CN110363706A - A kind of large area bridge floor image split-joint method - Google Patents
A kind of large area bridge floor image split-joint method Download PDFInfo
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Abstract
The invention discloses a kind of large area bridge floor image split-joint methods.With the development of Computer Vision Detection Technique, gradually appears and image detection is applied in bridge machinery engineering practice.But since pontic space is larger, according to long-range shooting sampling, it will receive the limitation of resolution of video camera, cause to be unable to get satisfied detection accuracy.The present invention is as follows: 1, acquiring the image for being detected bridge floor one by one, obtain bridge floor image collection.Later, image preprocessing is carried out.2, image registration.3, image co-registration.The present invention completes the automation non-destructive testing of bridge defect feature instead of human eye by Image Acquisition and processing technique, has very important realistic meaning to the research of the bridge floor damage detection technology under complicated landform environment.On the one hand working security is enhanced, on the other hand improves operation mobility and flexibility.Invention realizes the fidelity splicing of large area bridge floor image, improves image mosaic precision and efficiency.
Description
Technical field
The invention belongs to technical field of image detection, and in particular to a kind of large area bridge floor image split-joint method.
Background technique
Bridge exposes to the sun and rain by long-term and loads operation, internal stress can be along bridge as important transport hub
Girder construction is transmitted to some weak parts, and the positional structure surface is caused the Disease Characters such as crack easily occur.Due to bridge surface
Disease Characters cause outside air and hazardous medium to be easy to penetrate into inside concrete after chemical reaction to generate carbonate,
Causing the basicity environment of wherein reinforcing bar reduces, and the purification membrane on surface is more also easy to produce corrosion after wrecking, in addition, concrete carbonization
Also it can aggravate shrinkage cracking, serious harm is generated to the safe handling of concrete-bridge.Therefore, in order to ensure that the use of girder construction
Service life and security performance need that bridge surface Disease Characters are detected and administered in time.
It in order to detect deck disease feature in time, and adopts remedial measures to eliminate safe hidden trouble, generallys use artificial
The mode of inspection and manual markings.However bridge bottom surface detection working environment is often more dangerous, this detection mode mobility
Difference, risk is big, low efficiency.And bridge floor damage by veteran reviewer's hand dipping and detects by an unaided eye to do and remembers
Record, have certain subjectivity, detection accuracy compared with depend on expert Heuristics, and experience lack in quantitative analysis it is objective
Property.
With the development of Computer Vision Detection Technique, gradually appears and image detection is applied to bridge machinery engineering practice
In.But since pontic space is larger, according to long-range shooting sampling, it will receive the limitation of resolution of video camera, cause not obtaining
To satisfied detection accuracy.Therefore need to carry out bridge surface the continuous multiple groups sampling of short distance, and to collected sample
Image carries out the image mosaic of large area, and splicing effect will have a direct impact on the detection accuracy of deck disease feature.In order to
On the basis of meeting wide viewing angle, high-resolution, reduction cumulative errors, raising bridge floor image mosaic precision are realized true as far as possible
The overall situation of bridge machinery face information shows.
Summary of the invention
The purpose of the present invention is to provide a kind of large area bridge floor image split-joint methods.
The specific steps of the present invention are as follows:
Step 1 acquires the image for being detected bridge floor one by one, obtains bridge floor image collection.Later, it extracts and counts bridge floor figure
The luma component information of each bridge floor image in image set conjunction, and the luma component information of each bridge floor image is equalized respectively.
Then each bridge floor image is transformed in frequency domain by Fourier transformation, and using the normalization cross-power in phase related algorithm
The phase information of spectrum obtains the translation parameters between image, completes the pre- estimation between overlapping region adjacent image.
Step 2, image registration
Firstly, extracting SIFT feature in overlapping region between each adjacent image.Then pass through adaptive contrast threshold
Method screens SIFT feature, obtains the feature descriptor being made of matching double points.And each neighbor map is calculated using RANSAC algorithm
Projective transformation matrix as between.
Adaptive contrast threshold method is specific as follows:
(1) characteristic point numerical lower limits N is setmin=200, upper limit Nmax=300, contrast threshold Tc=T0。T0For initial threshold
Value, value are 0.02~0.04.
