CN104048969A - Tunnel defect recognition method - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000007547 defect Effects 0.000 title claims abstract description 39
- 238000001514 detection method Methods 0.000 claims abstract description 18
- 230000004927 fusion Effects 0.000 claims abstract description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 39
- 201000010099 disease Diseases 0.000 claims description 38
- 238000006073 displacement reaction Methods 0.000 claims description 19
- 238000003860 storage Methods 0.000 claims description 14
- 230000006835 compression Effects 0.000 claims description 10
- 238000007906 compression Methods 0.000 claims description 10
- 238000003384 imaging method Methods 0.000 claims description 8
- 239000000284 extract Substances 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 238000005520 cutting process Methods 0.000 claims description 5
- 230000010339 dilation Effects 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 5
- 238000003709 image segmentation Methods 0.000 claims description 5
- 230000008595 infiltration Effects 0.000 claims description 5
- 238000001764 infiltration Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000007797 corrosion Effects 0.000 claims description 4
- 238000005260 corrosion Methods 0.000 claims description 4
- 238000003708 edge detection Methods 0.000 claims description 4
- 230000000877 morphologic effect Effects 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 3
- 238000011946 reduction process Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 description 5
- 238000000605 extraction Methods 0.000 description 4
- 238000005286 illumination Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 208000037656 Respiratory Sounds Diseases 0.000 description 2
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- 230000010354 integration Effects 0.000 description 2
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- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses a tunnel defect recognition method and a multi-image recognizing method. A linear array charge coupled device (CCD) and an image fusion technique are adopted to acquire and store digital images of a tunnel surface at a high speed, furthermore a digital image processing algorithm is adopted to perform characteristic classification modeling on known tunnel defects, a characteristic database is established for defect characteristic matching and defect recognition, and thus the defect detection efficiency and the accuracy are improved. The potential defect characteristics are repeatedly extracted by utilizing multiple detection means, the detection efficiency is ensured, and the accuracy in defect classification and the accuracy in parameter estimation are improved.
Description
Technical field
The present invention relates to image and extract and recognition technology, relate in particular to a kind of recognition methods of tunnel defect.
Background technology
In recent years, have relevant unit and scholar to propose robotization tunnel surface disease information acquisition system, this system is mainly to utilize optical image technology, and camera apparatus is installed on mobile platform, and subway tunnel is taken to high-definition image document image camera site.Obtain after subway tunnel high-definition image, then mass image data is carried out to artificial search and search tunnel defect.This system has mainly solved tunnel internal acquisition of information problem, and maintainer no longer needs to enter narrow tunnel and carefully investigates.200810235410.9 relate to the surface fatigue crack detecting method based on ccd image feature, and this scheme discloses and characterized the length of Crack Extension and the method for width, and reliable crack detecting method is provided.This scheme is applicable to the crack detection of miniature workpiece, and when characteristics of image storehouse is excessive, detection efficiency is low.
201210407674.4 relate to a kind of Vision Builder for Automated Inspection that gathers and process for road surface crack image, comprising: CCD camera, high speed acquisition module, image pretreatment module, image display, illumination module, image wireless transport module and motion-control module.This case is only enumerated physical arrangement, does not disclose the concrete technical scheme of crack detection.
201210213385.0 relate to the information of road surface extraction element based on two dimensional image and depth information, and it comprises two dimensional image extraction unit and depth image extraction unit, and this scheme need to be analyzed picture one by one, and data volume is huge, and treatment effeciency is low.
Summary of the invention
The technical problem to be solved in the present invention is to propose a kind of recognition methods of tunnel defect, can effectively identify infiltration, and crack and lining cutting such as peel off at the surperficial disease.
