CN109410156A - A kind of unmanned plane inspection transmission line of electricity image extraction method - Google Patents
A kind of unmanned plane inspection transmission line of electricity image extraction method Download PDFInfo
- Publication number
- CN109410156A CN109410156A CN201811341203.1A CN201811341203A CN109410156A CN 109410156 A CN109410156 A CN 109410156A CN 201811341203 A CN201811341203 A CN 201811341203A CN 109410156 A CN109410156 A CN 109410156A
- Authority
- CN
- China
- Prior art keywords
- camera
- transmission line
- unmanned plane
- image
- polar curve
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 44
- 230000005611 electricity Effects 0.000 title claims abstract description 29
- 238000007689 inspection Methods 0.000 title claims abstract description 15
- 238000000605 extraction Methods 0.000 title claims abstract description 10
- 238000012937 correction Methods 0.000 claims abstract description 20
- 239000011159 matrix material Substances 0.000 claims abstract description 13
- 238000000034 method Methods 0.000 claims abstract description 12
- 230000011218 segmentation Effects 0.000 claims abstract description 11
- 238000013519 translation Methods 0.000 claims abstract description 7
- 238000006116 polymerization reaction Methods 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000018109 developmental process Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 230000005484 gravity Effects 0.000 claims description 2
- 238000005457 optimization Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 238000003709 image segmentation Methods 0.000 abstract description 3
- PNEYBMLMFCGWSK-UHFFFAOYSA-N Alumina Chemical compound [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 description 3
- 230000007257 malfunction Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses a kind of unmanned plane inspection transmission line of electricity image extraction methods, comprising the following steps: binocular camera calibration obtains the inside and outside parameter matrix of left and right camera and the spin matrix and translation vector of binocular camera respectively；Receive the image data of binocular camera；Polar curve correction is carried out to image data；ADCensus is carried out to image and carries out Stereo matching, obtains disparity map；Image segmentation is carried out according to the depth map, obtains pure transmission line of electricity.This method utilizes ADCensus Stereo Matching Algorithm and Threshold Segmentation Algorithm, can quickly and accurately extract transmission line of electricity from complex background.
Description
Technical field
The invention belongs to the inspection of power transmission line unmanned machine and technical field of computer vision, and it is defeated to be related to a kind of unmanned plane inspection
Electric line image extraction method.
Background technique
Most transmission lines of electricity are exposed in natural environment complicated and changeable, right by the threat of various natural calamities
If detection cannot be obtained in time in transmission line malfunction and repaired, normal production activity will be directly affected.Carry out power transmission line
Road inspection has great meaning.
Artificial on-site test is mainly used at present or transmission line malfunction is detected by transmission line of electricity image, this
Kind mode large labor intensity, subjectivity are strong.For image detection, it is concentrated mainly on two-dimensional level and is detected, this mode
Cannot still effective Ground Split be carried out to complicated natural background and transmission line of electricity, transmission line of electricity defect diagonsis accuracy cannot protect
Card.
Summary of the invention
The problem to be solved in the present invention is: a kind of unmanned plane inspection transmission line of electricity image extraction method is provided, it is existing to solve
There is the artificial on-site test of technology or by transmission line of electricity image detection, large labor intensity, subjectivity are strong and accuracy is not high
Problem.
The technical scheme is that a kind of unmanned plane inspection transmission line of electricity image extraction method, comprising the following steps:
Step 1: camera calibration being carried out to left camera and right camera respectively, obtains the inside and outside parameter of left camera and right camera
Then matrix carries out stereo calibration by the parameter of two obtained cameras, obtain the spin matrix of binocular camera and be translated towards
Amount；
Step 2: by binocular camera and microcomputer-equipped on unmanned plane, the SDK provided camera being provided and carries out two
Secondary exploitation realizes binocular camera automatic collection or so transmission of electricity line image pair, wherein SDK refers to Software Development Kit；
Unmanned plane opens microcomputer before taking off, while the program for controlling binocular camera also automatically turns on, and uses nobody
Machine operation handle control unmanned plane flies along power transmission line, and automatic collection is to power transmission line left images pair；
Step 3: in the program that the data write-in polar curve of the spin matrix and translation vector that obtain after stereo calibration is corrected,
The collected power transmission line left images pair of unmanned plane inspection are handled using polar curve correction program, left images pair are corrected by polar curve
The object answered will obtain the picture after polar curve corrects, and improve the accuracy of Stereo matching on same polar curve；
Step 4: using the picture after the processing polar curve correction of ADCensus Stereo Matching Algorithm, by that will be obtained after Stereo matching
Obtain disparity map；
Step 5: analyzing the grey level histogram of disparity map, choose suitable threshold value and Threshold segmentation, threshold value are carried out to disparity map
The complex background in image will be rejected after segmentation obtains pure transmission line of electricity image.
