CN110969103B - Method for measuring length of highway pavement disease based on PTZ camera - Google Patents
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Abstract
The invention relates to a method for measuring the length of a highway pavement disease based on a PTZ camera, which specifically comprises the following steps: step S1: acquiring road surface images of colored highways containing lane lines at different sight distances; step S2: performing Harris angular point feature extraction on the obtained pavement image, and performing straight line segment extraction on the pavement image subjected to angular point extraction to obtain a corresponding straight line segment image; step S3: calculating the pixel precision corresponding to the road surface images under different visual distances according to the straight-line segment images of the highway road surface; step S4: and calculating the image defect length of the defect area in the road surface image of the corresponding expressway according to the pixel precision obtained in the step S3, and calculating the physical length of the defect area according to the image defect length to realize the measurement of the defect length of the expressway road surface. Compared with the prior art, the method has the advantages of efficiently measuring the length of the pavement damage, reducing the pavement maintenance cost of the expressway, improving the maintenance timeliness of the pavement damage and the like.
Description
Technical Field
The invention relates to the field of pavement disease detection, in particular to a method for measuring the length of a pavement disease of an expressway based on a PTZ camera.
Background
The continuous development of national economy of China promotes the remarkable position of traffic transportation in national economy and society, and the highway used as the traffic transportation aorta is developed more rapidly. With the increase of the number of highway construction, highway maintenance becomes a key point of increasing attention of traffic management departments. The pavement disease detection can well guarantee the effectiveness of the highway maintenance. At present, the severity of highway pavement diseases is mainly evaluated by the length of the disease area. The automatic and efficient pavement disease detection technology plays an important role in the highway maintenance system and can assist highway maintenance personnel to implement correct maintenance measures. When small-sized detection diseases are repaired in time, the highway can continuously maintain the optimal service performance, meanwhile, the service life of the pavement is prolonged, and the maintenance cost of the highway pavement is reduced. Therefore, how to automatically and efficiently measure the length of the highway pavement diseases becomes one of the problems which have important practical significance in the field of pavement disease detection at present and are urgently needed to be solved.
In the past 30 years, a plurality of highway pavement disease detection methods have been proposed, and development of pavement disease length measurement methods is promoted. The existing pavement damage length measuring method can be mainly divided into a manual method and an automatic method. The manual measuring method is based on an experienced expert, and adopts an advanced pavement detection device to evaluate the pavement diseases and quantify the sizes of the pavement diseases in a walking or detection vehicle mode; typical methods include a compass measurement method, a plane measurement method, and an off-line data analysis method. The pavement damage length measuring method based on the manual measuring mode is more time-consuming and low in precision, and is more difficult to meet the high timeliness requirement of the existing highway maintenance work. Therefore, in recent years, many researchers have proposed an automatic measurement method for road surface defect length measurement, for example, a highway inspection vehicle is used for fusing collected multi-source road surface defect data to detect defects and measure the length, and a highway road surface defect detection technology based on deep learning is proposed. However, the existing automatic detection technology for highway pavement diseases is based on mobile devices, such as a patrol inspection vehicle and a mobile phone, and cannot directly measure distance information between diseases and the pavement, and meanwhile, the collected images lack integrity information, so that the requirement of road network level highway pavement maintenance workload cannot be met.
Disclosure of Invention
The invention aims to provide a method for measuring the length of a pavement defect of an expressway based on a PTZ camera, aiming at overcoming the defects that the prior art has a single detection tool and cannot meet the requirement of pavement maintenance workload of an expressway with a road network level.
The purpose of the invention can be realized by the following technical scheme:
a method for measuring the length of a pavement defect of an expressway based on a PTZ camera specifically comprises the following steps:
step S1: acquiring road surface images of colored highways containing lane lines at different sight distances;
step S2: performing Harris corner feature extraction on the road surface image obtained in the step S1, and performing straight line segment extraction on the road surface image subjected to corner extraction to obtain a straight line segment image of the highway road surface;
step S3: calculating the pixel precision corresponding to the road surface image of the expressway under different visual ranges according to the straight-line segment image of the expressway road surface in the step S2;
step S4: calculating the image disease length of the disease area in the pavement image of the corresponding expressway according to the pixel precision corresponding to the pavement image of the expressway under different visual distances, and calculating the physical length of the disease area according to the image disease length to realize the disease length measurement of the expressway pavement.