Technical scheme of the present invention is achieved in that
A recognition methods for tunnel defect, is characterized in that comprising:
Linear array CCD camera and displacement transducer are fixed on same detection platform,
In detection platform moving process, displacement transducer provides displacement signal, and linear array CCD camera obtains tunnel coloured image according to this displacement signal,
The image stream media data that linear array CCD camera obtains merges;
To coloured image piecemeal compression storage, coloured image is changed into gray level image, noise reduction process along tunnel bearing of trend;
Utilize Gradient edge detection algorithm and image-region gray feature to Image Segmentation Using, and make morphological dilations corrosion and binary conversion treatment, obtain contour feature;
The tunnel surface of normal tunnel surface and handmarking's disease is provided, repeats above-mentioned steps, obtain tunnel surface normal profile feature and disease contour feature, set up data characteristics storehouse;
Obtain the contour feature of tunnel surface to be monitored, carry out feature registration analysis with data characteristics storehouse, determine tunnel defect position and type;
Repeatedly imaging is carried out in disease position at known type, extracts disease contour feature, measuring and calculating disease parameter.
In the recognition methods of tunnel defect of the present invention, the noise-reduction method of image comprises figure image intensifying and filtering algorithm.
In the recognition methods of tunnel defect of the present invention, disease parameter comprises infiltration area, and area is peeled off in fracture length, width and lining cutting.
Many image-recognizing methods of tunnel defect, is characterized in that comprising the following steps:
Detection platform is installed multiple linear array CCD cameras, in moving process, linear laser device is to tunnel surface Emission Lasers line, displacement transducer provides displacement signal, linear array CCD camera obtains tunnel coloured image according to this displacement signal, on two images that laser rays obtains at two CCD cameras adjacent with laser instrument, all has imaging;
The image that utilizes infrared laser mark to obtain different CCD cameras splices fusion;
To image carry out greyscale transformation, hard-threshold is cut apart, and obtains two-value contour feature figure, detects the straight line of contour feature figure, mark lengths is greater than the straight line of setting value, and rectilinear coordinates are pos, and Image Mosaics interval is [pos – index, pos+index], index is width parameter between splice region;
Generate splicing coefficient parameter y
x=0.5*exp (0.5*x
2/ σ
2), wherein, x ∈ [index, index];
Stitching image
Wherein j is the coordinate on image, I
jdenotation coordination is the pixel line of j, I
1, I
2represent two adjacent width images, I
1jrepresent I
1upper coordinate is the pixel line of j, pos
maxrepresent I
1, I
2middle pos coordinate is larger one, pos
minrepresent I
1, I
2middle pos coordinate is less one, W=pos
max+ W
2-pos
minfor splicing the width of rear image, W
2for image I
2width;
After Image Mosaics completes, to coloured image piecemeal compression storage, utilize Gradient edge detection algorithm and image-region gray feature to Image Segmentation Using along tunnel bearing of trend, and make morphological dilations corrosion and binary conversion treatment, obtain contour feature;
The tunnel surface of normal tunnel surface and handmarking's disease is provided, repeats above-mentioned steps, obtain tunnel surface normal profile feature and disease contour feature, set up data characteristics storehouse;
Obtain the contour feature of tunnel surface to be monitored, carry out feature registration analysis with data characteristics storehouse, determine tunnel defect position and type, repeatedly imaging is carried out in known disease position and type, extract disease contour feature, measuring and calculating disease parameter.
In many image-recognizing methods of tunnel defect of the present invention, adopt Hough algorithm to detect the straight line of contour feature figure.
In many image-recognizing methods of tunnel defect of the present invention, pos is horizontal ordinate or ordinate.
In many image-recognizing methods of tunnel defect of the present invention, in contour feature figure, detected straight line should be less than 5 DEG C with the angle at least one limit of image, and two straight lines that are less than setting threshold ξ of adjusting the distance are regarded as repetition straight line, only retains wherein one.
In many image-recognizing methods of tunnel defect of the present invention, threshold xi is less than 10 pixels.