(2) characteristic point detection is carried out, and Statistical Comparison degree is higher than TcCharacteristic point quantity N.
(3) if Nmin≤N≤Nmax, then contrast is higher than TcCharacteristic point be included in initial matching point set, reject contrast
Lower than threshold value TcCharacteristic point, and be directly entered step (5).Otherwise, step (4) are executed.
(4) if N < Nmin, then by contrast threshold TcIt is reduced to former numerical valueAnd execute step (3).If N > Nmax, then
Contrast threshold is increased to 2 times of former numerical value, and executes step (3).
(5) the mistake characteristic point that initial matching point is concentrated is rejected than secondary near neighbor method by arest neighbors, and generates feature description
Symbol.In feature descriptor comprising by pairs of feature point group at multiple matching double points and the distance between each matching double points
And directional information.
Step 3, image co-registration
First according to the projective transformation matrix between adjacent image, projective transformation is carried out to corresponding bridge floor image.Then it uses
Be fade-in gradually go out blending algorithm smooth transition is weighted respectively to tri- Color Channel of RGB of each adjacent bridge floor image, obtain bridge floor
Stitching image.
It is fade-in and gradually goes out in blending algorithm, being fade-in for each merging point pixel value I (x, y) gradually goes out to add in adjacent image overlapping region
It is as follows to weigh formula:
Wherein, I1(x,y)、I2(x, y) is respectively correspondence merging point of the two adjacent bridge floor images in overlapping region
Pixel value.d1、d2Gradual change weight factor of the two respectively adjacent bridge floor images in corresponding merging point.Withx1、x2The respectively abscissa on overlapping region two sides boundary.X is the abscissa of corresponding merging point.T is two adjacent
Gray difference threshold of the image overlapping region on corresponding merging point.
Preferably, the method for acquiring image in step 1 is specific as follows:
(1) after the internal reference matrix for calculating CCD camera using Zhang Zhengyou plane reference method, radial direction is obtained by least square method
Distortion factor.
(2) on bridge machinery platform placement by step 1-1 calibration CCD camera, according to preset shooting track into
The Image Acquisition of the complete bridge floor of row.Preset shooting track is S-shaped.
(3) according to the obtained CCD camera internal reference matrix of step 1-1 and distortion factor each bridge floor collected to step 1-2
Image carries out image calibration respectively.
Preferably, the process that RANSAC algorithm solves projective transformation matrix is as follows in step 2:
(1) original training set S is constructed with each matching double points in feature descriptor.It is respectively matched in statistics original training set S
Euclidean distance between point pair, and by sorting from small to large.
(2) the preceding 85% matching double points building new samples collection S ' of sequence obtained by step (1) is taken.
(3) 4 groups of matching double points are randomly selected from new samples collection S ' forms an interior point set Si, and calculating matrix model
Interior point set SiHi, enter step (4).
(4) remaining interior each matching double points of new samples collection S ' are directed to matrix model HiCarry out adaptive test.It examines if it exists
The match point that error is less than error threshold is tested, then interior point set S is added in the matching double points for examining error to be less than threshold valuei, and hold
Row step (5).Otherwise, give up matrix model Hi, re-execute (3).
(5) if interior point set SiMiddle element number is greater than defined threshold, then it is assumed that reasonable parameter model is obtained, to update
Interior point set S afterwardsiRecalculate matrix model Hi, and use LM algorithmic minimizing cost function.Otherwise, give up the matrix norm
Type Hi, and it re-execute the steps (3).
(6) l step (3) is repeated to (5), and l is maximum number of iterations.Later, interior point set obtained in l iteration is compared
Close Si, with the maximum interior point set S of element numberiAs final interior point set, and the matrix model H for taking it to calculateiAs adjacent
Projective transformation matrix between bridge floor image.
Preferably, specific step is as follows for projective transformation in step 3:
(1) according to the transitivity of the projective transformation matrix between adjacent image, made respectively with first bridge floor image of every row
Benchmark image for corresponding row is spliced.Between the transformation matrix H each adjacent bridge floor imageii-1Transmitting transformation is carried out, is obtained
Transmitting transformation matrix H between each bridge floor image and benchmark imagei1.Pass through each transformation matrix H againi1By corresponding bridge floor image
It is respectively mapped in datum plane coordinate system, to complete the image mosaic fusion in horizontal direction between each adjacent image, is formed more
Open the lateral panoramic picture Image of wide viewing anglei。
(2) by first obtained in step (1) lateral panoramic picture Image1Spliced as reference panorama image.