The types of the straight line segments include a vertical line segment, a horizontal line segment, a left diagonal line segment, and a right diagonal line segment.
The straight line segments of the vertical line type and the horizontal line type are median lines in candidate areas of the straight line segments determined by the corner points of the pavement image, and the straight line segments of the left diagonal line type and the right diagonal line type are diagonal lines in the candidate areas of the straight line segments determined by the corner points of the pavement image.
The step S2 specifically includes:
step S201: selecting a point P on a diagonal line or a median line in a candidate area of the straight line segment determined by the corner points as a central point;
step S202: with the point P selected in the step S201 as the center, four edge pixel points C are sequentially determined in the counterclockwise direction on the neighborhood edge with the radius r1、C2、C3And C4Which isC in1、C3Is connected to C2、C4The connecting lines are mutually vertical, and binary conversion is carried out on the four pixel points, wherein the specific conversion formula is as follows:
wherein, CiAs edge pixels, I (x)i,yi) Image pixel values, I (x), corresponding to edge pixel pointsimod4+1,yimod4+1) Representing image pixel values corresponding to adjacent positions of the edge pixel points when the edge pixel points are circularly shifted to the right, wherein T is a set edge threshold value for reducing image noise interference;
repeatedly executing the calculation steps until pixel binary conversion in all the straight-line segment candidate areas in the road surface image is completed, and obtaining a binary image corresponding to the road surface image;
step S203: counting the number of 1-value pixels in a binary image of a pavement image, establishing a window with the size of k multiplied by k by taking a single 1-value pixel as a central 1-value pixel, counting all 1-value pixels connected with the central 1-value pixel in the window to obtain a 1-value pixel set corresponding to the single 1-value pixel, and calculating a characteristic value corresponding to the single 1-value pixel according to the 1-value pixel set, wherein the characteristic value calculation formula is as follows:
wherein, λ is the characteristic value corresponding to a single 1-value pixel point, cxIs the average value of the abscissa of all 1-value pixel points in the window, cyIs a window inner placeAverage of ordinate of 1-valued pixel points, c11、c22、c12And c12Is a process variable;
and simultaneously generating a small characteristic value image, wherein a specific generation formula is as follows:
wherein, T [ g ]e(x,y)]For small feature value images, g, corresponding to a single 1-value pixel pointe(x, y) are 1-valued pixels in a window, FjA window 1 value pixel point set corresponding to all single 1 value pixel points;
step S204: and (3) executing step S203 on all 1-value pixel points in the binary image of the road surface image to generate a final small characteristic value image, and thresholding the small characteristic value image according to a thresholding formula to obtain the straight-line-section image of the highway road surface.
The thresholding formula is specifically as follows:
wherein,
for the final small eigenvalue image after thresholding,
and t is a set threshold value, wherein the small characteristic value images correspond to all the single 1-value pixel points on the final small characteristic value image which is not subjected to thresholding.
The step S3 specifically includes:
step S301: establishing a corresponding relation among a camera inclination angle, a camera sight distance, an object pixel length on an image and a corresponding actual object length, wherein the specific corresponding relation is as follows:
where f is the focal length of the PTZ camera, α is the tilt angle of the camera in the PTZ camera, liThe pixel length, ol, corresponding to the straight line segment closest to the image bottom boundary in the road surface imageiIs 1iPixel length, s, from line segment to image center pixeliIs the physical distance from the center of the plane parallel to the plate to the road surface, diIs the line of sight of the PTZ camera;
step S302: according to the highway pavement straight-line segment image obtained in the step S204, extracting a straight-line segment closest to the bottom boundary of the image and thinning the straight-line segment to obtain a straight-line segment set, meanwhile, calculating the pixel distance of each straight-line segment from the central pixel of the image, and calculating the pixel precision of the pavement image and the sight distance of the corresponding PTZ camera by combining the corresponding relation in the step S301;
step S303: and setting the sight distance interval of the road surface image needing to be collected, and executing the step S302 under different sight distances to obtain the pixel precision of the road surface image under different sight distances.
