CN113324581A - High-precision non-contact type slope dangerous rock monitoring and early warning method - Google Patents
High-precision non-contact type slope dangerous rock monitoring and early warning method Download PDFInfo
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Abstract
The invention provides a high-precision non-contact type slope dangerous rock monitoring and early warning method, which specifically comprises the following steps: laying a monitoring instrument; calibrating a camera; starting a vibration measurement module to obtain a speed time course of the slope dangerous rock surface; starting a large-scale particle image speed measuring module to obtain a slope dangerous rock image; the calculation module calculates the vibration dominant frequency and the displacement change rate of the slope dangerous rock according to the speed time course and the slope dangerous rock image; monitoring the vibration dominant frequency and the displacement change rate of the slope dangerous rock in real time, sending out early warning when the vibration dominant frequency and the displacement change rate reach a preset threshold value, and adjusting the shooting frequency of the large-scale particle image speed measurement module according to early warning information; the method is based on a large-scale particle image speed measurement technology and a laser vibration measurement technology, realizes remote, non-contact and real-time monitoring and early warning on the slope dangerous rock, is superior to the traditional slope dangerous rock monitoring method, and has a good application prospect.
Description
Technical Field
The invention relates to the technical field of slope dangerous rock monitoring and early warning, in particular to a high-precision non-contact slope dangerous rock monitoring and early warning method.
Background
China is a country with frequent collapse disasters, and life and property losses caused by dangerous rock collapse are huge every year. Monitoring and early warning of side slope dangerous rocks are always research hotspots and difficulties in disaster prevention and reduction engineering. The purpose of the slope dangerous rock monitoring and early warning is to timely find and master the displacement motion state of a dangerous rock block body and monitor the displacement speed and direction of the slope dangerous rock, the stability of the dangerous rock and the like. The research on an effective and convenient slope dangerous rock monitoring and early warning method is of great significance and is related to the life and property safety of people.
However, most of the existing slope dangerous rock monitoring and early warning technologies are contact monitoring methods, and have the defects that the monitoring instruments are complicated to arrange, the danger in the arrangement process is high, and only single-point monitoring can be realized. Although the existing non-contact type monitoring method for dangerous rock on side slope overcomes many defects of the contact type method, the existing non-contact type monitoring method still has the defects of low measurement precision, expensive instrument and equipment and the like.
Disclosure of Invention
The invention solves the problem of providing a high-precision non-contact type slope dangerous rock monitoring and early warning method, the large-scale particle image speed measurement technology is an image speed measurement technology which is developed on the basis of the particle image speed measurement technology and is specially used for measuring the displacement and the speed of a large-scale research object, and the method is based on the large-scale particle image speed measurement technology and the laser vibration measurement technology, realizes remote, non-contact and real-time monitoring and early warning on the slope dangerous rock, is superior to the traditional slope dangerous rock monitoring method, and has good application prospect.
A high-precision non-contact type slope dangerous rock monitoring and early warning method comprises the following steps:
laying a monitoring instrument;
calibrating a camera;
starting a vibration measurement module to obtain a speed time course of the slope dangerous rock surface;
starting a large-scale particle image speed measuring module to obtain a slope dangerous rock image;
the calculation module calculates the vibration dominant frequency and the displacement change rate of the slope dangerous rock according to the speed time course and the slope dangerous rock image;
and monitoring the vibration dominant frequency and the displacement change rate of the slope dangerous rock in real time, sending out early warning when the vibration dominant frequency and the displacement change rate reach a preset threshold value, and adjusting the shooting frequency of the large-scale particle image speed measurement module according to early warning information.
Furthermore, the detecting instrument comprises a large-scale particle image speed measuring module, a vibration measuring module, a calculating module, an information transmission module and a power supply module.
Furthermore, the monitoring instrument is arranged on a side slope dangerous rock site, a camera in the large-scale particle image speed measuring module can be opposite to the front side of the side slope, and the camera can shoot and acquire a complete and clear side slope dangerous rock image by adjusting the pixel and the focal length of the camera.
