A visibility monitoring device and method suitable for automated deformation monitoring

Abstract

This invention provides a visibility monitoring method and apparatus suitable for automated deformation monitoring. A CNC pan-tilt unit, a high-resolution camera, a monocular telescope, and a control terminal are set up at the observation and shooting point. At each monitoring target point, a prism, an artificial light source, and a light sensor are set up. The visibility monitoring process includes placing the CNC pan-tilt unit and high-resolution camera next to the measuring robot station; acquiring daytime and nighttime images; statistically analyzing the grayscale histograms of the daytime images at each monitoring target point; calculating the center coordinates and radius of the circle formed by the artificial light source as a circular spot in the nighttime images at each monitoring target point; setting thresholds for the daytime grayscale histogram, and for the center coordinates and radius of the nighttime artificial light source based on the statistical information; acquiring daytime or nighttime images of the monitoring target points during actual deformation monitoring; determining whether the visibility meets the requirements; and verifying the results using other monitoring target points.

Description

Technical Field

[0001] This invention belongs to the fields of engineering measurement and digital image processing, and specifically relates to a visibility monitoring device and method suitable for automated deformation monitoring. Background Technology

[0002] In deformation monitoring projects for slopes, dams, and other structures, a surveying robot is typically deployed in the surrounding stable area, while several monitoring points are set up in the deformation area. The stability of the monitored area is assessed by statistically analyzing the multi-period automatic monitoring values ​​from these points. The measurement of monitoring point coordinates must be conducted under good visibility conditions. In rainy, snowy, or foggy weather, the electromagnetic wave signals emitted by the surveying robot are affected by meteorological conditions, causing deflections in their propagation path and leading to inaccurate measurement results, ultimately affecting the stability assessment of the monitored area. Therefore, a visibility monitoring device should also be installed in the area where the surveying robot is located. When the visibility monitoring value does not meet the measurement conditions, the surveying robot stops working and waits for the visibility monitoring value to return to normal before resuming monitoring.

[0003] Conventional visibility monitoring instruments determine the visually observable distance in the atmosphere by measuring the atmospheric extinction coefficient and applying empirical formulas. Specifically, visibility distance can be determined by calculating atmospheric transmittance and extinction coefficient, or by measuring the intensity of scattered light caused by gas molecules, aerosol particles, and fog droplets in a given volume of air. The main problems with conventional visibility monitoring methods and equipment are:

[0004] (1) Conventional visibility monitoring equipment requires the integration of multiple monitoring sensors, and the cost of the whole set of equipment is relatively high.

[0005] (2) Conventional visibility monitoring equipment has a relatively complex method for detecting visibility. It is necessary to use corresponding sensors and data processing algorithms to calculate the extinction coefficient of the horizontal air column or measure the scattering coefficient of light by a small volume of air to determine the meteorological optical range.

[0006] Therefore, conventional visibility monitoring equipment is mainly suitable for scenarios with high visibility requirements, such as airports, highways, and ports. However, in deformation monitoring projects such as slopes and dams, the target objects are relatively close and fixed, and it is not necessary to monitor the accurate meteorological optical range. It is only required that the measuring robot can aim at the target and perform measurements under the condition that the target image is clear. Summary of the Invention

[0007] This invention proposes a visibility monitoring device and method suitable for automated deformation monitoring to solve the problems existing in the background technology. The acquired visibility monitoring results are used to assist the measurement robot in determining whether to perform monitoring point coordinate measurement.

[0008] The technical solution of this invention provides a visibility monitoring method suitable for automated deformation monitoring, which involves setting up a digitally controlled pan-tilt unit, a high-resolution camera, a monocular telephoto lens, and a control terminal at the observation and shooting point.

[0009] The control terminal is connected to the CNC pan-tilt unit and the high-resolution camera, respectively.

[0010] The CNC gimbal is connected to the monocular telephoto lens;

[0011] The monocular telephoto lens is connected to the high-resolution camera.

[0012] The control terminal controls the high-resolution camera to acquire images and transmits the acquired images to the control terminal.

[0013] The control terminal controls the rotation of the CNC gimbal;

[0014] At each monitoring target point, prisms, artificial light sources, and light sensors are installed.

[0015] An artificial light source is connected to the light sensor; the light sensor is connected to a prism used to measure the coordinates of the target point observed by the robot.

[0016] The visibility monitoring process includes the following steps:

[0017] Step 1, instrument setup, including setting up a CNC pan-tilt head and a high-resolution camera next to the measuring robot station, and setting up a prism, artificial light source and light sensor at the selected visibility monitoring target point;

[0018] Step 2, sample image acquisition, including daytime image acquisition and nighttime image acquisition.