The calculation formula of the pixel precision of the road surface image and the sight distance of the PTZ camera corresponding to the pixel precision is as follows:
wherein apiFor the pixel accuracy of the road surface image, w is the standard width of the lane line specification, and m is the number of straight line segments.
The calculation formula of the physical length of the disease area is specifically as follows:
cs＝{ls·api|di-1≤ds＜di,i＝1,k，2k,…,l/2
wherein cs is the physical length of the disease region, ls is the image disease length, and k is the visual distance intervalL is the installation interval of the roadside PTZ camera, dsIs the current line of sight of the PTZ camera.
Compared with the prior art, the invention has the following beneficial effects:
1. the PTZ camera equipped in the road side monitoring system is adopted, and based on the camera imaging principle, the image processing method is adopted to measure the actual length of the diseases on the highway pavement image, so that the interference of the dynamic environment on the image processing algorithm is reduced, and the PTZ camera can be quickly applied to the road side monitoring system of the domestic highway, and the efficient and stable road surface disease length measurement is realized.
2. The invention can save the maintenance cost of the highway pavement by means of the PTZ camera which is the existing high-definition camera equipment at the highway side without additional hardware cost investment.
3. According to the method, the color image of the highway pavement is obtained, the pixel precision of the image under different camera views is calculated, the physical length of the disease is measured by calculating the pixel length of the detected disease image area under different viewing distances, statistics of the disease length under different road sections and different viewing distances of the highway is realized, scientific reference is provided for highway pavement maintenance personnel, the disease beyond the maintenance requirement range is repaired in time, the best maintenance period of the highway is obtained in time, and the service quality and the service performance of the highway are guaranteed.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of the principles and application scenarios of the present invention;
FIG. 3 is a schematic diagram of a straight line segment type defined by corner points according to the present invention;
FIG. 4 is a diagram illustrating the results of the detection of a straight line segment according to the present invention;
FIG. 5 is a diagram showing the measurement results of the lesion length according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 1, a method for measuring length of a road surface defect of an expressway based on a PTZ camera specifically includes the following steps:
step S1: acquiring road surface images of colored highways containing lane lines at different sight distances;
step S2: performing Harris corner feature extraction on the road surface image obtained in the step S1, and performing straight line segment extraction on the road surface image subjected to corner extraction to obtain a straight line segment image of the highway road surface;
step S3: calculating the pixel precision corresponding to the road surface image of the expressway under different visual ranges according to the straight-line segment image of the expressway road surface in the step S2;
step S4: calculating the image disease length of the disease area in the pavement image of the corresponding expressway according to the pixel precision corresponding to the pavement image of the expressway under different visual distances, and calculating the physical length of the disease area according to the image disease length to realize the disease length measurement of the expressway pavement.
As shown in fig. 3, the types of the straight line segments include a vertical line type, a horizontal line type, a left diagonal line type, and a right diagonal line type, the straight line segments of the vertical line type and the horizontal line type being median lines in candidate areas of the straight line segments determined by the corner points of the road surface image, and the straight line segments of the left diagonal line type and the right diagonal line type being diagonal lines in candidate areas of the straight line segments determined by the corner points of the road surface image.