The beneficial effects of the present invention are:
(1) present invention can be accurately detected transmission line of electricity, and complex background is rejected and obtains pure transmission line of electricity
Image is provided convenience for the fault diagnosis of next step, improves the accuracy of transmission line malfunction detection；
(2) the invention avoids three-dimensional reconstruction is carried out to transmission line of electricity, the relative depth information between target object is only calculated
Image segmentation is completed, computation complexity is greatly reduced, can be good at the requirement of real-time for meeting transmission line faultlocating.
Detailed description of the invention
Fig. 1 is implementation process of the invention；
Fig. 2 (a), the left images that (b) is actual scene one of the present invention are (c) the polar curve school of actual scene one of the present invention
Positive figure is (d) disparity map of the Stereo matching of actual scene one of the present invention, (e) is the ash of one disparity map of actual scene of the present invention
Histogram is spent, (f) is the segmentation figure of one disparity map of actual scene of the present invention；
Fig. 3 (a), the left images that (b) is actual scene two of the present invention；It (c) is the polar curve school of actual scene two of the present invention
Positive figure is (d) disparity map of the Stereo matching of actual scene two of the present invention, (e) is the ash of two disparity map of actual scene of the present invention
Histogram is spent, (f) is the segmentation figure of two disparity map of actual scene of the present invention.
Specific embodiment
It elaborates with reference to the accompanying drawing to the embodiment of the method for the present invention.
The technical solution adopted by the present invention are as follows: a kind of unmanned plane inspection transmission line of electricity image extraction method, including following step
It is rapid:
Step 1: left camera and right camera are demarcated respectively, obtain the inside and outside parameter matrix of left camera and right camera,
Then stereo calibration is carried out by the parameter of two obtained cameras, obtains the spin matrix and translation vector of binocular camera；
Step 2: by binocular camera and microcomputer-equipped on unmanned plane, the SDK provided camera being provided and carries out two
Secondary exploitation realizes binocular camera automatic collection or so transmission of electricity line image pair, wherein SDK refers to Software Development Kit；
Unmanned plane opens microcomputer before taking off, while the program for controlling binocular camera also automatically turns on, and uses nobody
Machine operation handle control unmanned plane flies along power transmission line, and automatic collection is to power transmission line left images pair；
Step 3: in the program that the data write-in polar curve of the spin matrix and translation vector that obtain after stereo calibration is corrected,
The collected power transmission line left images pair of unmanned plane inspection are handled using polar curve correction program, left images pair are corrected by polar curve
The object answered will obtain the picture after polar curve corrects, and improve the accuracy of Stereo matching on same polar curve；
Step 4: using the picture after the processing polar curve correction of ADCensus Stereo Matching Algorithm, by that will be obtained after Stereo matching
Obtain disparity map；
Step 5: analyzing the grey level histogram of disparity map, choose suitable threshold value and Threshold segmentation, threshold value are carried out to disparity map
The complex background in image will be rejected after segmentation obtains pure transmission line of electricity image.
Wherein, camera calibration is carried out to the left camera of binocular camera and right camera in step 1, calibration tool uses
The calibration tool case of MATLAB, specific demarcation flow are as follows:
Step 1.1: it is 200mm*200mm, grid that aluminium oxide, which demarcates gridiron pattern model LGP200-12*9 outer dimension,
Side length 15mm, pattern array 12*9, pattern dimension 240*180；
Step 1.2: the picture by shooting gridiron pattern different angle, the angle of gridiron pattern rotation will guarantee to clap in phase function
It takes the photograph in the range of tessellated each grid, this experiment acquires 20 pairs or so chessboard table images pair altogether；
Step 1.3: monocular calibration being carried out to left and right camera respectively, obtains the intrinsic parameter of left and right camera, outer parameter and abnormal
Variable element；
Step 1.4: carrying out binocular calibration with the calibration tool case of MATLAB, obtain the initial parameter of binocular camera.It obtains
Obtain Intrinsic Matrix M, the radial distortion parameter (k of left camera and right camera1,k2,k3), tangential distortion parameter (p1,p2)。
Wherein: fx, fyThe normalization focal length being referred to as in x-axis and y-axis, cx, cyIt is imaged for image origin relative to optical center
The transverse and longitudinal offset of point.