Implement the recognition methods of tunnel defect of the present invention, have following beneficial effect: detection platform can be used as mobile unit, for tunnel is carried out to robotization Real-Time Monitoring, monitoring mode is simple, convenient, efficient, has greatly saved personal monitoring's cost.Utilize line array CCD can realize high speed imaging and high speed storing, utilize Digital Image Processing algorithm to carry out tagsort modeling to known tunnel defect, set up property data base, improved disease detection efficiency and accuracy.Utilize repeated detection means to repeat to extract Disease Characters to potential disease, when ensureing detection efficiency, improved the accuracy of disease classification and the degree of accuracy of estimated parameter.
Subway tunnel is due to the repeated layout of its tunnel segment structure, the inapplicable method for registering images with based on characteristic matching.Adopt high strength wire laser instrument to make aid mark, identify the position of laser rays imaging light on camera by image detection algorithm, utilize this mark position to do splicing to tunnel photographic images and merge.Near laser rays, adopt the average mode of pixel weighting coefficient, obtain the even image of splicing.
Brief description of the drawings
Fig. 1 is the system chart of the recognition methods of tunnel defect of the present invention;
Fig. 2 is the picture splicing schematic diagram of the recognition methods of tunnel defect of the present invention;
Fig. 3 is that two images are at splicing charts for finned heat corresponding to splicing regions
Fig. 4 is a kind of shooting schematic diagram of the disease that seeps water;
Fig. 5 is the disease contour feature of Fig. 4;
Fig. 6 is a kind of shooting schematic diagram of crackle disease;
Fig. 7 is the disease contour feature of Fig. 5.
Embodiment
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 clearly and completely described.
As Fig. 1,2, the recognition methods of tunnel defect of the present invention, can be made up of main control unit, linear array CCD camera, image compression unit, image storage unit, isochronous controller, displacement transducer etc.The input end of this isochronous controller is connected to described main control unit, output terminal is connected to described image compression unit, the output terminal of this image compression unit is connected to described linear array CCD camera, the input end of this image storage unit is also connected to described image storage unit, and the output terminal of this image storage unit is connected to described main control unit.Displacement transducer provides raw bits shifting signal, capture view data by main control unit, isochronous controller, image compression unit control line array CCD camera successively, the displacement signal that this view data is communicated with this moment by linear array CDD camera offers image compression unit, and image, displacement data are sent to image storage unit by image compression unit.View data is offered main control unit by image storage unit, and main control unit splices, analyzes image, obtains disease position.In addition, for ensureing brightness of image, illumination compensation system can also be set, it can comprise area source.Movable storage device can be so that by the data reading of the storages such as image storage unit.
The recognition methods of tunnel defect of the present invention, mainly comprises the steps:
S1: obtain image.
Utilize multiple high speed linear array CCD cameras, displacement transducer and illumination compensation auxiliary lighting system to obtain tunnel internal high-definition image, obtain image I, and by fixing unit length, image block is compressed to storage { I along tunnel bearing of trend
1, I
2... I
n, n presentation video piecemeal number.Fig. 4 is a kind of shooting results of disease.
S2: by I
i, i ∈ 1,2 ..., and n} is converted into gray level image, and carries out denoising, and denoising result is
Utilize histogram equalization and gaussian filtering method, to I
i, i ∈ 1,2 ..., n} strengthens and filtering, reduces illumination effect and picture noise.The discrete Gaussian filter function of two dimension zero-mean
wherein σ is noise variance.This wave filter has good inhibition to the noise of the Normal Distribution in image.Image filtering result is designated as
S3: right
carry out image and cut apart, extract coarse contour feature
and gray feature
Image segmentation algorithm is selected gradient operator
wherein, G
x, G
ybe respectively through image horizontal and that longitudinal edge detects, B
x, B
yfor 3*3 parameter matrix.Coarse contour is characterized as
the half-tone information in image outline region is as gray feature
fig. 5 is gray-scale map, the disease contour feature of presentation graphs 4.