Step S2 specifically includes:
step S201: determining straight-line segment information in an image between image corners according to the definition of the corners, namely determining one corner in the image, wherein at least two line segments or edges with different directions exist near the corner, determining a straight-line segment candidate area based on a corner sequence extracted from the highway pavement image, and selecting a point P on a diagonal line or a median line in the candidate area as a central point;
step S202: with the point P selected in the step S201 as the center, four edge pixel points C are sequentially determined in the counterclockwise direction on the neighborhood edge with the radius r1、C2、C3And C4In which C is1、C3Is connected to C2、C4The connecting lines are mutually vertical, and binary conversion is carried out on the four pixel points, wherein the specific conversion formula is as follows:
wherein, CiAs edge pixels, I (x)i,yi) Image pixel values, I (x), corresponding to edge pixel pointsimod4+1,yimod4+1) Representing image pixel values corresponding to adjacent positions of the edge pixel points when the edge pixel points are circularly shifted to the right, wherein T is a set edge threshold value for reducing image noise interference;
repeatedly executing the calculation steps until pixel binary conversion in all the straight-line segment candidate areas in the road surface image is completed, and obtaining a binary image corresponding to the road surface image;
step S203: counting the number of 1-value pixels in a binary image of a road surface image, establishing a window with the size of k multiplied by k for 1-value pixels pi (i is 1,2, …, J) by taking a single 1-value pixel as a central 1-value pixel, counting all 1-value pixels connected with the central 1-value pixel in the window, and obtaining a 1-value pixel set F corresponding to the single 1-value pixeli＝{pi(xi,yi) And i is 1,2, …, n, calculating a feature value corresponding to a single 1-value pixel point according to the 1-value pixel set, wherein the feature value calculation formula is as follows:
wherein, λ is the characteristic value corresponding to a single 1-value pixel point, cxIs the average value of the abscissa of all 1-value pixel points in the window, cyIs the average value of the vertical coordinates of all 1-value pixel points in the window, c11、c22、c12And c12Is a process variable;
and simultaneously generating a small characteristic value image, wherein a specific generation formula is as follows:
wherein, T [ g ]e(x,y)]For small feature value images, g, corresponding to a single 1-value pixel pointe(x, y) are 1-valued pixels in a window, FjA window 1 value pixel point set corresponding to all single 1 value pixel points;
step S204: step S203 is executed on all 1-value pixel points in the binary image of the road surface image, a final small feature value image is generated, thresholding is performed on the small feature value image according to a thresholding formula, and an expressway road surface straight-line segment image is obtained, as shown in fig. 4, a result of performing straight-line segment detection on the kyanite expressway road surface image by using the straight-line segment detection method provided by the invention is obtained, the camera view distance corresponding to fig. 4(a) is 157m, and the camera view distance corresponding to fig. 4(b) is 227 m.
The thresholding formula is specifically:
wherein,
for the final small eigenvalue image after thresholding,
and t is a set threshold value, wherein the small characteristic value images correspond to all the single 1-value pixel points on the final small characteristic value image which is not subjected to thresholding.
Step S3 specifically includes:
step S301: establishing a corresponding relation among a camera inclination angle, a camera sight distance, an object pixel length on an image and a corresponding actual object length, wherein the specific corresponding relation is as follows:
wherein f is the focal length of the PTZ camera, alpha is the inclination angle of the camera in the PTZ camera, namely the included angle between an imaging photosensitive plate (CCD/CMOS) and the vertical line of the road surface, and liThe pixel length, ol, corresponding to the straight line segment closest to the image bottom boundary in the road surface imageiIs 1iPixel length, s, from line segment to image center pixeliIs the physical distance from the center of the plane parallel to the plate to the road surface, diIs the line of sight of the PTZ camera;
as shown in fig. 2, a plane parallel to the photosensitive plate is a virtual plane, which is difficult to be directly calculated in real situations, so that the tangent position between the virtual plane and the road surface cannot be directly obtained, and the image is projected on the road surface to be nonlinearly stretched from the bottom to the top of the image, so that a straight line segment closest to the bottom of the image on the highway road surface is selected, that is, the straight line segment in fig. 2 is obtained, and the pixel length l corresponding to the vertical virtual line segment in the image area is obtainediApproximately as the pixel length corresponding to the intersection line segment of the lane line in the horizontal direction when the plane parallel to the light-sensitive plate and the road surface tangent line are projected on the image;
step S302: according to the straight-line segment image of the highway pavement acquired in step S204, the straight-line segment closest to the bottom boundary of the image is extracted, such as the straight-line segment l shown in fig. 21，l2，l3Thinning the straight line segment to obtain a straight line segment set li(i 1,2, … m) while calculating the pixel distance ol of each straight line segment from the center pixel of the imagei(i is 1,2, … m), the pixel accuracy of the road surface image and the view distance of the corresponding PTZ camera are calculated by combining the correspondence in step S301, and the calculation formula of the pixel accuracy of the road surface image and the view distance of the corresponding PTZ camera is specifically as follows:
wherein apiThe pixel precision of the road surface image is shown, w is the standard width of the lane line specification, and m is the number of straight line segments;
step S303: using k as the camera sight distance interval to collect different camera sight distances d in sequencei(i-k, i-2 k, …, l/2) corresponding highway road surface images, executing step S302 under different visual distances, and counting di(i-k, i-2 k, …, l/2) to obtain the pixel accuracy AP-AP at different camera viewing distancesi1, k,2k, …, l/2}, as shown in table 1, a road side PTZ camera view distance corresponding to a road surface image pixel precision statistical result obtained by calculating a road surface image acquired from a certain road section of a kyanite highway, where table 1 includes the following specific contents:
TABLE 1 comparison table of camera sight distance and road surface image pixel precision of certain section of kyanite highway
Step S4 is embodied as a step for a given viewing distance dsDetecting the diseases of the collected highway pavement images containing the diseases to obtain a disease area, calculating the length of the image diseases corresponding to the disease area, and calculating the physical length of the disease area according to the length of the image diseases, wherein the calculation formula of the physical length of the disease area is as follows:
cs＝{ls·api|di-1≤ds＜di,i＝1,k,2k,…,l/2
wherein cs is the physical length of the disease area, ls is the image disease length, k is the view distance interval, l is the installation interval of the roadside PTZ camera, dsAs the current view of PTZ cameraDistance;
fig. 5 shows the beijing stone highway pavement images collected under the camera sight distance of 15 meters and 40 meters, wherein the physical length of the damaged area in fig. 5(a) is 148.59mm, and the physical length of the damaged area in fig. 5(b) is 476.95 mm.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. Minor or simple variations in the structure, features and principles of the present invention are included within the scope of the present invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (5)
1. A method for measuring the length of a pavement defect of an expressway based on a PTZ camera is characterized by comprising the following steps:
step S1: acquiring road surface images of colored highways containing lane lines at different sight distances;
step S2: performing Harris corner feature extraction on the road surface image obtained in the step S1, and performing straight line segment extraction on the road surface image subjected to corner extraction to obtain a straight line segment image of the highway road surface;
step S3: calculating the pixel precision corresponding to the road surface image of the expressway under different visual ranges according to the straight-line segment image of the expressway road surface in the step S2;
step S4: calculating the image disease length of the disease area in the pavement image of the corresponding expressway according to the pixel precision corresponding to the pavement image of the expressway under different visual distances, and calculating the physical length of the disease area according to the image disease length to realize the disease length measurement of the expressway pavement;
the step S3 specifically includes:
step S301: establishing a corresponding relation among a camera inclination angle, a camera sight distance, an object pixel length on an image and a corresponding actual object length, wherein the specific corresponding relation is as follows:
where f is the focal length of the PTZ camera, α is the tilt angle of the camera in the PTZ camera, liThe pixel length, ol, corresponding to the straight line segment closest to the image bottom boundary in the road surface imageiIs 1iPixel length, s, from line segment to image center pixeliIs the physical distance from the center of the plane parallel to the plate to the road surface, diThe view distance of the PTZ camera is shown, and w is the standard width of the lane line specification;
step S302: according to the highway pavement straight-line segment image obtained in the step S204, extracting a straight-line segment closest to the bottom boundary of the image and thinning the straight-line segment to obtain a straight-line segment set, meanwhile, calculating the pixel distance of each straight-line segment from the central pixel of the image, and calculating the pixel precision of the pavement image and the sight distance of the corresponding PTZ camera by combining the corresponding relation in the step S301;
step S303: setting the sight distance interval of the road surface image to be acquired, and executing the step S302 under different sight distances to obtain the pixel precision of the road surface image under different sight distances;
the calculation formula of the pixel precision of the road surface image and the sight distance of the PTZ camera corresponding to the pixel precision is as follows:
wherein apiThe pixel precision of the road surface image is shown, w is the standard width of the lane line specification, and m is the number of straight line segments;
the above-mentionedStep S4 is embodied as a step for a given viewing distance dsDetecting the diseases of the collected highway pavement images containing the diseases to obtain a disease area, calculating the length of the image diseases corresponding to the disease area, and calculating the physical length of the disease area according to the length of the image diseases, wherein the calculation formula of the physical length of the disease area is as follows:
cs＝{ls·api|di-1≤ds＜di,i＝1,k,2k,…,l/2}
wherein cs is the physical length of the disease area, ls is the image disease length, k is the view distance interval, l is the installation interval of the roadside PTZ camera, dsIs the current line of sight of the PTZ camera.