[0019] Daytime image acquisition includes, when the image is clear, rotating a high-resolution camera to the direction of each monitoring target point under the control of a CNC pan-tilt head to acquire target images, including those of the prism;

[0020] Nighttime data acquisition includes automatically controlling the artificial light source to start using a light sensor when the image is clear, and rotating the high-resolution camera to the direction of each monitoring target point under the control of a CNC pan-tilt head to acquire target images including the artificial light source.

[0021] When the CNC pan-tilt unit first controls the high-resolution camera to rotate and aim at each monitoring target point, the required rotation angle is preset, and the angle information is recorded after aiming at the target monitoring point. In subsequent monitoring processes, the rotation of the CNC pan-tilt unit is automatically controlled by the control terminal.

[0022] Step 3, monitoring information statistics, including the statistics of grayscale histograms of images collected during the day for each monitoring target point, and edge extraction of targets after imaging with circular light spots from artificial light sources in images collected at night for each monitoring target point, and further obtaining the center coordinates and radius of the circle through least squares fitting;

[0023] Step 4, setting visibility monitoring thresholds, including setting daytime grayscale histogram thresholds and nighttime artificial light source center coordinate thresholds and radius thresholds based on the statistical information from Step 3;

[0024] Step 5, visibility monitoring and data verification, includes collecting daytime or nighttime images of the monitoring target points during the actual deformation monitoring process, judging whether the visibility meets the requirements for clear imaging when the measurement robot observes based on the threshold set in Step 4, and verifying the judgment results. During the verification process, image information of other monitoring target points is collected, and the measurement robot is judged whether it can perform observation based on the threshold. If the image judgment results of each detection target point are consistent, it is confirmed that the measurement robot can perform target point observation.

[0025] Moreover, step 2 is implemented by selecting Q time periods under clear imaging conditions during the day and night according to the deformation monitoring time scheme of the measurement robot, and selecting S monitoring target points for image sample collection. M images are collected for each monitoring point target in each time period, where D, Q, S, and M are preset values.

[0026] Furthermore, the implementation steps for step 3 are as follows:

[0027] Step 3.1, grayscale processing, includes grayscale processing of all images acquired during the day and night, using the following formula:

[0028] f(u,v)=P1×R(u,v)+P2×G(u,v)+P3×B(u,v)

[0029] Where u and v represent the row and column coordinates of the pixel in the image, respectively; f(u,v) represents the gray value of the pixel (u,v) in the grayscale image; R(u,v) represents the R component of the pixel (u,v) in the original image; G(u,v) represents the G component of the pixel (u,v) in the original image; and B(u,v) represents the B component of the pixel (u,v) in the original image. P1, P2, and P3 represent weighting coefficients.

[0030] Step 3.2, Daytime Image Gray-Level Histogram Statistics, including calculating the gray-level value H corresponding to the maximum value of the gray-level histogram of all daytime images for each monitoring point. max ;

[0031] Step 3.3, Nighttime Image Spot Edge Detection and Parameter Calculation, includes detecting circular light spots in the image caused by artificial light sources at night. For each monitoring point's nighttime image after grayscale processing, Hough transform is used to detect the circular target edges. The extracted edge information is then used for circle fitting to calculate the center coordinates and radius of the circle in each nighttime image. The formula is as follows:

[0032] (u k -u O ) 2 +(v k -v O ) 2 =r 2

[0033] Further calculation of the center coordinates (u) using the least squares method O ,v O ), r is the radius of the sphere, u and v represent the row and column coordinates of the edge points of the circular target in the image, k∈[1,N], and N is the number of circular edge points in each night image, (u k ,v k () represents the coordinates of the k-th edge point;

[0034] Step 3.4, Center Coordinates and Radius Statistics, including grouping each monitoring target point and calculating the average center coordinates and average radius within each group, using the following formula:

[0035]

[0036] Where t is used to identify the t-th monitoring target point, and i is used to identify the i-th nighttime image of the current monitoring target point. Let x and y represent the average row and column coordinates of the center of the circle in the image of the t-th monitoring target point, respectively. Let represent the average radius of the circle in the image of the t-th monitoring target point. Let represent the summation of the row and column coordinates of the center of the circle in all images acquired for the t-th monitoring target point, respectively. This represents the summation of the radii of the circles in all images acquired for the t-th monitoring target point, where n is the sum of the radii of the circles. t This represents the total number of nighttime images acquired for the current t-th monitoring target point.

