CN106327499A - Oil stain image recognition based on edge point self-similarity and TEDS system - Google Patents
Oil stain image recognition based on edge point self-similarity and TEDS system Download PDFInfo
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- 238000000034 method Methods 0.000 claims description 11
- 238000002372 labelling Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000003708 edge detection Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 238000012216 screening Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 10
- 230000004888 barrier function Effects 0.000 description 2
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Abstract
The invention provides oil stain image recognition based on edge point self-similarity, comprising the steps of acquiring edge points of an image to be detected, allocating a reference direction for each edge point and calculating a feature vector thereof, and normalizing the feature vectors; calculating partial and global self-similarity values of each edge point on edge lines, and performing weighted combination on the partial and global self-similarity values to obtain a final self-similarity value of the edge point; acquiring a set of edge points with high self-similarity on the edge lines, and screening and marking irregular edge points with low self-similarity in the set according to a predetermined mode; calculating pixel intensity differences in partial neighborhoods of all the irregular edge points; setting a pixel intensity difference threshold, and marking the irregular edge points of which the pixel intensity differences in partial neighborhoods are greater than the set threshold as an oil stain image. The invention further provides a TEDS system using the recognition, which quickly and accurately recognizes oil stains on motor train units, avoids recognition errors and reduces the misjudgment rate.
Description
Technical field
The present invention relates to computer picture detection identification field, particularly relate to a kind of greasy dirt based on marginal point self-similarity
The identification of image and apply the TEDS system of this identification.
Background technology
At present, greasy dirt image, as interferogram picture, has in field of image detection and is specifically designed for stationary body surface and oil contaminant
Detection equipment and detection method, be mainly used in cleaning and the purification of object surface.
Chinese patent description CN104318556A discloses the recognition methods of a kind of greasy dirt interference region, this identification
Method is that picture first carries out pretreatment, then uses to enter greasy dirt defect image based on the morphologic detection method of notable line scanning
Row detection and localization, identifies greasy dirt interference region.The Clutter edge that this greasy dirt recognition methods detects is more, applies in EMUs event
In barrier detection, EMUs operation troubles Motion Image Detection system (TEDS system) is easy is greasy dirt image by fault detect, improves
False Rate.
Summary of the invention
The present invention proposes the identification of a kind of greasy dirt image based on marginal point self-similarity, it is possible to identify greasy dirt image, and
Solve high problem of judging by accident in motor-car fault detect in prior art.
The technical scheme is that and be achieved in that:
The identification of a kind of greasy dirt image based on marginal point self-similarity, comprises the following steps:
Step one: input image to be detected in a computer, utilizes canny edge detection algorithm to obtain all limits of this image
Edge point；
Step 2: classify all marginal points, similar marginal point belongs to an initial edge line of an image outline, obtains
Take all of initial edge line of image to be detected, distribute a reference direction to each marginal point, and extract each edge
The characteristic vector of point, and each characteristic vector is normalized；
Step 3: calculate each marginal point on every initial edge line according to the characteristic vector after each marginal point normalized
Local self-similarity value and overall self-similarity value, and by local self-similarity value and the weighted array of overall self-similarity value
As the self-similarity value that this marginal point is final；
Step 4: set a high threshold, obtains the marginal point higher than high threshold of the self-similarity value on every initial edge line
Set, reject the marginal point less than high threshold of self-similarity value on every initial edge line；
Step 5: calculate the self-similarity value of each marginal point marginal point closest with it in above-mentioned set, sets a low threshold
Value, obtains all of self-similarity value in set and, higher than the marginal point of Low threshold, will be less than the marginal point of Low threshold in this set
It is labeled as being formed the broken edge point of irregular image；
Step 6: classifying the marginal point being higher than Low threshold in step 5, same class marginal point forms the school of an image outline
Positive edge line, sets a length threshold to all of calibration edge line, obtains the calibration edge line less than this threshold value, by this school
Point on positive edge line is labeled as being formed the broken edge point of irregular image；
Image pixel intensities in the local neighborhood of all broken edge points obtained in step 7, calculation procedure five and step 6
Difference, sets image pixel intensities difference limen value, and the image pixel intensities difference in the local neighborhood of labelling each broken edge point is more than given threshold
The broken edge point of value is greasy dirt image.