S4: to coarse contour feature
carry out morphology processing and to feature binaryzation, obtain
To coarse contour feature
carry out dilation erosion processing, coarse contour feature is pruned to refinement, obtain better segmentation effect, be designated as
right
carry out the binary conversion treatment of adaptive threshold, obtain the contour feature of image
fig. 6,7 represents wall crackle and contour feature thereof.
S5: set up tunnel surface normal profile feature and Disease Characters database E
1, E
2, E
3, E
4.
Utilize above step to carry out contour feature and gray feature extraction to testing tunnel, utilize a large amount of test results to set up normal tunnel surface profile property data base E
1, crack contour feature database E
2, infiltration contour feature database E
3and contour feature E is peeled off in lining cutting
4.
S6: monitoring tunnel surface
Utilize above-mentioned identical algorithms to extract tunnel surface contour feature and gray feature, by comparing with known type contour feature database, carry out contour feature classification, obtain current monitoring tunnel surface contour feature type, and improve classification accuracy by repeated detection.
S7: tunnel surface disease calculation of parameter
Utilize disease contour feature to estimate relevant disease parameter: infiltration area, area etc. is peeled off in fracture width and lining cutting.
In the present invention, tunnel inner wall is arc, and area is large, and single camera cannot obtain all images in whole tunnel, needs multiple CCD cameras to coordinate and realizes.The image that CCD camera obtains can intersect mutually, therefore obtains after image at single camera, need to splice image.Therefore obtain after image at single camera, preferably carry out following steps:
To image carry out greyscale transformation, hard-threshold is cut apart, obtain two-value contour feature figure, adopt Hough algorithm to detect the straight line of contour feature figure, straight line should be less than 5 DEG C with the angle at least one limit of image, two straight lines that are less than setting threshold ξ of adjusting the distance are regarded as repetition straight line, only retain wherein one.Mark lengths is greater than the straight line of setting value, and rectilinear coordinates are pos (horizontal ordinate that pos is straight line or ordinate), and Image Mosaics interval is [pos – index, pos+index], and index is width parameter between splice region.Threshold xi is less than 10 pixels.
Generate splicing coefficient parameter y
x=0.5*exp (0.5*x
2/ σ
2), wherein, x ∈ [index, index], σ=20; Stitching image
Wherein j is the coordinate on image, I
jdenotation coordination is the pixel line of j, I
1, I
2represent two adjacent width images, I
1jrepresent I
1upper coordinate is the pixel line of j, pos
maxrepresent I
1, I
2middle pos coordinate is larger one, pos
minrepresent I
1, I
2middle pos coordinate is less one, W=max (pos1, pos2)+W
2-min (pos1, pos2) is the width of composograph.I
1, I
2the laser rays position pos that two width images are corresponding is different, is divided into pos1, pos2; For larger pos value (pos
max) figure we retain its left half figure, the figure (pos of less pos value
min) we retain right half part, the size of composograph is: max (pos1, pos2)+W
2-min (pos1, pos2), the image coordinate system after synthesizing is consistent with left half-image.Outside integration region, stitching image is respectively at I
1, I
2unanimously, in integration region (interval [pos – index, pos+index]), stitching image is by I
1, I
2being multiplied by respectively its splicing parameter forms.The value of splicing parameter changes along with the variation of coordinate j, and Fig. 3 has shown the splicing charts for finned heat in Image Mosaics region.