Further, the camera calibration includes: and arranging a camera calibration plate with a light reflecting area on the surface of the slope dangerous rock, and shooting by using a camera in the large-scale particle image speed measuring module to obtain a calibration image.
Further, the calculation module carries out fast Fourier transform processing on the speed time course of the dangerous rock surface to obtain the vibration dominant frequency of the side slope dangerous rock vibration; and the calculation module performs image processing on the side slope dangerous rock image acquired at the moment and the side slope dangerous rock image acquired at the last moment, and calculates to obtain the displacement value, the speed value and the displacement change rate of the side slope dangerous rock.
Further, the image processing includes: image preprocessing, image matching, image postprocessing and coordinate conversion processing according to a camera calibration result.
Further, the image preprocessing process uses gray level conversion, Gaussian filtering noise reduction and histogram equalization algorithm for processing;
the formula of the gray scale conversion algorithm is shown as follows:
Grey＝0.299＊R+0.587＊G+0.114＊B
where, Grey — converted pixel grayscale value;
r, G, B-pixel values of three color channels of color image red, green, and blue, respectively;
the algorithm formula of the gaussian filtering noise reduction is shown as follows:
wherein σ -is the standard deviation;
g (x, y) -the weighting coefficient value of each pixel point in the neighborhood;
the formula of the histogram equalization algorithm is shown as follows:
in the formula, L is the number of the division of the image gray value interval, and is called the total number of gray levels;
n is the total number of pixels of the image;
nj-the number of pixels for the current gray level;
sk-a grey distribution frequency value for a grey value interval.
The image matching process adopts a least square error algorithm to process;
the formula of the least squares difference algorithm is as follows:
in the formula, G1And G2-for two matrices, the elements represent the gray values of the calculation windows in two image pairs;
m and N — the length of the selected calculation window in the x, y direction, typically M ═ N;
g1and g2-representing the gray value distribution function of the calculation window in the two image pairs, respectively;
Δ x and Δ y, displacement values corresponding to the calculation windows in the image after the object is deformed;
the image post-processing process adopts signal-to-noise ratio filtering, inverse distance weight interpolation and a three-point Gaussian sub-pixel fitting algorithm for processing;
the snr filtering algorithm is formulated as follows:
in the formula, p1-is the cross correlation coefficient peak;
p2-is the second peak of the cross-correlation coefficient;
the formula of the used inverse distance weight interpolation algorithm is shown as the following formula:
in the formula, Zi-a calculated value for discrete point i;
z-is an estimated value;
n is the number of discrete points participating in calculation;
Di-estimating a distance value of a point for the ith point distance;
p-is the power of the distance, typically taken to be 2;
the formula of the three-point Gaussian sub-pixel fitting algorithm is shown as follows:
in the formula, Ci，j-calculating the cross correlation coefficient peak value corresponding to the window;
i, j-is the integer pixel coordinate value of the point corresponding to the peak value of the cross correlation coefficient;
and deltax and deltay are the difference between the actual cross-correlation coefficient peak value and the integer coordinate obtained after interpolation fitting.
Further, when the large-scale particle image velocimetry module is started, the large-scale particle image velocimetry module operates in a low-frequency monitoring mode.
Further, when the displacement change rate of the slope dangerous rock is increased and is greater than a first preset threshold value and smaller than a second preset threshold value, or the vibration dominant frequency of the slope dangerous rock is reduced and the reduction value reaches a first preset amplitude, an orange early warning is sent out; when the monitoring instrument sends out an orange early warning, the large-scale particle image speed measurement module starts a medium-frequency monitoring mode.
Further, when the displacement change rate of the slope dangerous rock is increased and is greater than a second preset threshold value, a red early warning is sent out; when the monitoring instrument sends out red early warning, the large-scale particle image speed measurement module starts a high-frequency monitoring mode.