Between the transformation matrix T each lateral panoramic picturejj-1Transmitting transformation is carried out, each lateral panoramic picture Image is obtainediIt is complete with benchmark
Transmitting transformation matrix T between scape imagej1.Pass through each transmitting transformation matrix T againj1Respectively by corresponding lateral panoramic picture point
It is not mapped in datum plane coordinate system, to complete the image mosaic fusion on vertical direction between each adjacent transverse panoramic picture,
Form final bridge floor panoramic picture.
The invention has the advantages that:
1, the present invention completes the lossless inspection of automation of bridge defect feature instead of human eye by Image Acquisition and processing technique
It surveys, there is very important realistic meaning to the research of the bridge floor damage detection technology under complicated landform environment.On the one hand enhance
Working security, on the other hand improves operation mobility and flexibility.
2, the present invention is directed to traditional images registration Algorithm in the problem that operand is larger and precision is insufficient, in order to more complete
And bridge floor image Disease Characters data are accurately extracted, and a kind of adjacent bridge floor image registration algorithm of improved multiple groups is proposed, it is real
The fidelity splicing of existing large area bridge floor image, improves image registration accuracy and efficiency, examines for subsequent bridge defect characteristic image
Working foundation has been established in survey, also provides a Technical Reference for the image mosaic detection of other field.
3, the present invention detection environment special for bridge surface proposes that improved be fade-in gradually goes out Image Fusion, draws
The gray difference threshold for entering adjacent bridge floor image, can be effectively suppressed the influence of bridge floor image uncorrelated noise, farthest retains bridge
The minutia information of face disease improves the signal-to-noise ratio of stitching image on the basis of realizing the fusion of multiple groups bridge floor image fidelity.
4, the present invention improves the anti-interference and stability of bridge floor merging algorithm for images under complex background, has preferable
Robustness.Guarantee accuracy and precision that subsequent Disease Characters data are extracted.
5, the present invention carries out the design of multiple groups bridge floor merging algorithm for images according to bridge surface Image Acquisition working environment, and
Reliability Analysis Research is carried out to wherein crucial calculate, algorithm novelty with higher and bridge machinery engineering reference price
Value.
Detailed description of the invention
Fig. 1 is large area bridge floor image mosaic flow chart in the present invention；
Fig. 2 is bridge floor Image Acquisition track schematic diagram in the present invention；
Fig. 3 is several bridge image mosaic process flow diagrams in the present invention；
Fig. 4 is adaptive contrast threshold calculation flow chart in the present invention；
Fig. 5 is that progressive image splices tactful schematic diagram in the present invention；
Fig. 6 is image mosaic strategy schematic diagram by column in the present invention；
Fig. 7 a, 7b the Weighted Fusion schematic diagram between adjacent image in the present invention.
Specific embodiment
Below in conjunction with attached drawing, the invention will be further described.
As shown in Figure 1, a kind of large area bridge floor image split-joint method, the specific steps are as follows:
Step 1, splicing pretreatment
1-1. uses Zhang Zhengyou plane reference method, carries out image sampling to scaling board from different perspectives using CCD camera, leads to
After crossing the internal reference matrix of scaling board X-comers coordinate calculating CCD camera of detection, obtained by least square method radial abnormal
Variable coefficient.
1-2. disposed on bridge machinery platform by step 1-1 calibration CCD camera, according to preset shooting track into
The multiple series of images acquisition of the complete bridge floor of row.Preset shooting track is as shown in Fig. 2, S-shaped.
1-3. is according to the obtained CCD camera internal reference matrix of step 1-1 and distortion factor each bridge floor collected to step 1-2
Image carries out image calibration respectively, to eliminate lens distortion bring distortion effect.