The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any amendment of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (9)
1. a recognition methods for tunnel defect, is characterized in that comprising:
Linear array CCD camera and displacement transducer are fixed on same detection platform,
In detection platform moving process, displacement transducer provides displacement signal, and linear array CCD camera obtains tunnel coloured image according to this displacement signal,
The image stream media data that linear array CCD camera obtains merges;
To coloured image piecemeal compression storage, coloured image is changed into gray level image, noise reduction process along tunnel bearing of trend;
Utilize Gradient edge detection algorithm and image-region gray feature to Image Segmentation Using, and make morphological dilations corrosion and binary conversion treatment, obtain contour feature;
The tunnel surface of normal tunnel surface and handmarking's disease is provided, repeats above-mentioned steps, obtain tunnel surface normal profile feature and disease contour feature, set up data characteristics storehouse;
Obtain the contour feature of tunnel surface to be monitored, carry out feature registration analysis with data characteristics storehouse, determine tunnel defect position and type;
Repeatedly imaging is carried out in disease position at known type, extracts disease contour feature, measuring and calculating disease parameter.
2. the recognition methods of tunnel defect according to claim 1, is characterized in that, the noise-reduction method of image comprises figure image intensifying and filtering algorithm.
3. the recognition methods of tunnel defect according to claim 1, is characterized in that, disease parameter comprises infiltration area, and area is peeled off in fracture length, width and lining cutting.
4. many image-recognizing methods of tunnel defect, is characterized in that comprising the following steps:
Detection platform is installed multiple linear array CCD cameras, in moving process, linear laser device is to tunnel surface Emission Lasers line, displacement transducer provides displacement signal, linear array CCD camera obtains tunnel coloured image according to this displacement signal, on two images that laser rays obtains at two CCD cameras of adjacent laser instrument, all has imaging;
The image stream media data that single linear array CCD camera is obtained merges, and obtains the image of fixed size;
The image that utilizes infrared laser mark to obtain different CCD cameras splices fusion;
After Image Mosaics completes, to coloured image piecemeal compression storage, utilize Gradient edge detection algorithm and image-region gray feature to Image Segmentation Using along tunnel bearing of trend, and make morphological dilations corrosion and binary conversion treatment, obtain contour feature;
The tunnel surface of normal tunnel surface and handmarking's disease is provided, repeats above-mentioned steps, obtain tunnel surface normal profile feature and disease contour feature, set up data characteristics storehouse;
Obtain the contour feature of tunnel surface to be monitored, carry out feature registration analysis with data characteristics storehouse, determine tunnel defect position and type, repeatedly imaging is carried out in known disease position and type, extract disease contour feature, measuring and calculating disease parameter.
5. many image-recognizing methods of tunnel defect according to claim 4, is characterized in that, described Image Mosaics merges, and comprises the following steps:
Two width images of the same position that adjacent C CD camera is obtained carry out greyscale transformation, hard-threshold is cut apart, obtain two-value contour feature figure, detect the straight line of contour feature figure, mark lengths is greater than the straight line of setting value, linear position coordinate is pos, Image Mosaics interval is [pos – index, pos+index], and index is width parameter between splice region;
Generate splicing coefficient parameter y
x=0.5*exp (0.5*x
2/ σ
2), wherein, x ∈ [index, index];
Stitching image
Wherein j is the coordinate on stitching image, I
jdenotation coordination is the pixel line of j, I
1, I
2represent two adjacent width images, I
1jrepresent I
1upper coordinate is the pixel line of j, pos
maxrepresent I
1, I
2middle pos coordinate is larger one, pos
minrepresent I
1, I
2middle pos coordinate is less one, W=pos
max+ W
2-pos
minfor splicing the width of rear image, W
2for image I
2width;
6. many image-recognizing methods of tunnel defect according to claim 4, is characterized in that, adopt Hough algorithm to detect the straight line of contour feature figure.
7. many image-recognizing methods of tunnel defect according to claim 4, is characterized in that, Pos is horizontal ordinate or ordinate.
8. many image-recognizing methods of tunnel defect according to claim 4, it is characterized in that, in contour feature figure, detected straight line should be less than 5 DEG C with the angle at least one limit of image, two straight lines that are less than setting threshold ξ of adjusting the distance are regarded as repetition straight line, only retain wherein one.
9. many image-recognizing methods of tunnel defect according to claim 4, is characterized in that, threshold xi is less than 10 pixels.
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