Preferably, in the identification of described greasy dirt image based on marginal point self-similarity, basis in described step 3
Characteristic vector after each marginal point normalized calculates the self-similarity value that the marginal point on every initial edge line is final
Mode be: setWithFor any two marginal point on edge line, its characteristic vector is respectivelyWith, then
Any two marginal point on edge lineWithSimilarity be:, here in vector
Long-pending calculation is multiplied for vector corresponding element and is added, obtains two marginal pointsWithSimilarity；
Marginal pointLocal self-similarity value be: take this marginal pointAnd position adjacent with this marginal point on the edge line at place
Four marginal points in its both sides、、、, then marginal pointLocal self-similarity value be:
；
Marginal pointOverall self-similarity value be: assuming that total n marginal point, then marginal point on this edge lineEntirety
Self-similarity value is:
；
Set the weights of local self-similarity and overall self-similarity as, and,, by local self-similarity value and overall self-similarity value combination, then marginal pointSelf-similarity value
For:
；
Characteristic vector after normalizedScope between 0 to 1, then self-similarity value represents similar between 0 to 1
Degree, self-similarity value is to be the most dissimilar state when 0, is to be complete similar state when 1.
Preferably, in the identification of described greasy dirt image based on marginal point self-similarity, described step 2 is given every
The mode of one marginal point one reference direction of distribution is:
For any one marginal point, structure local neighborhood centered by current edge point, all pixels in calculating this neighborhood
Grad and direction, utilize Grad and the direction of all pixels in this neighborhood of statistics with histogram, comprise 0 in rectangular histogram
9 Nogata posts that the direction scope of ~ 180 degree is divided equally, divide equally and are incorporated on 9 Nogata posts for 180 ~ 360 degree；
Calculate each marginal point weight coefficient to adjacent both direction, calculate each edge further according to weight coefficient and Grad
Contribution weights are added to histogrammic each Nogata post at this marginal point place by the some contribution weights to adjacent both direction
On, direction, histogram peak place is the reference direction of this marginal point.
Preferably, in the identification of described greasy dirt image based on marginal point self-similarity, arbitrary in described step 2
The extracting mode of the characteristic vector of individual marginal point is:
Set any edge point as, the reference direction of this marginal point is, coordinate axes is rotated to reference direction；Seat after rotation
Mark system takes distance marginal point respectively along four orientationThe point of predetermined location of pixels、、、, construct with、、
、、Centered by 5 local neighborhood, calculate the Grad of each pixel and each pixel and the contribution of adjacent both direction weighed
Value；The directional spreding rectangular histogram of 5 local neighborhood of statistics, obtains 5 rectangular histograms
；The characteristic vector of this marginal point is:；
Finally the characteristic vector of each marginal point is normalized.
Preferably, in the identification of described greasy dirt image based on marginal point self-similarity, described step 7 calculates
The mode of the image pixel intensities difference in the local neighborhood of all broken edge points is:
Set arbitrary broken edge point as, construct withCentered by local neighborhood；Maximum picture in calculating this local neighborhood
Element intensity and the pixel of minimum pixel intensity element by force is poor。
A kind of TEDS system, including the identification of any of the above-described item greasy dirt based on marginal point self-similarity image, by labelling
Greasy dirt image be defaulted as the normal condition of EMUs, the not labelling when EMUs fault detect.
The invention have the benefit that in the present invention, first consider every initial edge line, calculate every according to characteristic vector
The self-similarity of the marginal point on initial edge line, is obtained from similar bigger marginal point set, rejects the limit that self-similarity is little
Edge point；Again in all marginal point set that self-similarity is bigger according to predetermined mode recalculate each marginal point from phase
Like property, it is obtained from the similarity marginal point set higher than Low threshold, increases the seriality of marginal point；To set is higher than low threshold
The marginal point of value reclassifies, and obtains calibration edge line；It is less than correcting in the point and set being less than length threshold on edge line
The point of Low threshold is labeled as broken edge point, and broken edge point constitutes irregular image；Then image pixel intensities difference limen is set
Value, the image pixel intensities difference in the local neighborhood of labelling each broken edge point is oil more than the broken edge point of given threshold value
Dirty image.The method first identifies relative to the irregular image in Background, then utilizes the characteristic of greasy dirt to identify greasy dirt figure
Picture, improves the accuracy of greasy dirt identification；And apply in TEDS system, can quick and precisely identify the greasy dirt image on car, fall
Low EMUs fault False Rate, improves the accuracy of TEDS system detection.