The high-precision non-contact type slope dangerous rock monitoring and early warning method provided by the invention utilizes a large-scale particle image speed measurement technology and a laser vibration measurement technology, and the technologies are combined for use, so that the remote, non-contact and real-time monitoring and early warning of the slope dangerous rock are realized, the method is superior to the traditional slope dangerous rock monitoring method, and the method has a good application prospect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a high-precision non-contact slope dangerous rock monitoring and early warning method of the invention;
FIG. 2 is a flow chart of the present invention;
FIG. 3 is a graph of the initial dominant frequency of vibration of slope rock crisis;
FIG. 4 is a displacement cloud chart of slope dangerous rocks;
FIG. 5 is a displacement variation diagram of slope dangerous rock;
FIG. 6 is a frequency variation diagram of slope dangerous rock;
FIG. 7 is a red early warning displacement change diagram of slope dangerous rock;
wherein: 1-large scale particle image velocimetry module; 2-a vibration measuring module; 3-a calculation module; 4-an information transmission module; 5-power supply module.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a high-precision non-contact type slope dangerous rock monitoring and early warning method, which comprises the following steps of:
step S1: and laying a monitoring instrument.
The monitoring instrument includes five modules: the device comprises a large-scale particle image speed measurement module, a vibration measurement module, a calculation module, an information transmission module and a power supply module. As shown in fig. 1: the monitoring instrument is arranged on a side slope dangerous rock site, a camera in the large-scale particle image speed measuring module can be opposite to the front side of the side slope, and the camera can shoot and acquire a complete and clear side slope dangerous rock image by adjusting the pixels and the focal length of the camera.
Step S2: and calibrating the camera.
The camera calibration plate with the light reflecting area is arranged on the surface of the slope dangerous rock, and 14-20 calibration images are shot and obtained by a camera in the large-scale particle image speed measuring module.
And the calculation module calculates the calibration image by using a Zhang's plane calibration method, and solves the internal parameters and the external parameters of the camera and the radial distortion coefficient of the camera lens.
In this embodiment, the camera built-in parameters are: f. ofx＝3932.3，fy＝3933.3，u0＝1974.4，v01299.7, k is 0; the external parameters of the camera are as follows:
α＝-0.0934，β＝-0.0309，γ＝1.5837，t1＝56.0561，t2＝-11.0637，t3954.2009. The radial distortion coefficient is: k is a radical of1＝-0.0358，k2＝-0.0455；。
The camera parameters are variable values, with different parameters for different types of cameras.
Step S3: and starting the vibration measuring module to obtain the speed time course of the slope dangerous rock surface.
Starting the vibration measuring module, adjusting the orientation of the vibration measuring module, enabling laser emitted by the vibration measuring module to point to a light reflecting area of the camera calibration plate, receiving the reflected laser by the vibration measuring module, obtaining the speed time course of the surface of the dangerous rock, performing fast Fourier transform processing through the calculating module, and obtaining the dominant frequency of the side slope dangerous rock vibration, wherein the used vibration measuring module is preferably a laser Doppler vibration meter, the monitoring frequency is 1 day/time, and the measurement is continuously carried out for 15min at the sampling frequency of 1000Hz every time.
Step S4: and starting the large-scale particle image speed measuring module to obtain a slope dangerous rock image.
When the large-scale particle image speed measurement module is started, the large-scale particle image speed measurement module operates in a low-frequency monitoring mode, the low-frequency monitoring mode refers to that a lower camera frequency is set to shoot and obtain a slope dangerous rock image, for example, the shooting frequency can be one image per hour.
The speed measuring module and the vibration measuring module in the steps S3 and S4 are started simultaneously, and there is no sequence, but the sampling frequencies of the two modules are different.
Step S5: and the calculation module calculates the vibration dominant frequency and the displacement change rate of the slope dangerous rock according to the speed time course and the slope dangerous rock image.
The calculation module calculates and transmits the calculation results of the large-scale particle image speed measurement module and the vibration measurement module in real time, and the calculation results are sent to the platform through the signal transmission module.
