CN104730083A - Automatic detection method for joint twitching of steel wire rope core conveying belt - Google Patents
Automatic detection method for joint twitching of steel wire rope core conveying belt Download PDFInfo
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- 206010028347 Muscle twitching Diseases 0.000 title claims abstract description 19
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- 238000010606 normalization Methods 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- 238000000605 extraction Methods 0.000 claims abstract description 9
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Abstract
The invention discloses an automatic detection method for twitching of a steel wire rope core conveyor belt joint, which comprises the following steps: a. performing joint point detection on the joint image to be detected and the reference joint image; b. surf feature extraction is carried out on the image of the joint point to be detected, feature matching is carried out according to the feature value, a mapping relation between the joint image and a reference joint image is sought, and a normalization coefficient is obtained; c. the invention can ensure the calculation reliability of the normalized coefficient, so that even if the conveying belt deviates or changes in speed, as long as the joint points are correctly matched, the calculation accuracy of the distance of the joint points can be ensured according to the normalized coefficient, the detection accuracy of the twitching of the steel wire rope joint is improved, the problem of inaccurate calculation of the twitching of the steel wire rope joint in the conveying belt is solved, and the twitching automatic detection method can adapt to the speed change and the translation of the conveying belt; has important significance for guaranteeing the safe operation of the transportation system.
Description
Technical field
The present invention relates to a kind of conveying band joint detection method, particularly relate to a kind of steel cable core conveying belt joint and twitch automatic testing method.
Background technology
At coal mine conveying belt transport field, the steel rope core conveying belt of long distance has a lot of vulcanized joint, and joint is the link that whole piece steel rope core conveying belt tension is the weakest.With regard to steel rope core conveying belt fracture accident in existing coal enterprise, great majority are twitched due to joint and are not caused by Timeliness coverage.Therefore, steel rope core conveying belt joint situation of twitching is detected and predicted, has important realistic meaning to what avoid broken belt accident.Domestic multiple university and company carry out twitch to radioscopy conveying band joint image and analyze, but all do not have in consideration actual shipment environment in these technology, can be there is velocity variations, shake, sideslip due to load carried difference in conveying belt, translation, the distortion such as flexible will occur for the same joint image of detector secondary acquisition.If directly carry out joint to the joint image of secondary acquisition to twitch calculating, there will be very big error undoubtedly.Cause occurring wrong report or failing to report, impact safety in production, even because the joint of having twitched is not by Timeliness coverage process, conveyor belt breakage accident may occurs, cause economic loss.
Summary of the invention
In view of this, the object of this invention is to provide one and can adapt to conveying belt velocity variations, the steel cable core conveying belt joint of translation twitches automatic testing method, treat detection tabs image and benchmark joint image carries out tap points detection and feature extraction, characteristic matching is carried out according to eigenwert, seek joint mapping relations between joint image and benchmark joint image, obtain normalization coefficient, carry out twitch analytical calculation again according to normalization coefficient to obtain twitching distance, even if therefore belt deflection or velocity variations, as long as tap points coupling is correct, just can guarantee that tap points distance calculates accurately according to normalization coefficient.
Steel cable core conveying belt joint of the present invention twitches automatic testing method, comprises the following steps: a, treat detection tabs image and benchmark joint image carries out tap points detection; B, surf feature extraction is carried out to tap points image to be checked, carry out characteristic matching according to eigenwert, seek mapping relations between joint image and benchmark joint image, obtain normalization coefficient; C, to carry out twitch analytical calculation according to normalization coefficient again and obtain twitching distance;
Further, described step a specifically comprises the following steps: a1, gray level correction; A2, gaussian filtering; A3, Iamge Segmentation; A4, image filtering eliminate isolated point; A5, Contour extraction locator sub point;
Further, described step b specifically comprises the following steps: b1, tap points surf eigenwert describe; B2, benchmark image and Image Feature Point Matching to be detected; B3, calculate image joint length to be detected; B4, obtain normalization coefficient k;
Further, described step c specifically comprises the following steps: c1, calculate image butt junction distance to be detected; C2, according to butt junction Distance geometry label, try to achieve twitch distance.