[0037] Furthermore, the implementation steps for step 4 are as follows:

[0038] Determine the daytime visibility monitoring threshold, including, for each monitoring target point, based on the daytime grayscale histogram results obtained in step 3.2, the grayscale value H. max and its corresponding highest frequency F(H) max) = MAX is the preferred threshold for daytime visibility monitoring. After performing grayscale histogram statistics on images acquired during subsequent monitoring, if the grayscale value H... max The corresponding frequency F(H) max If the visibility is at its highest, then the visibility meets the requirements for automatic monitoring by the measurement robot.

[0039] Determine the nighttime visibility monitoring threshold, including, for each monitoring target point, based on the calculation results and statistical results of the nighttime image center and radius from steps 3.3 and 3.4. As the preferred threshold for daytime visibility monitoring, the center of the circular light spot detected in the acquired image during subsequent monitoring is... And the radius is The visibility then meets the requirements for automatic monitoring by the measurement robot. 5. The visibility monitoring method for automated deformation monitoring according to claim 4, characterized in that: when determining the daytime visibility monitoring threshold in step 4, it is optimized to set H... max The corresponding frequency F(H) max ) is the highest or H max The corresponding frequency F(H) max H is the second highest, and H max The corresponding frequency F(H) max H is the second highest time. max The corresponding frequency is not lower than the highest frequency F. max p% is used as the threshold, and p takes a preset value.

[0040] Furthermore, in step 4, when determining the nighttime visibility monitoring threshold, it is optimized to... and As the preferred threshold, Δ represents the difference fluctuation value, u′ t v′ t 、r′ t These are the row and column coordinates of the actual obtained light spot center and the radius, respectively. When the following requirement is met, the visibility meets the automatic monitoring requirements of the measurement robot.

[0041]

[0042] Here, Δ takes a preset value.

[0043] Furthermore, the implementation steps for step 5 are as follows:

[0044] Step 5.1, Visibility monitoring information acquisition and preliminary judgment, including the actual deformation monitoring process of the measuring robot, firstly the visibility monitoring device determines whether the visibility meets the observation requirements, selects the current target point to be monitored by the measuring robot, uses the control terminal to rotate the CNC gimbal, aims the high-resolution camera at the monitoring target point, collects multiple images, performs image processing according to the processing method in step 3, and takes the average of the processing results as the final result;

[0045] If it is a daytime image, the processing result is compared with the preferred threshold for daytime visibility monitoring; if it is a nighttime image, the processing result is compared with the preferred threshold for nighttime visibility monitoring. If the threshold requirement is met, the visibility is determined to meet the observation requirements, and the measurement robot measures the monitoring target point.

[0046] Step 5.2, visibility monitoring information verification, includes making multiple judgments using other monitoring points after the initial judgment result. If the judgment results are consistent, the final judgment result is obtained.

[0047] On the other hand, the present invention provides a visibility monitoring device suitable for automated deformation monitoring, for implementing the visibility monitoring method for automated deformation monitoring as described above.

[0048] Moreover, it includes CNC gimbals, high-resolution cameras, monocular telescopes, prisms, artificial light sources, light sensors, and control terminals;

[0049] A digitally controlled gimbal, a high-resolution camera, a monocular telescope, and a control terminal were set up at the observation and shooting point.

[0050] The control terminal is connected to the CNC pan-tilt unit and the high-resolution camera, respectively.

[0051] The CNC gimbal is connected to the monocular telephoto lens;

[0052] The monocular telephoto lens is connected to the high-resolution camera.

[0053] The control terminal controls the high-resolution camera to acquire images and transmits the acquired images to the control terminal.

[0054] The control terminal controls the rotation of the CNC gimbal;

[0055] At each monitoring target point, prisms, artificial light sources, and light sensors are installed.

[0056] An artificial light source is connected to the light sensor; the light sensor is connected to a prism used to measure the coordinates of the target point observed by the robot.

[0057] Moreover, the prism is a circular prism.

[0058] This invention proposes a visibility monitoring device and method suitable for automated deformation monitoring. Its advantages include: solving the problem of erroneous monitoring results caused by unclear imaging during automated deformation monitoring using measuring robots; and improving existing visibility monitoring methods, making them more suitable for automated deformation monitoring projects. The technical solution of this invention is applicable to various visibility monitoring devices and methods composed of CNC pan-tilt units for photography equipment, high-resolution cameras, monocular telescopes, prisms, artificial light sources, light sensors, and control terminals, and is particularly suitable for visibility monitoring in automated deformation monitoring engineering projects. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the visibility monitoring device according to an embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of the automated deformation monitoring system in an embodiment of the present invention. Detailed Implementation

[0061] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0062] like Figure 1 As shown, the visibility monitoring device for automated deformation monitoring provided in this embodiment of the invention includes: a CNC pan-tilt unit, a high-resolution camera, a monocular telescope, a prism (preferably a circular prism, which has the highest accuracy when measuring coordinates), an artificial light source, a light sensor, and a control terminal.