1-4. image preprocessing
Estimate the size of panoramic picture in advance first.Resolution ratio sum number measurement value of the size according to image to be spliced, splicing
Inactive area is removed after the completion；Then by extracting and counting the luma component information of all bridge floor images to be spliced, and divide
The other luma component information to each bridge floor image equalizes, to eliminate the influence of uneven illumination bring difference in brightness；Finally
Each bridge floor image is transformed in frequency domain by Fourier transformation, and using the normalization crosspower spectrum in phase related algorithm
Phase information obtains the translation parameters between image, completes the pre- estimation between overlapping region adjacent image with this.
Phase related algorithm is first to be transformed to image to be spliced in frequency domain using Fourier transformation, then pass through normalization mutually
The translation parameters of two image of spectra calculation obtains two-dimensional impulse function: the peak value size reflection of the two-dimensional impulse function
Content relevance between adjacent bridge floor image, value are that 1 two images of expression are identical, indicate entirely different for 0.Bridge figure
Change as brought by being moved between the collected adjacent image of detection device institute there are perspective transform and position, although impulse can be made
The energy of function is dispersed to numerous small leaks from single peak value, but its corresponding translation parameters in peak-peak position can still keep phase
To stabilization.Therefore, the translational movement obtained by phase related algorithm can obtain roughly the overlapping region between image to be spliced, and
The algorithm is insensitive to illumination brightness change, and correlation maximum peak point detected has preferable robustness and stability.
Step 2, image registration
Firstly, SIFT feature is extracted in overlapping region between each adjacent image, to reduce a large amount of unnecessary characteristic points
Calculation amount is detected, the detection efficiency of SIFT feature is improved；Then it by adaptive contrast threshold method, will test
SIFT feature quantity controls in a reasonable range, to filter out stable feature point set；And using improved
RANSAC algorithm (RANSAC algorithm) calculates the projective transformation matrix H between each adjacent image.
Adaptive contrast threshold method is specific as follows:
Overlapping region between determination two-by-two adjacent bridge floor image (after Δ x, Δ y), is carried out only for the overlapping region
SIFT feature detection.Since wherein the lower characteristic point of contrast is more sensitive to bridge floor ambient noise, therefore set contrast
Threshold value filters out stable feature point set, is denoted as C.
In the prior art, the contrast of each SIFT feature is calculated by difference of Gaussian Taylor expansion, and fixation is set
Contrast threshold retain the characteristic point higher than the contrast threshold as invariant feature point.However above-mentioned contrast threshold Tc
For fixed value, general value is between 0.02 to 0.04.But in the crack image detection of different concrete-bridges, SIFT detection
To candidate feature point set have very big difference, Partial Bridges surface is more smooth bright and clean, and acquired image digital signal is more
Smoothly, scale space factor sigma is smaller, causes the characteristic point detected less, may be unable to satisfy the number of Feature Points Matching instead
Amount demand influences final splicing precision.(the contrast threshold step used in traditional stitching algorithm).The present invention is arranged one
The contrast threshold of variation, to guarantee the SIFT feature number detected control in a reasonable range.It is tested through multiple groups
Verifying, which shows that the characteristic point of Bridge Crack image detection is maintained between 200 to 300, can meet preferable splicing precision.
As shown in figure 4, determining contrast threshold T in the present inventioncMethod, it is specific as follows:
(1) characteristic point numerical lower limits N is setmin=200, upper limit Nmax=300, contrast threshold Tc=T0；T0For initial threshold
Value, value are 0.02~0.04.
(2) characteristic point detection is carried out, and Statistical Comparison degree is higher than TcCharacteristic point quantity N.
(3) if Nmin≤N≤Nmax, then contrast is higher than TcCharacteristic point be included in initial matching point set, reject contrast
Lower than threshold value TcCharacteristic point, and be directly entered step (5)；Otherwise, step (4) are executed.
(4) if N < Nmin, then by contrast threshold TcIt is reduced to former numerical valueAnd execute step (3)；If N > Nmax, then
Contrast threshold is increased to 2 times of former numerical value, and executes step (3).
(5) the mistake characteristic point that initial matching point is concentrated is rejected than secondary near neighbor method by arest neighbors, and generates feature description
Symbol.In feature descriptor comprising by pairs of feature point group at multiple matching double points and the distance between each matching double points and
Directional information.
After the Feature Points Matching between adjacent image overlapping region, enough matching double points are filtered out, matching double points are passed through
The transformation matrix between Bridge Crack sequence image is solved, large-scale bridge floor image mosaic is completed with this.To further increase figure
As registration efficiency and precision, RANSAC algorithm is improved.