The invention has the beneficial effects as follows: steel cable core conveying belt joint of the present invention twitches automatic testing method, adopt tap points characteristic matching mode, even if belt deflection or velocity variations, as long as tap points coupling is correct, calculate according to normalization and just can guarantee that tap points distance calculates accurately, improve wireline adapter and twitch accuracy of detection, solve conveying belt inner wire rope socket and twitch the inaccurate problem of calculating, conveying belt velocity variations and translation can be adapted to; To ensureing that the safe operation of transportation system is significant.
Accompanying drawing explanation
Fig. 1 is joint image schematic diagram;
Fig. 2 is for eliminating isolated point image schematic diagram;
Fig. 3 is joint distance schematic diagram.
Embodiment
Fig. 1 is joint image schematic diagram, and Fig. 2 is for eliminating isolated point image schematic diagram, and Fig. 3 is joint distance schematic diagram; As shown in the figure: the steel cable core conveying belt joint of the present embodiment twitches automatic testing method, comprises the following steps: a, treat detection tabs image and benchmark joint image carries out tap points detection; B, surf feature extraction is carried out to tap points image to be checked, carry out characteristic matching according to eigenwert, seek mapping relations between joint image and benchmark joint image, obtain normalization coefficient; C, to carry out twitch analytical calculation according to normalization coefficient again and obtain twitching distance.
In the present embodiment, described step a specifically comprises the following steps: a1, gray level correction; A2, gaussian filtering; A3, Iamge Segmentation; A4, image filtering eliminate isolated point; A5, Contour extraction locator sub point; In described step a1, because detector pixel is penetrated strong and weak different by X ray illumination, intensity profile is uneven, image both sides gray-scale value is lower, need butt junction image to carry out gray-level correction, adopt piecewise linear transform to map in a certain tonal range, original image f (x, y) tonal range is [min, max], after conversion, image g (x, y) tonal range extends to [a, b], its greyscale transformation is expressed as:
Then Gaussian smoothing filter stress release treatment is adopted; In step a2, joint image shown in Figure 1, it is larger that tap points is adjacent a gray value differences in the vertical direction, wherein top connection point gray-scale value is less than neighbor grayscale value, lower sub point gray-scale value is greater than neighbor grayscale value, Y-direction difference is done to each pixel and can realize Iamge Segmentation, note vertical direction difference is DY=P (x, y)-P (x, y+1), P (x, y) be a certain pixel gray-scale value, P (x, y+1) is vertical direction neighbor pixel gray-scale value, thr is a threshold value, order
Gray-scale value is 128 and thinks top connection point, and gray-scale value is 255 and thinks lower sub point, and thr is determined by joint gradation of image scope after gray-level correction and picture contrast, if joint picture contrast is comparatively large, then threshold value can be chosen larger, in actual applications, affected by noise, usually there is transition portion in brightness/gray scale change, if directly calculated with the gray-scale value of neighborhood point itself, threshold value is determined more difficult, in order to effectively avoid this situation, before applying equation (1) calculates, the value of tap points and its a neighbor pixel gray scale are done average, three neighbor pixel gray scales are adopted to do average, carry out Y-direction difference again, note DY=(P (x-1, y)+P (x, y)+P (x+1, y))/3-P (x, y+1), in step a3, after the segmentation of butt junction difference, also there is the less noise isolated point of a lot of area, these isolated points are needed to get rid of, the method eliminating isolated point is a lot, as the medium filtering process of 5 × 5 can be carried out, but tap points area is also smaller sometimes, be taken as isolated point to eliminate, by observing tap points pixel distribution, only filtering process is carried out to isolated point X-direction, make P (x, y)=0, as P (x-1, y)=0 and P (x+1, y)=0, elimination isolated point image shown in Figure 2, finally by all upper and lower tap points in Contour extraction target area, calculate the center-of-mass coordinate of tap points.