The calculation module carries out fast Fourier transform processing on the speed time course of the dangerous rock surface to obtain the vibration dominant frequency of the slope dangerous rock vibration; and the calculation module performs image processing on the side slope dangerous rock image acquired at the moment and the side slope dangerous rock image acquired at the last moment, and calculates to obtain the displacement value, the speed value and the displacement change rate of the side slope dangerous rock.
The image processing process comprises the following steps: image preprocessing, image matching, image postprocessing and coordinate conversion processing according to a camera calibration result.
The image preprocessing process uses gray level conversion, Gaussian filtering noise reduction and histogram equalization algorithm for processing.
The formula of the gray scale conversion algorithm is shown as the following formula.
Grey＝0.299＊R+0.587＊G+0.114＊B
Where, Grey — converted pixel grayscale value;
r, G, B-pixel values for the three color channels of the color image red, green, and blue, respectively.
The algorithm formula of the gaussian filtering noise reduction is shown as the following formula.
Wherein σ -is the standard deviation;
g (x, y) -the weighting factor value for each pixel in the neighborhood.
The formula of the histogram equalization algorithm is shown in the following formula.
In the formula, L is the number of the division of the image gray value interval, and is called the total number of gray levels;
n is the total number of pixels of the image;
nj-the number of pixels for the current gray level;
sk-a grey distribution frequency value for a grey value interval.
And the image matching process adopts a least square error algorithm for processing.
The formula of the least squares difference algorithm is shown below.
In the formula, G1And G2-for two matrices, the elements represent the gray values of the calculation windows in two image pairs;
m and N — the length of the selected calculation window in the x, y direction, typically M ═ N;
g1and g2-representing the gray value distribution function of the calculation window in the two image pairs, respectively;
and deltax and deltay are displacement values corresponding to the calculation window in the image after the object is deformed.
And in the image post-processing process, signal-to-noise ratio filtering, inverse distance weight interpolation and a three-point Gaussian sub-pixel fitting algorithm are adopted for processing.
The snr filtering algorithm is formulated as follows.
In the formula, p1-is the cross correlation coefficient peak;
p2-the second peak of the cross correlation coefficient.
The formula of the used inverse distance weight interpolation algorithm is shown in the following formula.
In the formula, Zi-a calculated value for discrete point i;
z-is an estimated value;
n is the number of discrete points participating in calculation;
Di-estimating a distance value of a point for the ith point distance;
p-is the power of the distance, typically taken as 2.
The formula of the three-point Gaussian sub-pixel fitting algorithm is shown as the following formula.
In the formula, Ci，j-calculating the cross correlation coefficient peak value corresponding to the window;
i, j-is the integer pixel coordinate value of the point corresponding to the peak value of the cross correlation coefficient;
and deltax and deltay are the difference between the actual cross-correlation coefficient peak value and the integer coordinate obtained after interpolation fitting.
Step S6: and monitoring the vibration dominant frequency and the displacement change rate of the slope dangerous rock in real time, sending out early warning when the vibration dominant frequency and the displacement change rate reach a preset threshold value, and adjusting the shooting frequency of the large-scale particle image speed measurement module according to early warning information.
Step S610: when the displacement change rate of the side slope dangerous rock is increased and is greater than a first preset threshold and smaller than a second preset threshold, or the vibration dominant frequency of the side slope dangerous rock is reduced and the reduction value reaches a first preset amplitude, an orange early warning is sent out;
the first preset threshold may be 2mm/h, and the first preset amplitude may be 5% of the initial frequency size.
Step S620: when the monitoring instrument sends out an orange early warning, the large-scale particle image speed measurement module starts a medium-frequency monitoring mode.
The shooting frequency of the camera in the large-scale particle image speed measurement module is improved, for example, the camera obtains a side slope dangerous rock image at the frequency of taking one image every 10 seconds, and the side slope dangerous rock is monitored at the intermediate frequency.