Accompanying drawing explanation
For the technical scheme being illustrated more clearly that in the embodiment of the present invention, in embodiment being described below required for make
Accompanying drawing be briefly described, it should be apparent that, below describe in accompanying drawing be only some embodiments of the present invention, for
From the point of view of those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain other according to embodiment
Accompanying drawing.
Fig. 1 is the image at a certain position of the EMUs gathered；
Fig. 2 is for identifying greasy dirt image in FIG.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Describe, it is clear that described embodiment is only a part of embodiment of the present invention rather than whole embodiments wholely.Based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under not making creative work premise
Embodiment, broadly falls into the scope of protection of the invention.
Embodiment 1: the identification of a kind of greasy dirt image based on marginal point self-similarity applied in TEDS system, including
Following steps:
Step one: input EMUs image to be detected in a computer, as it is shown in figure 1, utilize canny edge detection algorithm to obtain
Take all marginal points of this image；Detailed process is as follows:
1, it is gray level image by EMUs image procossing on computers；
2, gray level image is carried out Gaussian Blur to reduce the interference of picture noise；
3, Grad and the direction of each pixel in the image after noise reduction are calculated；
4, the Grad to each pixel carries out non-maxima suppression, tentatively obtains image border point set；5, dual threashold is used
Value method carries out edge connection, rejects false edge, completion emargintion, it is thus achieved that more accurate marginal point set.
Step 2: classify all marginal points, similar marginal point belongs to an initial edge of an image outline
Line, obtains all of initial edge line of image to be detected, distributes a reference direction to each marginal point, and extracts every
The characteristic vector of individual marginal point, and the characteristic vector of each marginal point is normalized；
The mode of each marginal point one reference direction of distribution is:
1, for any one marginal point, structure 8*8 neighborhood centered by current edge point, all pixels in calculating this neighborhood
Grad and direction, utilize Grad and the direction of all pixels in this neighborhood of statistics with histogram, comprise 0 in rectangular histogram
9 Nogata posts that the direction scope of ~ 180 degree is divided equally, 20 degree of each Nogata post, divide equally and be incorporated in 9 Nogata posts for 180 ~ 360 degree
On；
2, calculate each marginal point weight coefficient to adjacent both direction, calculate each limit further according to weight coefficient and Grad
Contribution weights are added to histogrammic each Nogata post at this marginal point place by the edge point contribution weights to adjacent both direction
On, direction, histogram peak place is the reference direction of this marginal point.
The extracting mode of the characteristic vector of each marginal point is:
1, set any edge point as, the reference direction of this marginal point is, coordinate axes being rotated to reference direction, coordinate becomes
It is changed to；Coordinate system after rotation takes distance marginal point respectively along four orientationIn advance
The point of fixed location of pixels、、、, construct with、、、、Centered by 5 8*8 neighborhoods, calculate each
The Grad of pixel, calculate each pixel to adjacent two sides
To contribution weights；
2, add up the directional spreding rectangular histogram of 5 8*8 neighborhoods, obtain 5 rectangular histograms
；The characteristic vector of this marginal point is:；
Finally the characteristic vector of each marginal point is normalized.
Step 3: calculate each limit on every initial edge line according to the characteristic vector after each marginal point normalized
The local self-similarity value of edge point and overall self-similarity value, and by local self-similarity value and the weighting of overall self-similarity value
Combine the self-similarity value final as marginal point；
The calculation of the final self-similarity value of the marginal point on every initial edge line is: setWithFor edge
Any two marginal point on line, its characteristic vector is respectivelyWith, then
Any two marginal point on edge lineWithSimilarity be:；Here in vector
Long-pending calculation is multiplied for vector corresponding element and is added, obtains two marginal pointsWithSimilarity；
Marginal pointLocal self-similarity value be: take this marginal pointAnd position adjacent with this marginal point on the edge line at place
Four marginal points in its both sides、、、, then marginal pointLocal self-similarity value be:
；
Marginal pointOverall self-similarity value be: assuming that total n marginal point, then marginal point on this edge lineEntirety
Self-similarity value is:
；
Set the weights of local self-similarity and overall self-similarity as, choose,, by local self-similarity value and overall self-similarity value combination, then marginal pointFinal self-similarity
Value is:
；
Characteristic vector after normalizedScope between 0 to 1, then self-similarity value represents similar between 0 to 1
Degree, self-similarity value is to be the most dissimilar state when 0, is to be complete similar state when 1.