And then binocular camera calibration is completed, the calibrated intrinsic parameter of binocular camera and right camera are obtained relative to left camera
Spin matrix, translation vector.
Polar curve correction is divided into two parts in step 3, and respectively Lens Distortion Correction and tangential distortion corrects, and updating formula is such as
Under:
The correction of radial distortion:
X'=x (1+k1r2+k2r4+k3r6)
Y'=y (1+k1r2+k2r4+k3r6)
The correction of tangential distortion:
X'=x+ [2p1y+p2(r2+2x2)]
Y'=y+ [p1(r2+2y2)+2p2x]
Wherein, k1、k2、k3For the coefficient of radial distortion of camera, p1、p2For the tangential distortion coefficient of camera, (x, y) is abnormal
The home position of height, (x ', y ') for the new position after correction, the result such as attached drawing 2 (c) and attached drawing 3 (c) after correction are shown.
Using the picture after the processing polar curve correction of ADCensus Stereo Matching Algorithm in step 4, specific algorithm flow is such as
Shown in lower:
Step 4.1:ADCensus matching cost calculates, and calculation formula is as follows:
C (p, d)=λCensusCCensus(p,d)+λADCAD(p,d)
Wherein, C (p, d) is ADCensus matching cost, CCensus(p, d) is the matching cost of Census transformation, CAD(p,
D) be AD matching cost, λCensusWith λADFor the parameter for adjusting specific gravity between Census and AD.
Step 4.2: cost polymerization, detailed process are as follows:
The construction method of standard support region is that there is the point building regional area of similar luminance value to carry out Stereo matching for selection,
It selects horizontal direction or vertical direction to be polymerize first, in order to obtain stable polymerization cost, needs to carry out four polymerizations
It calculates, horizontal polymerization twice and twice vertical polymerization.
Step 4.3: the calculating and optimization of parallax
The method that disparity computation uses WTA, wherein Caggr(p, d) is cost polymerization as a result, calculation method is as follows:
D (p)=arg (Caggr(p,d))
Wherein D (p) is that cost polymerize the parallax acquired.Because of the limitation of algorithm, it is invalid that obtained disparity map can exist
Parallax value, the erroneous matching such as generated by blocking for object, therefore original disparity map is optimized.Left and right consistency school
Invalid parallax can be effective filtered out by testing, specific method: assuming that its corresponding parallax value is in Fig. 2 (a) left figure there are a point P
DL(p), then the corresponding parallax value of right figure be DR(p-DL(p)), whether detection both sides relation meets following relationship:
|DL(p)-DR(p-DL(p)) | < δ
Wherein, δ is threshold value, general δ=1.Meet above formula then to illustrate to meet consistency check, otherwise the point is regarded
The method of difference correction correction is the parallax value using the lesser point of parallax in the correct match point of the point or so.
D (p)=min (DL(PR),DR(PL))
Wherein, DL(pR) it is the corresponding parallax value of left figure, DR(pL) it is the corresponding parallax value of right figure, at step as above
Left and right transmission line of electricity image pair is managed, can be obtained shown in the disparity map such as attached drawing 2 (d) and attached drawing 3 (d) of transmission line of electricity.
Basis analyzes such as attached drawing 2 (e) and attached drawing 3 (e) institute the grey level histogram of transmission line of electricity disparity map in step 5
Show, determine segmentation threshold, segmentation threshold is applied to rejecting complex background in image segmentation algorithm and obtains pure transmission line of electricity
Shown in image such as attached drawing 2 (f) and attached drawing 3 (f).
Claims (3)
1. a kind of unmanned plane inspection transmission line of electricity image extraction method, which comprises the steps of:
Step 1: camera calibration is carried out to left camera and right camera respectively, obtains the inside and outside parameter matrix of left camera and right camera,
Then stereo calibration is carried out by the parameter of two obtained cameras, obtains the spin matrix and translation vector of binocular camera；
Step 2: by binocular camera and microcomputer-equipped on unmanned plane, secondary open being carried out by the SDK provided camera
Hair realizes binocular camera automatic collection or so transmission of electricity line image pair, wherein SDK refers to Software Development Kit；
Unmanned plane opens microcomputer before taking off, while the program for controlling binocular camera also automatically turns on, and is grasped using unmanned plane
Make handle control unmanned plane to fly along power transmission line, and automatic collection is to power transmission line left images pair；
Step 3: by the program of the data write-in polar curve correction of the spin matrix and translation vector that are obtained after stereo calibration, using
Polar curve correction program handles the collected power transmission line left images pair of unmanned plane inspection, and it is corresponding to correct left images by polar curve
Object on same polar curve, will obtain the picture after polar curve corrects；
Step 4: using the picture after the processing polar curve correction of ADCensus Stereo Matching Algorithm, by will be regarded after Stereo matching
Difference figure；
Step 5: analyzing the grey level histogram of disparity map, choose suitable threshold value and Threshold segmentation, Threshold segmentation are carried out to disparity map
The complex background in image will be rejected afterwards obtains pure transmission line of electricity image.