In the present embodiment, described step b specifically comprises the following steps: b1, tap points surf eigenwert describe; B2, benchmark image and Image Feature Point Matching to be detected; B3, calculate image joint length to be detected; B4, obtain normalization coefficient k; In step b1, using the tap points extracted as key feature points, set up the distribution (local message integration) of single order Haar small echo response in the x and y direction, algorithm steps is as follows:
1. around selected characteristic point, 24 × 24 regions, as region-of-interest, and become this region segmentation the subregion of 4 × 4, and in order to retain some spatial informations, subregion can overlap more, and subregion is 9 × 9 sizes;
2., in each subregion, in the dot matrix of 9 × 9, calculate Haar little wave response dx, dy, and carry out Gauss's weighting;
3. respectively to the dx of subregion, dy, | dx|, | dy| response summation also normalized vector.Obtain feature interpretation [∑ dx, ∑ dy, ∑ | dx|, ∑ | dy|], each like this unique point is exactly 64 dimensional vectors;
In step b2, the Euclidean distance of Calculation Basis image all joint characteristics point and all tap points of image to be detected
wherein, (x
1, x
2x
64) be the proper vector of certain joint characteristics point of benchmark image, (x
1', x
2' ... x
64') proper vector of certain joint characteristics point of image to be detected; Calculate its arest neighbors and time neighbour's Euclidean distance ratio carrys out match query point, when ratio is less than certain threshold value, then think two Point matching;
In step b3, after b1 step coupling, obtain the match point set of image to be detected, [(x
1,y
1), (x
2,y
2) ... (x
n,y
n)], observe the joint image after segmentation, setting space constraint condition, be easy to obtain joint tap points and tap points bottom topmost, topmost the top a line white point in tap points and Fig. 2, they are all contained in match point set [(x
1,y
1), (x
2,y
2) ... (x
n,y
n)] in, make top connection point be
lower sub point
k is top connection point number, and l is lower sub point number; Average multiple joint characteristics point coordinate asks joint length, and joint length to be detected is expressed as: L
c=((y
b1+ y
b2+ ... + y
bl)/l-(y
t1+ y
t2+ ... + y
tk)/k) × PixWidth, wherein PixWidth is pel spacing, can be also in like manner L in the hope of the order of benchmark image joint length
r, finally, in step b4, calculate normalization coefficient k=L
c/
r.
In the present embodiment, described step c specifically comprises the following steps: c1, calculate image butt junction distance to be detected; C2, according to butt junction Distance geometry label, try to achieve twitch distance; Wherein in step c1, obtain top connection by tool joint monitor and be designated as U (x
i, y
i), lower sub is designated as D (x
j, y
j), wherein x
i, y
irepresent the coordinate of certain joint;
If meet following space length constraint condition, then think that corresponding upper and lower 2 tap points are a butt junction; | x
i-x
j| < σ
x(2)
|y
i-y
j|＜σ
y(3)
σ
xand σ
yfor level thresholds and vertical threshold, decided by conveying belt specification in embody rule, usual σ
xslightly larger than the mean distance between two Steel cords, σ
yslightly larger than the standard vertical distance between upper and lower tap points; After obtaining coupling butt junction, calculate the distance between joint, be expressed as:
wherein PixWidth is pel spacing, is determined by detector precision;
According to the feature interpretation of these two tap points, dock first unique label to this, like this with regard to the butt junction one_to_one corresponding in all butt junctions in guarantee reference image and image to be detected; In step c2, the distance of Calculation Basis image label butt junction identical with image to be detected respectively, see Fig. 3, is designated as R respectively
iand C
i, wherein (i=0,1 ... N), N is butt junction number, and i is butt junction numbering, then distance L is twitched in butt junction
i=C
i× k-R
i, mean twitch distance is L
i/ N.
What finally illustrate is, above embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although with reference to preferred embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that, can modify to technical scheme of the present invention or equivalent replacement, and not departing from aim and the scope of technical solution of the present invention, it all should be encompassed in the middle of right of the present invention.
Claims (4)
1. steel cable core conveying belt joint twitches an automatic testing method, it is characterized in that: comprise the following steps: a, treat detection tabs image and benchmark joint image carries out tap points detection; B, surf feature extraction is carried out to tap points image to be checked, carry out characteristic matching according to eigenwert, seek mapping relations between joint image and benchmark joint image, obtain normalization coefficient; C, to carry out twitch analytical calculation according to normalization coefficient again and obtain twitching distance.