Step 4: high threshold is set to 0.5, obtains the edge higher than 0.5 of the self-similarity value on every initial edge line
The set of point, rejects self-similarity value on every initial edge line and is less than the marginal point of high threshold.
Step 5: the self-similarity of any two marginal point in the set of calculation procedure four, is set to 0.1 by Low threshold,
Obtain all of self-similarity value marginal point higher than 0.1 in set, be labeled as being formed by the marginal point being less than 0.1 in this set
The broken edge point of irregular image.
Step 6: classifying the marginal point being higher than Low threshold in step 5, same class marginal point forms an image outline
Calibration edge line, to all of calibration edge line preseting length threshold value 12, obtain the edge line length calibration edge less than 12
Line, is labeled as being formed the broken edge point of irregular image by the point on this calibration edge line.
Pixel in the local neighborhood of all broken edge points obtained in step 7, calculation procedure five and step 6 is strong
It is poor to spend, and sets image pixel intensities difference limen value, and the image pixel intensities difference in the local neighborhood of labelling each broken edge point is more than given
The broken edge point of threshold value is greasy dirt image.
Set the marginal point of image pixel intensities difference limen value labelling irregular image as greasy dirt image concrete mode as: due to
Greasy dirt typically presents deeper color state, the shade being similar in image, has less gray value, and and peripheral region
Gray value difference is relatively big, and the image pixel intensities in therefore we calculate the local neighborhood of the marginal point on irregular image is poor, sets
Image pixel intensities difference limen value E=30, when the image pixel intensities difference in the local neighborhood of the marginal point of irregular image meets E > 30 time, mark
Remember that the marginal point on current irregular image is greasy dirt image, as shown in Figure 2.
Step 8, the reference picture of the EMUs to be detected extracted in image library, application standard picture method is in TEDS system
Image to be detected and reference picture are compared by system, the greasy dirt image identified in step 7 is defaulted as external interference
Factor, is not the malfunction of EMUs, and when EMUs fault detect, not labelling, reduction fault erroneous judgement, improves the event obtained
The degree of accuracy of barrier detection figure.
The neighborhood of above-mentioned appearance selects as the case may be, it is possible to elect other neighborhoods such as 8*16 as.Above-mentioned high threshold, low
Threshold value, length threshold and image pixel intensities difference limen value can be chosen according to the type of actually detected image.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Within god and principle, any modification, equivalent substitution and improvement etc. made, should be included within the scope of the present invention.
Claims (6)
1. the identification of a greasy dirt image based on marginal point self-similarity, it is characterised in that comprise the following steps:
Step one: input image to be detected in a computer, utilizes canny edge detection algorithm to obtain all limits of this image
Edge point；
Step 2: classify all marginal points, similar marginal point belongs to an initial edge line of an image outline, obtains
Take all of initial edge line of image to be detected, distribute a reference direction to each marginal point, and extract each edge
The characteristic vector of point, and each characteristic vector is normalized；
Step 3: calculate each marginal point on every initial edge line according to the characteristic vector after each marginal point normalized
Local self-similarity value and overall self-similarity value, and by local self-similarity value and the weighted array of overall self-similarity value
As the self-similarity value that this marginal point is final；
Step 4: set a high threshold, obtains the marginal point higher than high threshold of the self-similarity value on every initial edge line
Set, reject the marginal point less than high threshold of self-similarity value on every initial edge line；
Step 5: calculate the self-similarity value of each marginal point marginal point closest with it in above-mentioned set, sets a low threshold
Value, obtains all of self-similarity value in set and, higher than the marginal point of Low threshold, will be less than the marginal point of Low threshold in this set
It is labeled as being formed the broken edge point of irregular image；
Step 6: classifying the marginal point being higher than Low threshold in step 5, same class marginal point forms the school of an image outline
Positive edge line, sets a length threshold to all of calibration edge line, obtains the calibration edge line less than this threshold value, by this school
Point on positive edge line is labeled as being formed the broken edge point of irregular image；
Image pixel intensities in the local neighborhood of all broken edge points obtained in step 7, calculation procedure five and step 6
Difference, sets image pixel intensities difference limen value, and the image pixel intensities difference in the local neighborhood of labelling each broken edge point is more than given threshold
The broken edge point of value is greasy dirt image.