2. The PTZ camera-based highway pavement damage length measuring method according to claim 1, wherein the types of the straight line segments comprise a vertical line segment, a horizontal line segment, a left diagonal line segment and a right diagonal line segment.
3. The PTZ camera-based highway pavement damage length measuring method according to claim 2, wherein the straight line segments of the vertical line type and the horizontal line type are median lines in candidate areas of the straight line segments determined by the corner points of the pavement image, and the straight line segments of the left diagonal line type and the right diagonal line type are diagonal lines in candidate areas of the straight line segments determined by the corner points of the pavement image.
4. The method for measuring the length of the pavement damage of the expressway based on the PTZ camera as claimed in claim 3, wherein the step S2 specifically comprises the following steps:
step S201: selecting a point P on a diagonal line or a median line in a candidate area of a straight line segment determined by the corner points as a central point;
step S202: with the point P selected in the step S201 as the center, four edge pixel points C are sequentially determined in the counterclockwise direction on the neighborhood edge with the radius r1、C2、C3And C4In which C is1、C3Of (2) a connection lineAnd C2、C4The connecting lines are mutually vertical, and binary conversion is carried out on the four pixel points, wherein the specific conversion formula is as follows:
wherein, CiAs edge pixels, I (x)i,yi) Image pixel values, I (x), corresponding to edge pixel pointsimod4+1,yimod4+1) Representing image pixel values corresponding to adjacent positions of the edge pixel points when the edge pixel points are circularly shifted to the right, wherein T is a set edge threshold value for reducing image noise interference;
repeatedly executing the calculation steps until pixel binary conversion in all the straight-line segment candidate areas in the road surface image is completed, and obtaining a binary image corresponding to the road surface image;
step S203: counting the number of 1-value pixels in a binary image of a pavement image, establishing a window with the size of k multiplied by k by taking a single 1-value pixel as a central 1-value pixel, counting all 1-value pixels connected with the central 1-value pixel in the window to obtain a 1-value pixel set corresponding to the single 1-value pixel, and calculating a characteristic value corresponding to the single 1-value pixel according to the 1-value pixel set, wherein the characteristic value calculation formula is as follows:
wherein, λ is the characteristic value corresponding to a single 1-value pixel point, cxIs the average value of the abscissa of all 1-value pixel points in the window, cyIs the average value of the vertical coordinates of all 1-value pixel points in the window, c11、c22、c12And c21Is a process variable;
and simultaneously generating a small characteristic value image, wherein a specific generation formula is as follows:
wherein, T [ g ]e(x,y)]For small feature value images, g, corresponding to a single 1-value pixel pointe(x, y) are 1-valued pixels in a window, FjA window 1 value pixel point set corresponding to all single 1 value pixel points;
step S204: and (3) executing step S203 on all 1-value pixel points in the binary image of the road surface image to generate a final small characteristic value image, and thresholding the small characteristic value image according to a thresholding formula to obtain the straight-line-section image of the highway road surface.
