corrected with Gamma;Step 2, the image gradient for calculating electric bucket tooth positive and negative samples;Step 3, gradient direction statistic histogram is calculated, obtain HOG features.
- 4. the real-time vision detection method according to claim 3 for electric bucket tooth missing, it is characterised in that:The step In rapid II, Gamma correction calculation formula (1) are as followsY (x, y)=I (x, y)γFormula (1)Wherein I (x, y) represents gray scale of the input picture in point (x, y) place pixel, and Y (x, y) represents the pixel after correcting Gray value, take γ=0.5;The calculation formula of the image gradient size and Orientation for the electric bucket tooth positive and negative samples extracted is as follows:Gx(x, y)=H (x+1, y)-H (x-1, y) formula (2)Gy(x, y)=H (x, y+1)-H (x, y-1) formula (3)Wherein GX(x,y)、GY(x, y) represents the Grad of x directions and y directions epigraph respectively, and H (x, y) represents certain point in image Gray value, G (x, y) represents the size of image point gradient vector, and α (x, y) represents the direction of this gradient vector;Gradient direction statistic histogram is calculated, obtains HOG features, specific implementation method comprises the following steps:A) cell factory cell size is selected, because detection window pixel size is 64 × 128, is herein divided into detection window 128 cell factories, the size of each cell factory is 8 × 8 pixels;B) each cell factory inside gradient direction histogram is calculated, computational methods are as follows:The range set of gradient direction is arrived into 180 degree for 0, and gradient direction is divided into 9 sections, then counts every in the cell factory The number that different gradient directions occur in individual pixel, the number that gradient direction occurs on each each pixel of cell factory is counted When, it is necessary to be multiplied by a weight coefficient to each pixel, take the size that the weight coefficient is each pixel gradient, each pixel Point calculation formula (6) of occurrence number in each gradient direction section is as follows:Wherein, α (x, y) be cell factory in certain point gradient direction, binkFor k-th section, Vk(x, y) represents pixel The number occurred in k-th gradient direction section;By above-mentioned calculating process, in each cell factory, by the gradient magnitude of each pixel according to different gradient directions Counted, the HOG characteristic vectors of the cell factory are precisely formed;C) several adjacent cell factories are formed into a description block block, to the system of cell factory all in description block Meter histogram feature vector is grouped together into the characteristic vector of description block, and by that analogy, the HOG for calculating all description blocks is special Sign vector;D) histogram in description block is normalized, calculation formula (7) is as follows:L2-norm:Wherein v represents characteristic vector, and ε represents the number of a very little, is taken as 0.000001, the feature of all description blocks after normalization Vector, which joins end to end, can form HOG features.
- 5. the real-time vision detection method according to claim 4 for electric bucket tooth missing, it is characterised in that:The step Rapid III calculation procedure is as follows:1) sample set { x of given training is seti,yi, i=1,2...n, x ∈ Rn, y ∈ { ± 1 }, x represents the spy of sample in formula Sign vector, y represents category label, if required optimal hyperlane is:H:wTX+b=0;2) Lagrange multiplier α value is calculated, calculation formula is as follows:Bring α values into formula (9), formula (10) can obtain hyperplane ω, b optimal solution;WhereinTherefore optimal classification plane ω is obtained*·x+b*=0, and optimal decision function is:
- 6. the real-time vision detection method according to claim 5 for electric bucket tooth missing, it is characterised in that:The step Rapid IV includes:Step 1) setting camera candid photograph image temporal interval, the working region real-time grasp shoot image using camera to electric bucket tooth, And by the image transmitting captured to computer;Step 2) detects to the every piece image captured, the detection window used according to 10 pixels step-length in the picture Slide until covering entire image, often slides the HOG features for once just extracting image in detection window, then, by positive and negative template The decision function obtained by SVM classifier training is judged characteristic vector, whether to determine the image in window For electric bucket tooth target, and it will determine that result is identified in the picture for positive window, the like, wait slip detection to terminate The window for the expression same target that overlaps is combined again afterwards, completes the Preliminary detection to electric bucket tooth.
- 7. the real-time vision detection method for being used for electric bucket tooth and lacking stated according to claim 6, it is characterised in that:The step V includes:A. the image in detection window is pre-processed and extracts profile, comprised the following steps:1) bianry image is converted the image into, calculation formula (12) is as follows:Wherein, T1、T2For threshold value set in advance, scope is 0~255;2) boundary tracking algorithm is used, extracts objective contour;B. SC features are calculated, are comprised the following steps:1) by extracting profile and the point on profile being sampled, a positional information for containing profile up-sampling point is obtained Data set P={ p1,p2...pn, some point p that first selected point is concentrated1(x1,y1) polar limit is used as, calculate remaining N-1 point pn(xn,yn) and p1(x1,y1) vectorial size and Orientation is formed, similarly, then other points are used as pole using in set P Origin calculates angle and distance information successively, and the matrix R and A of accurate available two n × (n-1) size, it is stored The distance and angle information of all sampled points, calculation formula are as follows on the profile:2) all range information r are normalized using experience densimetry, and to the r after normalizationnorCarry out logarithm Conversion, is accurately mapped to log-polar (log by each point (x, y) in rectangular coordinate systemr, θ) in, normalize formula (15) it is as follows:3) polar coordinate system is divided into m × n subinterval, wherein m is distance parameter, and n is angle parameter, and statistics falls into every afterwards The number of profile point in individual subinterval, so as to obtain SC statistic histograms;Similarly, respectively using difference as origin, can calculate The SC features of whole profile;Calculation formula (16) is as follows:hi(k)={ q ≠ pi:(q-pi) ∈ bin (k) formula (16)Wherein k=1,2 ... K, K=m × n, represent k-th of component in histogram, PiRepresent that point concentration is used as polar coordinates pole The point of point, q represent other points, (q-pi) ∈ bin (k) represent relative to pi, point q belongs to k-th of subinterval;C. shape similarity calculates, and formula (17) is as follows:Wherein piRepresent the SC histograms using point i as origin, q on a profile pjRepresent on another profile q using point j as origin SC histograms;Cost=C (pi, qj) represent the distance between the two statistic histograms, that is, the matching generation of two profiles Valency;hiAnd h (k)j(k) two shape histogram p are represented respectivelyiAnd qjThe value of middle kth level, it is known that Ci,jSpan 0 to 1 Between, the similarity of its value and two profiles is inversely proportional, i.e. Ci,jSmaller, two profiles are more similar, Ci,jBigger, two profiles are got over It is dissimilar;According to formula (17), a point P on profile p is first calculated1With on sample profile q Matching power flow a little, i.e., by profile All points on the upper point of p and profile q are matched, and can obtain the matrix of 1 × n size, are retained first in this matrix The minimum value of element is as point P1Matching power flow, have found P equivalent on profile q1The point most matched, is counted in the same way Calculating all-pair on profile p, in q Matching power flow, accurately can equally obtain the matrix of 1 × n size, calculate this square The sum of whole elements in battle array, a single non-vector index is accurately can obtain, in order to describe phase between the two profiles Like property.
- 8. the real-time vision detection method for being used for electric bucket tooth and lacking stated according to claim 7, it is characterised in that:The step In VI, judge that the method whether electric bucket tooth lacks is as follows, if a known scraper bowl has N number of bucket tooth, if bucket tooth does not come off, The number M for the bucket tooth being detected in the picture should be equal to N, only need to compare M and N difference it may determine that whether bucket tooth takes off Fall.
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