The process that improved RANSAC algorithm solves projective transformation matrix H is as follows:
(1) original training set S is constructed with each matching double points in feature descriptor.It is respectively matched in statistics original training set S
Euclidean distance between point pair, and by sorting from small to large；
(2) the preceding 85% matching double points building new samples collection S ' of sequence obtained by step (1) is taken；
(3) 4 groups of matching double points are randomly selected from new samples collection S ' forms an interior point set Si, and calculating matrix model
Interior point set SiHi, enter step (4)；
(4) remaining interior each matching double points of new samples collection S ' are directed to matrix model HiCarry out adaptive test；It examines if it exists
The match point that error is less than error threshold is tested, then interior point set S is added in the matching double points for examining error to be less than threshold valuei, and hold
Row step (5)；Otherwise, give up matrix model Hi, re-execute (3).
(5) if interior point set SiMiddle element number is greater than defined threshold, then it is assumed that reasonable parameter model is obtained, to update
Interior point set S afterwardsiRecalculate matrix model Hi, and use LM algorithmic minimizing cost function；Otherwise, give up the matrix norm
Type Hi, and it re-execute the steps (3).
(6) l step (3) is repeated to (5), and l is maximum number of iterations.Later, interior point set obtained in l iteration is compared
Close Si, with the maximum interior point set S of element numberiAs final interior point set, and the matrix model H for taking it to calculateiAs adjacent
Projective transformation matrix H between bridge floor image.
Improved RANSAC algorithm is by the Euclidean distance between calculating all matching double points and is ranked up screening, not only subtracts
The sample set data for having lacked point pair to be matched improve intra-office point proportion in sample set, and reduce projective transformation square
The iteration of battle array refines number, to improve the matching precision of bridge floor image.According to image characteristic point to the distance between it is smaller,
With the higher characteristic of similarity, calculate herein the Euclidean distance between all characteristic points pair and according to sequence from small to large arrange into
Row screening.After showing overlapped provincial characteristics point to first matching by multiple groups bridge floor image mosaic test result statistics, initial sample
The successful match rate of this collection S then takes preceding 85% characteristic point of its sequence to building new samples collection S ' up to 85% or more.Through sample
Notebook data screening, sample set S ' they include enough matching double points, not only increase intra-office point proportion in sample set, and
Greatly reduce the number of iterations of transformation matrix parameter model H.
Step 3, image co-registration
First according to the projective transformation matrix between adjacent image, projective transformation is carried out to corresponding bridge floor image；Then it uses
Be fade-in gradually go out blending algorithm smooth transition is weighted respectively to tri- Color Channel of RGB of each adjacent bridge floor image, obtain bridge floor
Stitching image.
Specific step is as follows for projective transformation:
(1) as shown in figure 5, according to the transitivity of the projective transformation matrix between adjacent image, with first bridge floor of every row
Image is spliced respectively as the benchmark image of corresponding row according to image line splicing strategy.Between each adjacent bridge floor image
Transformation matrix Hii-1Transmitting transformation is carried out, the transmitting transformation matrix H between each bridge floor image and benchmark image is obtainedi1；Pass through again
Each transformation matrix Hi1Corresponding bridge floor image is respectively mapped in datum plane coordinate system, it is each adjacent in horizontal direction to complete
Image mosaic fusion between image, forms the lateral panoramic picture Image of multiple wide viewing anglesi。
Each transfer matrix transformation for mula is as follows:
H21=H21
H31=H32×H21
Hn1=Hnn-1×Hn-1n-2×…×H21
Wherein, Hii-1For the transformation matrix between (i-1)-th bridge floor image and i-th bridge floor image of same a line, value exists
It is calculated in step 2-2；Hi1For the transformation matrix between the 1st bridge floor image of same a line and i-th bridge floor image；N is same
Amount of images in a line.