5. The method for measuring the length of the highway pavement damage based on the PTZ camera as claimed in claim 4, wherein the thresholding formula is specifically as follows:
wherein,
for the final small eigenvalue image after thresholding,
and t is a set threshold value, wherein the small characteristic value images correspond to all the single 1-value pixel points on the final small characteristic value image which is not subjected to thresholding.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509291A (en) * | 2011-10-31 | 2012-06-20 | 东南大学 | Pavement disease detecting and recognizing method based on wireless online video sensor |
| CN103345542A (en) * | 2013-06-05 | 2013-10-09 | 西安理工大学 | Method for marking bridge disease based on three-dimensional visualization technology |
| CN106053475A (en) * | 2016-05-24 | 2016-10-26 | 浙江工业大学 | Tunnel disease full-section dynamic rapid detection device based on active panoramic vision |
| CN106296691A (en) * | 2016-08-11 | 2017-01-04 | 梁晓东 | Pavement disease recognition methods based on graphical analysis |
| CN107633516A (en) * | 2017-09-21 | 2018-01-26 | 武汉武大卓越科技有限责任公司 | A kind of method and apparatus for identifying surface deformation class disease |
| CN108733053A (en) * | 2018-04-23 | 2018-11-02 | 上海圭目机器人有限公司 | A kind of Intelligent road detection method based on robot |
| CN109870456A (en) * | 2019-02-01 | 2019-06-11 | 上海智能交通有限公司 | A kind of road surface health status rapid detection system and method |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103955923B (en) * | 2014-04-18 | 2017-03-01 | 南京理工大学 | A kind of quickly pavement disease detection method based on image |
| CN104021574B (en) * | 2014-07-04 | 2016-12-07 | 武汉武大卓越科技有限责任公司 | Pavement disease automatic identifying method |
| US10480939B2 (en) * | 2016-01-15 | 2019-11-19 | Fugro Roadware Inc. | High speed stereoscopic pavement surface scanning system and method |
| CN106677037B (en) * | 2016-11-21 | 2020-11-27 | 同济大学 | Portable asphalt pavement disease detection method and device based on machine vision |
| CN106778541B (en) * | 2016-11-28 | 2019-08-13 | 华中科技大学 | A kind of identification in the multilayer beam China and foreign countries beam hole of view-based access control model and localization method |
| CN107462223B (en) * | 2017-08-08 | 2020-06-16 | 长安大学 | Automatic measuring device and method for sight distance of vehicle before turning on highway |
| CN107609486A (en) * | 2017-08-16 | 2018-01-19 | 中国地质大学（武汉） | To anti-collision early warning method and system before a kind of vehicle |
| CN109934452A (en) * | 2019-01-21 | 2019-06-25 | 上海同济检测技术有限公司 | Road Comfort Evaluation method based on multi-source data |
-
2019
- 2019-11-21 CN CN201911148337.6A patent/CN110969103B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509291A (en) * | 2011-10-31 | 2012-06-20 | 东南大学 | Pavement disease detecting and recognizing method based on wireless online video sensor |
| CN103345542A (en) * | 2013-06-05 | 2013-10-09 | 西安理工大学 | Method for marking bridge disease based on three-dimensional visualization technology |
| CN106053475A (en) * | 2016-05-24 | 2016-10-26 | 浙江工业大学 | Tunnel disease full-section dynamic rapid detection device based on active panoramic vision |
| CN106296691A (en) * | 2016-08-11 | 2017-01-04 | 梁晓东 | Pavement disease recognition methods based on graphical analysis |
| CN107633516A (en) * | 2017-09-21 | 2018-01-26 | 武汉武大卓越科技有限责任公司 | A kind of method and apparatus for identifying surface deformation class disease |
| CN108733053A (en) * | 2018-04-23 | 2018-11-02 | 上海圭目机器人有限公司 | A kind of Intelligent road detection method based on robot |
| CN109870456A (en) * | 2019-02-01 | 2019-06-11 | 上海智能交通有限公司 | A kind of road surface health status rapid detection system and method |
Non-Patent Citations (1)
| Title |
|---|
| 吉林省高速公路路面路基病害同步检测的技术研究;刘若川;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20160315(第3期);第C034-198页 * |
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