Step S630: and when the displacement change rate of the slope dangerous rock is increased and is greater than a second preset threshold value, a red early warning is sent out.
The second preset threshold may be 5 mm/h.
Step S640: when the monitoring instrument sends out red early warning, the large-scale particle image speed measurement module starts a high-frequency monitoring mode.
The shooting frequency of the camera in the large-scale particle image speed measurement module is further improved, for example, the camera obtains a slope dangerous rock image at the frequency of shooting one image per second, high-frequency monitoring is carried out on the slope dangerous rock, and displacement value and speed value information of the slope dangerous rock are sent in real time.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention.
Claims (10)
1. A high-precision non-contact type slope dangerous rock monitoring and early warning method is characterized by comprising the following steps:
laying a monitoring instrument;
calibrating a camera;
starting a vibration measurement module to obtain a speed time course of the slope dangerous rock surface;
starting a large-scale particle image speed measuring module to obtain a slope dangerous rock image;
the calculation module calculates the vibration dominant frequency and the displacement change rate of the slope dangerous rock according to the speed time course and the slope dangerous rock image;
and monitoring the vibration dominant frequency and the displacement change rate of the slope dangerous rock in real time, sending out early warning when the vibration dominant frequency and the displacement change rate reach a preset threshold value, and adjusting the shooting frequency of the large-scale particle image speed measurement module according to early warning information.
2. The high-precision non-contact slope dangerous rock monitoring and early warning method as claimed in claim 1, wherein the detecting instrument comprises a large-scale particle image speed measuring module, a vibration measuring module, a calculating module, an information transmission module and a power supply module.
3. The high-precision non-contact type slope dangerous rock monitoring and early warning method as claimed in claim 2, wherein the monitoring instrument is arranged on a slope dangerous rock site, a camera in the large-scale particle image speed measurement module can be over against the front face of the slope, and the camera can shoot and obtain a complete and clear slope dangerous rock image by adjusting the pixels and the focal length of the camera.
4. The high-precision non-contact slope dangerous rock monitoring and early warning method according to claim 1, wherein the camera calibration comprises: and arranging a camera calibration plate with a light reflecting area on the surface of the slope dangerous rock, and shooting by using a camera in the large-scale particle image speed measuring module to obtain a calibration image.
5. The high-precision non-contact type slope dangerous rock monitoring and early warning method according to claim 1, characterized in that the calculation module performs fast Fourier transform processing on the speed time course of the dangerous rock surface to obtain the vibration dominant frequency of the slope dangerous rock vibration; and the calculation module performs image processing on the side slope dangerous rock image acquired at the moment and the side slope dangerous rock image acquired at the last moment, and calculates to obtain the displacement value, the speed value and the displacement change rate of the side slope dangerous rock.
6. The high-precision non-contact slope dangerous rock monitoring and early warning method according to claim 5, wherein the image processing comprises: image preprocessing, image matching, image postprocessing and coordinate conversion processing according to a camera calibration result.
7. The high-precision non-contact side slope dangerous rock monitoring and early warning method according to claim 6, characterized in that the image preprocessing process uses gray level conversion, Gaussian filtering noise reduction and histogram equalization algorithm for processing;
the formula of the gray scale conversion algorithm is shown as follows:
Grey＝0.299＊R+0.587＊G+0.114＊B
where, Grey — converted pixel grayscale value;
r, G, B-pixel values of three color channels of color image red, green, and blue, respectively;
the algorithm formula of the gaussian filtering noise reduction is shown as follows:
wherein σ -is the standard deviation;
g (x, y) -the weighting coefficient value of each pixel point in the neighborhood;
the formula of the histogram equalization algorithm is shown as follows:
in the formula, L is the number of the division of the image gray value interval, and is called the total number of gray levels;
n is the total number of pixels of the image;
nj-the number of pixels for the current gray level;
sk-a grey distribution frequency value for a grey value interval.