2. steel cable core conveying belt joint according to claim 1 twitches automatic testing method, it is characterized in that: described step a specifically comprises the following steps: a1, gray level correction; A2, gaussian filtering; A3, Iamge Segmentation; A4, image filtering eliminate isolated point; A5, Contour extraction locator sub point.
3. steel cable core conveying belt joint according to claim 1 twitches automatic testing method, it is characterized in that: described step b specifically comprises the following steps: b1, tap points surf eigenwert describe; B2, benchmark image and Image Feature Point Matching to be detected; B3, calculate image joint length to be detected; B4, obtain normalization coefficient k.
4. steel cable core conveying belt joint according to claim 1 twitches automatic testing method, it is characterized in that: described step c specifically comprises the following steps: c1, calculate image butt junction distance to be detected; C2, according to butt junction Distance geometry label, try to achieve twitch distance.
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Cited By (7)
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|---|---|---|---|---|
| CN106296700A (en) * | 2016-08-15 | 2017-01-04 | 南京工程学院 | Detection method twitched by a kind of steel cord conveyor belt joint |
| CN106706238A (en) * | 2016-11-22 | 2017-05-24 | 山西大学 | Steel wire rope core conveying belt connector lap marking and identifying method |
| CN107423744A (en) * | 2017-03-23 | 2017-12-01 | 北京环境特性研究所 | The Seam tracking and damage positioning method of steel rope core conveying belt |
| CN107728223A (en) * | 2017-09-13 | 2018-02-23 | 太原理工大学 | A kind of steel rope core conveying belt joint twitches online test method |
| CN108550135A (en) * | 2018-03-07 | 2018-09-18 | 天津工业大学 | A kind of steel cable core conveying belt joint elongation automatic testing method based on x-ray image |
| CN110288562A (en) * | 2019-05-16 | 2019-09-27 | 枣庄学院 | A kind of steel cable core conveying belt joint twitch detection method based on x-ray image |
| CN114235825A (en) * | 2022-02-24 | 2022-03-25 | 武汉祥文钢材制品有限公司 | Steel wire rope quality detection method based on computer vision |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106296700A (en) * | 2016-08-15 | 2017-01-04 | 南京工程学院 | Detection method twitched by a kind of steel cord conveyor belt joint |
| CN106296700B (en) * | 2016-08-15 | 2019-02-15 | 南京工程学院 | A kind of steel cord conveyor belt connector twitch detection method |
| CN106706238B (en) * | 2016-11-22 | 2019-01-29 | 山西大学 | Steel cable core conveying belt joint overlap joint label and recognition methods |
| CN106706238A (en) * | 2016-11-22 | 2017-05-24 | 山西大学 | Steel wire rope core conveying belt connector lap marking and identifying method |
| CN107423744A (en) * | 2017-03-23 | 2017-12-01 | 北京环境特性研究所 | The Seam tracking and damage positioning method of steel rope core conveying belt |
| CN107728223A (en) * | 2017-09-13 | 2018-02-23 | 太原理工大学 | A kind of steel rope core conveying belt joint twitches online test method |
| CN108550135A (en) * | 2018-03-07 | 2018-09-18 | 天津工业大学 | A kind of steel cable core conveying belt joint elongation automatic testing method based on x-ray image |
| CN108550135B (en) * | 2018-03-07 | 2021-07-30 | 天津工业大学 | Automatic detection method for elongation of steel wire rope core conveyor belt joint based on X-ray image |
| CN110288562A (en) * | 2019-05-16 | 2019-09-27 | 枣庄学院 | A kind of steel cable core conveying belt joint twitch detection method based on x-ray image |
| WO2020228111A1 (en) * | 2019-05-16 | 2020-11-19 | 枣庄学院 | X-ray image-based spasm detection method for steel cable core conveyor belt connector |
| CN110288562B (en) * | 2019-05-16 | 2023-01-17 | 枣庄学院 | Method for detecting joint twitching of steel wire rope core conveying belt based on X-ray image |
| CN114235825A (en) * | 2022-02-24 | 2022-03-25 | 武汉祥文钢材制品有限公司 | Steel wire rope quality detection method based on computer vision |
| CN114235825B (en) * | 2022-02-24 | 2022-05-17 | 武汉祥文钢材制品有限公司 | Steel wire rope quality detection method based on computer vision |
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