(2) as shown in fig. 6, first lateral panoramic picture Image obtained in step (1)1As reference panorama image,
According to image column splicing strategy, spliced.Between the transformation matrix T each lateral panoramic picturejj-1Transmitting transformation is carried out, it obtains
To each lateral panoramic picture ImageiTransmitting transformation matrix T between reference panorama imagej1；Pass through each transmitting transformation matrix again
Tj1Corresponding lateral panoramic picture is respectively mapped in datum plane coordinate system respectively, to complete each adjacent cross on vertical direction
It is merged to the image mosaic between panoramic picture, the description in image mosaic fusion in transfer matrix transformation for mula reference step (1),
Form final bridge floor panoramic picture.
It is fade-in in the present embodiment and gradually goes out blending algorithm by improving, referring specifically to following.
After image registration, further to eliminate the interference that bridge floor image mosaic stitches fracture detection processing, usually to phase
Adjacent bridge floor image pixel value is weighted and averaged, as shown in Figure 7b, distance of the pixel to both sides suture in overlapping region
As fusion weight distinguishing rule.
But since image capture position changes, bridge surface reflection is likely to result in respective pixel in overlapping region
There are hopping phenomenons for point gray value, to eliminate the influence that it generates blending image, are fade-in in tradition and gradually go out Weighted Fusion calculating
One threshold value t of middle introducing.Lap target pixel points are calculated in the corresponding gray scale difference value of two width original images, if the difference is small
In threshold value, illustrate the pixel in former bridge floor image and not shown notable difference, can directly take its weighted average as should
Point pixel value；Conversely, illustrating that image to be spliced, there are light and shade mutation, should take its smooth preceding weight larger under the pixel position
Pixel value as the fusion pixel values.
It is fade-in and gradually goes out in blending algorithm, being fade-in for each fusion pixel values I (x, y) gradually goes out to weight in adjacent image overlapping region
Formula is as follows:
Wherein, I1(x,y)、I2(x, y) is respectively correspondence merging point of the two adjacent bridge floor images in overlapping region
Pixel value, as shown in Figure 7a.d1、d2Gradual change weight factor of the two respectively adjacent bridge floor images in corresponding merging point；Such as figure
Shown in 7b,Withx1、x2The respectively abscissa on overlapping region two sides boundary；X is corresponding merging point
Abscissa；T is gray difference threshold of the two adjacent image overlapping regions on corresponding merging point.
Claims (4)
1. a kind of large area bridge floor image split-joint method, it is characterised in that: step 1 acquires the image for being detected bridge floor one by one, obtains
To bridge floor image collection；Later, the luma component information of each bridge floor image in bridge floor image collection is extracted and counts, and right respectively
The luma component information of each bridge floor image is equalized；Then each bridge floor image is transformed to by frequency domain by Fourier transformation
It is interior, and the translation parameters between image, completion pair are obtained using the phase information of the normalization crosspower spectrum in phase related algorithm
The pre- estimation of overlapping region between adjacent image；
Step 2, image registration
Firstly, extracting SIFT feature in overlapping region between each adjacent image；Then it is sieved by adaptive contrast threshold method
SIFT feature is selected, the feature descriptor being made of matching double points is obtained；And using between each adjacent image of RANSAC algorithm calculating
Projective transformation matrix；
Adaptive contrast threshold method is specific as follows:
(1) characteristic point numerical lower limits N is setmin=200, upper limit Nmax=300, contrast threshold Tc=T0；T0For initial threshold,
Value is 0.02~0.04；
(2) characteristic point detection is carried out, and Statistical Comparison degree is higher than TcCharacteristic point quantity N；
(3) if Nmin≤N≤Nmax, then contrast is higher than TcCharacteristic point be included in initial matching point set, reject contrast and be lower than threshold
Value TcCharacteristic point, and be directly entered step (5)；Otherwise, step (4) are executed；
(4) if N < Nmin, then by contrast threshold TcIt is reduced to former numerical valueAnd execute step (3)；If N > Nmax, then will be right
Than 2 times that degree threshold value is increased to former numerical value, and execute step (3)；
(5) the mistake characteristic point that initial matching point is concentrated is rejected than secondary near neighbor method by arest neighbors, and generates feature descriptor；It is special
Levy in descriptor comprising by pairs of feature point group at multiple matching double points and the distance between each matching double points and direction
Information；
Step 3, image co-registration
First according to the projective transformation matrix between adjacent image, projective transformation is carried out to corresponding bridge floor image；Then it uses and is fade-in
Gradually go out blending algorithm and smooth transition is weighted respectively to tri- Color Channel of RGB of each adjacent bridge floor image, obtains bridge floor splicing
Image；
It is fade-in and gradually goes out in blending algorithm, being fade-in for each merging point pixel value I (x, y) gradually goes out to weight public affairs in adjacent image overlapping region
Formula is as follows:
Wherein, I1(x,y)、I2(x, y) is respectively the pixel of correspondence merging point of the two adjacent bridge floor images in overlapping region
Value；d1、d2Gradual change weight factor of the two respectively adjacent bridge floor images in corresponding merging point；Withx1、x2The respectively abscissa on overlapping region two sides boundary；X is the abscissa of corresponding merging point；T is two adjacent
Gray difference threshold of the image overlapping region on corresponding merging point.