The image matching process adopts a least square error algorithm to process;
the formula of the least squares difference algorithm is as follows:
in the formula, G1And G2Two matrices, the elements representing two frames of the graphCalculating a grey value of a window in the image pair;
m and N — the length of the selected calculation window in the x, y direction, typically M ═ N;
g1and g2-representing the gray value distribution function of the calculation window in the two image pairs, respectively;
Δ x and Δ y, displacement values corresponding to the calculation windows in the image after the object is deformed;
the image post-processing process adopts signal-to-noise ratio filtering, inverse distance weight interpolation and a three-point Gaussian sub-pixel fitting algorithm for processing;
the snr filtering algorithm is formulated as follows:
in the formula, p1-is the cross correlation coefficient peak;
p2-is the second peak of the cross-correlation coefficient;
the formula of the used inverse distance weight interpolation algorithm is shown as the following formula:
in the formula, Zi-a calculated value for discrete point i;
z-is an estimated value;
n is the number of discrete points participating in calculation;
Di-estimating a distance value of a point for the ith point distance;
p-is the power of the distance, typically taken to be 2;
the formula of the three-point Gaussian sub-pixel fitting algorithm is shown as follows:
in the formula, Ci，j-calculating the cross correlation coefficient peak value corresponding to the window;
i, j-is the integer pixel coordinate value of the point corresponding to the peak value of the cross correlation coefficient;
and deltax and deltay are the difference between the actual cross-correlation coefficient peak value and the integer coordinate obtained after interpolation fitting.
8. The high-precision non-contact slope dangerous rock monitoring and early warning method as claimed in claim 1, wherein when the large-scale particle image velocimetry module is started, the large-scale particle image velocimetry module operates in a low-frequency monitoring mode.
9. The high-precision non-contact type slope dangerous rock monitoring and early warning method according to claim 8, characterized in that when the displacement change rate of the slope dangerous rock is increased and is greater than a first preset threshold and smaller than a second preset threshold, or the vibration dominant frequency of the slope dangerous rock is decreased and the decrease value reaches a first preset amplitude, an orange early warning is sent out; when the monitoring instrument sends out an orange early warning, the large-scale particle image speed measurement module starts a medium-frequency monitoring mode.
10. The high-precision non-contact type slope dangerous rock monitoring and early warning method according to any one of claims 8 or 9, characterized in that when the displacement change rate of the slope dangerous rock is increased and is greater than a second preset threshold, a red early warning is given; when the monitoring instrument sends out red early warning, the large-scale particle image speed measurement module starts a high-frequency monitoring mode.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102005104A (en) * | 2009-09-02 | 2011-04-06 | 吴立新 | Remote and rapid monitoring and alarming device and method for displacement and gesture of sliding mass |
| CN102721370A (en) * | 2012-06-18 | 2012-10-10 | 南昌航空大学 | Real-time mountain landslide monitoring method based on computer vision |
| CN107067333A (en) * | 2017-01-16 | 2017-08-18 | 长沙矿山研究院有限责任公司 | A kind of high altitudes and cold stability of the high and steep slope monitoring method |
| CN107220964A (en) * | 2017-05-03 | 2017-09-29 | 长安大学 | A kind of linear feature extraction is used for geology Taking stability appraisal procedure |