2. a kind of unmanned plane inspection transmission line of electricity image extraction method as described in claim 1, which is characterized in that in step 3
Polar curve correction is divided into two parts, and respectively Lens Distortion Correction and tangential distortion corrects, and updating formula is as follows:
The correction of radial distortion:
X'=x (1+k1r2+k2r4+k3r6)
Y'=y (1+k1r2+k2r4+k3r6)
The correction of tangential distortion:
X'=x+ [2p1y+p2(r2+2x2)]
Y'=y+ [p1(r2+2y2)+2p2x]
Wherein, k1、k2、k3For the coefficient of radial distortion of camera, p1、p2For the tangential distortion coefficient of camera, (x, y) is distortion point
Home position, (x ', y ') are the new position after correction.
3. a kind of unmanned plane inspection transmission line of electricity image extraction method as described in claim 1, which is characterized in that in step 4
Using the picture after the processing polar curve correction of ADCensus Stereo Matching Algorithm, specific algorithm flow is as follows:
Step 4.1:ADCensus matching cost calculates, and calculation formula is as follows:
C (p, d)=λCensusCCensus(p,d)+λADCAD(p,d)
Wherein, C (p, d) is ADCensus matching cost, CCensus(p, d) is the matching cost of Census transformation, CAD(p, d) is
The matching cost of AD, λCensusWith λADFor the parameter for adjusting specific gravity between Census and AD；
Step 4.2: cost polymerization, detailed process are as follows:
The construction method of standard support region is that there is the point building regional area of similar luminance value to carry out Stereo matching for selection, first
Selection horizontal direction or vertical direction are polymerize, then carry out four polymerizations calculating, i.e., horizontal twice to polymerize and hang down twice
Straight polymerization；
Step 4.3: the calculating and optimization of parallax
The method that disparity computation uses WTA, wherein Caggr(p, d) is cost polymerization as a result, calculation method is as follows:
D (p)=arg (Caggr(p,d))
Wherein D (p) is that cost polymerize the parallax acquired, then carries out left and right consistency desired result, specific method: assuming that in left camera
There are a point P in image, its corresponding parallax value is DL(p), then the corresponding parallax value of image of right camera be DR(p-DL
(p)), whether detection both sides relation meets following relationship:
|DL(p)-DR(p-DL(p)) | < δ
Wherein, δ is threshold value, general δ=1, meets above formula and then illustrates to meet consistency check, and otherwise it is strong to need to carry out parallax for the point
The method just corrected be using the parallax value of the lesser point of parallax in the correct match point of the point or so,
D (p)=min (DL(PR),DR(PL))
Wherein, DL(PR) be left camera the corresponding parallax value of image, DR(PL) be left camera the corresponding parallax value of image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811341203.1A CN109410156A (en) | 2018-11-12 | 2018-11-12 | A kind of unmanned plane inspection transmission line of electricity image extraction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811341203.1A CN109410156A (en) | 2018-11-12 | 2018-11-12 | A kind of unmanned plane inspection transmission line of electricity image extraction method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109410156A true CN109410156A (en) | 2019-03-01 |
Family
ID=65472821
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811341203.1A Pending CN109410156A (en) | 2018-11-12 | 2018-11-12 | A kind of unmanned plane inspection transmission line of electricity image extraction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109410156A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111009012A (en) * | 2019-11-29 | 2020-04-14 | 四川沃洛佳科技有限公司 | Unmanned aerial vehicle speed measurement method based on computer vision, storage medium and terminal |
| CN111784753A (en) * | 2020-07-03 | 2020-10-16 | 江苏科技大学 | Three-dimensional reconstruction stereo matching method for autonomous underwater robot recovery butt joint foreground view field |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150037319A (en) * | 2013-09-30 | 2015-04-08 | 엘지디스플레이 주식회사 | Stereoscopic image display device and disparity calculation method thereof |
| CN107481271A (en) * | 2017-07-25 | 2017-12-15 | 成都通甲优博科技有限责任公司 | A kind of solid matching method, system and mobile terminal |