2. a kind of large area bridge floor image split-joint method according to claim 1, it is characterised in that: acquire figure in step 1
The method of picture is specific as follows:
(1) after the internal reference matrix for calculating CCD camera using Zhang Zhengyou plane reference method, radial distortion is obtained by least square method
Coefficient；
(2) CCD camera of the placement by step 1-1 calibration on bridge machinery platform, has carried out according to preset shooting track
The Image Acquisition of whole bridge floor；Preset shooting track is S-shaped；
(3) according to the obtained CCD camera internal reference matrix of step 1-1 and distortion factor each bridge floor image collected to step 1-2
Image calibration is carried out respectively.
3. a kind of large area bridge floor image split-joint method according to claim 1, it is characterised in that: in step 2, RANSAC
The process that algorithm solves projective transformation matrix is as follows:
(1) original training set S is constructed with each matching double points in feature descriptor；Count each matching double points in original training set S
Between Euclidean distance, and by sorting from small to large；
(2) the preceding 85% matching double points building new samples collection S ' of sequence obtained by step (1) is taken；
(3) 4 groups of matching double points are randomly selected from new samples collection S ' forms an interior point set Si, and point in calculating matrix model
Set SiHi, enter step (4)；
(4) remaining interior each matching double points of new samples collection S ' are directed to matrix model HiCarry out adaptive test；Error is examined if it exists
Less than the match point of error threshold, then interior point set S is added in the matching double points for examining error to be less than threshold valuei, and execute step
(5)；Otherwise, give up matrix model Hi, re-execute (3)；
(5) if interior point set SiMiddle element number is greater than defined threshold, then it is assumed that reasonable parameter model is obtained, to updated
Interior point set SiRecalculate matrix model Hi, and use LM algorithmic minimizing cost function；Otherwise, give up matrix model Hi,
And it re-execute the steps (3)；
(6) l step (3) is repeated to (5), and l is maximum number of iterations；Later, interior point set S obtained in l iteration is comparedi,
With the maximum interior point set S of element numberiAs final interior point set, and the matrix model H for taking it to calculateiAs adjacent bridge floor
Projective transformation matrix between image.
4. a kind of large area bridge floor image split-joint method according to claim 1, it is characterised in that: in step 3, projection becomes
Change that specific step is as follows:
(1) according to the transitivity of the projective transformation matrix between adjacent image, using first bridge floor image of every row as right
The benchmark image that should be gone is spliced；Between the transformation matrix H each adjacent bridge floor imageii-1Transmitting transformation is carried out, each bridge is obtained
Transmitting transformation matrix H between face image and benchmark imagei1；Pass through each transformation matrix H againi1Corresponding bridge floor image is distinguished
It is mapped in datum plane coordinate system, to complete the image mosaic fusion in horizontal direction between each adjacent image, forms multiple width
The lateral panoramic picture Image at visual anglei；
(2) by first obtained in step (1) lateral panoramic picture Image1Spliced as reference panorama image；To each
Transformation matrix T between lateral panoramic picturejj-1Transmitting transformation is carried out, each lateral panoramic picture Image is obtainediWith reference panorama figure
Transmitting transformation matrix T as betweenj1；Pass through each transmitting transformation matrix T againj1Corresponding lateral panoramic picture is reflected respectively respectively
It is mapped in datum plane coordinate system, to complete the image mosaic fusion on vertical direction between each adjacent transverse panoramic picture, is formed
Final bridge floor panoramic picture.
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