| WO2018016703A1 (en) * | 2016-07-18 | 2018-01-25 | 연세대학교 산학협력단 | Wireless sensor network measurement system and measurement method for monitoring, forecasting and alarming landslide |
| WO2018018661A1 (en) * | 2016-07-29 | 2018-02-01 | 深圳朝伟达科技有限公司 | Display method of interactive stability display system for rock slope |
| CN108288258A (en) * | 2018-04-23 | 2018-07-17 | 电子科技大学 | A kind of low-quality images Enhancement Method under severe weather conditions |
| CN108955541A (en) * | 2018-08-31 | 2018-12-07 | 广东工业大学 | A kind of slope monitoring apparatus |
| CN109655040A (en) * | 2018-12-25 | 2019-04-19 | 南京工业大学 | A kind of slope displacement monitoring method based on unmanned plane targeting technology |
| CN110146030A (en) * | 2019-06-21 | 2019-08-20 | 招商局重庆交通科研设计院有限公司 | Side slope surface DEFORMATION MONITORING SYSTEM and method based on gridiron pattern notation |
| CN110453731A (en) * | 2019-08-15 | 2019-11-15 | 中国水利水电科学研究院 | A kind of dam deformation of slope monitoring system and method |
| CN110516730A (en) * | 2019-08-20 | 2019-11-29 | 中铁工程装备集团有限公司 | The online stage division of quality of surrounding rock based on PSO-SVM algorithm and image recognition |
| CN110795832A (en) * | 2019-10-14 | 2020-02-14 | 武汉大学 | Landslide disaster multi-source data integrated comprehensive real-time monitoring method |
-
2021
- 2021-04-26 CN CN202110455107.5A patent/CN113324581B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102005104A (en) * | 2009-09-02 | 2011-04-06 | 吴立新 | Remote and rapid monitoring and alarming device and method for displacement and gesture of sliding mass |
| CN102721370A (en) * | 2012-06-18 | 2012-10-10 | 南昌航空大学 | Real-time mountain landslide monitoring method based on computer vision |
| WO2018016703A1 (en) * | 2016-07-18 | 2018-01-25 | 연세대학교 산학협력단 | Wireless sensor network measurement system and measurement method for monitoring, forecasting and alarming landslide |
| WO2018018661A1 (en) * | 2016-07-29 | 2018-02-01 | 深圳朝伟达科技有限公司 | Display method of interactive stability display system for rock slope |
| CN107067333A (en) * | 2017-01-16 | 2017-08-18 | 长沙矿山研究院有限责任公司 | A kind of high altitudes and cold stability of the high and steep slope monitoring method |
| CN107220964A (en) * | 2017-05-03 | 2017-09-29 | 长安大学 | A kind of linear feature extraction is used for geology Taking stability appraisal procedure |
| CN108288258A (en) * | 2018-04-23 | 2018-07-17 | 电子科技大学 | A kind of low-quality images Enhancement Method under severe weather conditions |
| CN108955541A (en) * | 2018-08-31 | 2018-12-07 | 广东工业大学 | A kind of slope monitoring apparatus |
| CN109655040A (en) * | 2018-12-25 | 2019-04-19 | 南京工业大学 | A kind of slope displacement monitoring method based on unmanned plane targeting technology |
| CN110146030A (en) * | 2019-06-21 | 2019-08-20 | 招商局重庆交通科研设计院有限公司 | Side slope surface DEFORMATION MONITORING SYSTEM and method based on gridiron pattern notation |
| CN110453731A (en) * | 2019-08-15 | 2019-11-15 | 中国水利水电科学研究院 | A kind of dam deformation of slope monitoring system and method |
| CN110516730A (en) * | 2019-08-20 | 2019-11-29 | 中铁工程装备集团有限公司 | The online stage division of quality of surrounding rock based on PSO-SVM algorithm and image recognition |
| CN110795832A (en) * | 2019-10-14 | 2020-02-14 | 武汉大学 | Landslide disaster multi-source data integrated comprehensive real-time monitoring method |
Non-Patent Citations (6)
| Title |
|---|
| 占正锋: "基于GPU的SIFT立体匹配算法研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
| 张丝苇: "基于大尺度粒子图像测速技术的山区河道流量测验研究", 《中国优秀博硕士学位论文全文数据库(硕士) 基础科学辑》 * |
| 李庆忠 等: "基于小波变换的低照度图像自适应增强算法", 《中国激光》 * |
| 林宝栋 等: "基于对数图像处理模型的低照度图像增强算法", 《南京邮电大学学报(自然科学版)》 * |
| 程琦: "无人机技术在水利工程高边坡危岩调查中的应用", 《水利科学与寒区工程》 * |
| 蒋亚杰: "视频监控系统图像清晰化研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
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