The identification of greasy dirt image based on marginal point self-similarity the most according to claim 1, it is characterised in that described step
The marginal point calculated on every initial edge line according to the characteristic vector after each marginal point normalized in rapid three is final
The mode of self-similarity value is: setWithFor any two marginal point on edge line, its characteristic vector is respectivelyWith, then
Any two marginal point on edge lineWithSimilarity be:, here in vector
Long-pending calculation is multiplied for vector corresponding element and is added, obtains two marginal pointsWithSimilarity；
Marginal pointLocal self-similarity value be: take this marginal pointOn the edge line at place adjacent with this marginal point and be positioned at
Four marginal points of its both sides、、、, then marginal pointLocal self-similarity value be:
；
Marginal pointOverall self-similarity value be: assuming that total n marginal point, then marginal point on this edge lineEntirety from
Similarity is:
；
Set the weights of local self-similarity and overall self-similarity as, and,, by local self-similarity value and overall self-similarity value combination, then marginal pointSelf-similarity value
For:
；
Characteristic vector after normalizedScope between 0 to 1, then self-similarity value represents similar journey between 0 to 1
Degree, self-similarity value is to be the most dissimilar state when 0, is to be complete similar state when 1.
The identification of greasy dirt image based on marginal point self-similarity the most according to claim 1, it is characterised in that described step
The mode distributing a reference direction to each marginal point in rapid two is:
For any one marginal point, structure local neighborhood centered by current edge point, all pixels in calculating this neighborhood
Grad and direction, utilize Grad and the direction of all pixels in this neighborhood of statistics with histogram, comprise 0 in rectangular histogram
9 Nogata posts that the direction scope of ~ 180 degree is divided equally, divide equally and are incorporated on 9 Nogata posts for 180 ~ 360 degree；
Calculate each marginal point weight coefficient to adjacent both direction, calculate each edge further according to weight coefficient and Grad
Contribution weights are added to histogrammic each Nogata post at this marginal point place by the some contribution weights to adjacent both direction
On, direction, histogram peak place is the reference direction of this marginal point.
The identification of greasy dirt image based on marginal point self-similarity the most according to claim 1, it is characterised in that described step
In rapid two, the extracting mode of the characteristic vector of any one marginal point is:
Set any edge point as, the reference direction of this marginal point is, coordinate axes is rotated to reference direction；In rotation
Coordinate system after Zhuaning takes distance marginal point respectively along four orientationThe point of predetermined location of pixels、、、, construct with、、、、Centered by 5 local neighborhood, calculate each pixel Grad and
Each pixel contribution weights to adjacent both direction；The directional spreding rectangular histogram of 5 local neighborhood of statistics,
Obtain 5 rectangular histograms；The characteristic vector of this marginal point is:；The finally feature to each marginal point
Vector is normalized.
The identification of greasy dirt image based on marginal point self-similarity the most according to claim 1, it is characterised in that described step
The mode calculating the difference of the image pixel intensities in the local neighborhood of all broken edges point in rapid seven is:
Set arbitrary broken edge point as, construct withCentered by local neighborhood；Maximum picture in calculating this local neighborhood
Element intensity and the pixel of minimum pixel intensity element by force is poor。
6. a TEDS system, it is characterised in that include that any one described in claim 1 to 5 is based on marginal point self-similarity
The identification of greasy dirt image, the greasy dirt image of labelling is defaulted as the normal condition of EMUs, when EMUs fault detect not
Labelling.
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| CN110428474A (en) * | 2019-06-21 | 2019-11-08 | 珠海格力电器股份有限公司 | Judgment method, terminal and the computer-readable medium whether kitchen ventilator needs to clean |
| CN113538340A (en) * | 2021-06-24 | 2021-10-22 | 武汉中科医疗科技工业技术研究院有限公司 | Target contour detection method and device, computer equipment and storage medium |
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