| CN108520534A (en) * | 2018-04-23 | 2018-09-11 | 河南理工大学 | A kind of adaptive multimodality fusion Stereo Matching Algorithm |
-
2018
- 2018-11-12 CN CN201811341203.1A patent/CN109410156A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150037319A (en) * | 2013-09-30 | 2015-04-08 | 엘지디스플레이 주식회사 | Stereoscopic image display device and disparity calculation method thereof |
| CN107481271A (en) * | 2017-07-25 | 2017-12-15 | 成都通甲优博科技有限责任公司 | A kind of solid matching method, system and mobile terminal |
| CN108520534A (en) * | 2018-04-23 | 2018-09-11 | 河南理工大学 | A kind of adaptive multimodality fusion Stereo Matching Algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 刘党辉等: "一种改进的快速立体匹配算法", 《新技术新工艺》 * |
| 吕鹏程: "电力线视频监控方法研究与软件设计", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
| 王玉全: "基于双目视觉的实时三维重建技术研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111009012A (en) * | 2019-11-29 | 2020-04-14 | 四川沃洛佳科技有限公司 | Unmanned aerial vehicle speed measurement method based on computer vision, storage medium and terminal |
| CN111009012B (en) * | 2019-11-29 | 2023-07-28 | 四川沃洛佳科技有限公司 | Unmanned aerial vehicle speed measuring method based on computer vision, storage medium and terminal |
| CN111784753A (en) * | 2020-07-03 | 2020-10-16 | 江苏科技大学 | Three-dimensional reconstruction stereo matching method for autonomous underwater robot recovery butt joint foreground view field |
| CN111784753B (en) * | 2020-07-03 | 2023-12-05 | 江苏科技大学 | Jing Shichang three-dimensional reconstruction stereo matching method before recovery and docking of autonomous underwater robot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107977997B (en) | Camera self-calibration method combined with laser radar three-dimensional point cloud data | |
| CN103868460B (en) | Binocular stereo vision method for automatic measurement based on parallax optimized algorithm | |
| CN109410207A (en) | A kind of unmanned plane line walking image transmission line faultlocating method based on NCC feature | |
| CN108876749A (en) | A kind of lens distortion calibration method of robust | |
| CN107833181B (en) | Three-dimensional panoramic image generation method based on zoom stereo vision | |
| CN103278138B (en) | Method for measuring three-dimensional position and posture of thin component with complex structure | |
| CN107767442A (en) | A kind of foot type three-dimensional reconstruction and measuring method based on Kinect and binocular vision | |
| CN102986372B (en) | Based on the identification of the harvesting object of full-view stereo vision, classification and space positioning apparatus and method | |
| CN106780590A (en) | The acquisition methods and system of a kind of depth map | |
| CN105528785A (en) | Binocular visual image stereo matching method | |
| CN110232389A (en) | A kind of stereoscopic vision air navigation aid based on green crop feature extraction invariance | |
| CN108596975A (en) | A kind of Stereo Matching Algorithm for weak texture region | |
| CN106981081A (en) | A kind of degree of plainness for wall surface detection method based on extraction of depth information | |
| CN109165680A (en) | Single target object dictionary model refinement method under the indoor scene of view-based access control model SLAM | |
| CN102982334B (en) | The sparse disparities acquisition methods of based target edge feature and grey similarity | |
| CN105913013A (en) | Binocular vision face recognition algorithm | |
| CN110910431B (en) | Multi-view three-dimensional point set recovery method based on monocular camera | |
| CN110322572A (en) | A kind of underwater culvert tunnel inner wall three dimensional signal space method based on binocular vision | |
| CN112348775B (en) | Vehicle-mounted looking-around-based pavement pit detection system and method | |
| CN111536981B (en) | Embedded binocular non-cooperative target relative pose measurement method | |
| CN115272271A (en) | Pipeline defect detecting and positioning ranging system based on binocular stereo vision | |
| CN110288659A (en) | A kind of Depth Imaging and information acquisition method based on binocular vision | |
| CN117036641A (en) | Road scene three-dimensional reconstruction and defect detection method based on binocular vision | |
| CN109297978B (en) | Binocular imaging-based power line unmanned aerial vehicle inspection and defect intelligent diagnosis system | |
| CN104331978A (en) | Recognition device and method for fold of paper currency |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190301 |