[0063] A digitally controlled pan-tilt unit, a high-resolution camera, a monocular telephoto lens, and a control terminal are installed at the monitoring device (i.e., the observation and shooting point).

[0064] The control terminal is connected to the CNC pan-tilt unit and the high-resolution camera via wired connections.

[0065] The monocular telephoto lens is connected to the CNC gimbal via a fixing ring;

[0066] The CNC gimbal is mounted on the observation pier at the monitoring device;

[0067] The monocular telephoto lens is connected to the high-resolution camera.

[0068] The control terminal controls the rotation of the CNC gimbal;

[0069] The control terminal controls the high-resolution camera to acquire images and transmits the acquired images to the control terminal.

[0070] At each monitoring target point, prisms, artificial light sources, and light sensors are installed:

[0071] The artificial light source is connected to the light sensor;

[0072] The light sensor is connected to a circular prism used to measure the coordinates of the target point observed by the robot;

[0073] The circular prism is mounted on an observation pier at the monitoring target point;

[0074] The CNC gimbal model is PTS-301;

[0075] The high-resolution camera is recommended to have a CMOS sensor with a resolution of 30 megapixels or higher; the preferred embodiment is the Canon PowerShot SX720 HS.

[0076] The telephoto monocular is model CELESTRON 805;

[0077] The artificial light source is model YM1500W;

[0078] The light sensor is model ZYCN01;

[0079] The control terminal can be an existing device, with the preferred model being DELL XPS13;

[0080] In practice, the visibility monitoring device provided by this invention can be sold as a whole or partially utilize existing products.

[0081] The visibility monitoring method includes the following steps:

[0082] Step 1, instrument setup, includes placing a CNC pan-tilt head and high-resolution camera within 2 meters of the measuring robot station, and placing a prism, artificial light source, and light sensor at the selected visibility monitoring target location. For example... Figure 2 As shown, this is a schematic diagram of the monitoring site in an embodiment. A total of 3 monitoring target points were selected and marked as monitoring target point 1, monitoring target point 2 and monitoring target point 3 respectively.

[0083] Step 2, sample image acquisition, includes daytime and nighttime image acquisition. Daytime image acquisition requires a clear image and the high-resolution camera to be rotated by a CNC pan-tilt unit to the direction of each monitoring target point to acquire target images, including the prism. Nighttime data acquisition requires a clear image and the use of a light sensor to automatically activate an artificial light source. The high-resolution camera is then rotated by the CNC pan-tilt unit to the direction of each monitoring target point to acquire target images, including the artificial light source. When the CNC pan-tilt unit first controls the high-resolution camera to rotate and aim at each monitoring target point, it is recommended that the user input the corresponding angle parameters in the control terminal software. After aiming at the target monitoring point, the angle information is recorded. Subsequent monitoring processes will be automatically controlled by the control software on the control terminal.

[0084] Step 2 is implemented by selecting a total of D days, and according to the deformation monitoring time scheme of the measurement robot, selecting Q time periods under clear imaging conditions during the day and night, and selecting S monitoring target points for image sample collection. M images are collected for each monitoring point target in each time period.

[0085] Among them, D, Q, S, and M are preset values.

[0086] In this embodiment, according to the deformation monitoring time scheme of the measurement robot (monitoring at 3 o'clock, 9 o'clock and 16 o'clock every day), while ensuring clear imaging, D=7 days are selected, and Q=3 times are selected in both daytime and nighttime, which are the same as the deformation monitoring time scheme. S=3 monitoring target points are selected for image sample collection, and M=10 images are collected for each monitoring point target in each time period.

[0087] Step 3, monitoring information statistics, including the statistics of grayscale histograms of images collected during the day for each monitoring target point, and edge extraction of targets after imaging with circular light spots from artificial light sources in images collected at night for each monitoring target point, and further obtaining the center coordinates and radius of the circle through least squares fitting.

[0088] The steps to implement step 3 are as follows:

[0089] Step 3.1, grayscale processing.

[0090] All images acquired during the day and night are converted to grayscale using the following formula:

[0091] f(u,v)=P1×R(u,v)+P2×G(u,v)+P3×B(u,v)

[0092] In the embodiment, the weighting coefficients P1, P2, and P3 for the R, G, and B components are 0.3, 0.59, and 0.11, respectively.

[0093] f(u,v)=0.3×R(u,v)+0.59×G(u,v)+0.11×B(u,v)

[0094] Where u and v represent the row and column coordinates of the pixel in the image, respectively, f(u,v) represents the gray value of the pixel (u,v) in the grayscale image, R(u,v) represents the R component of the pixel (u,v) in the original image, G(u,v) represents the G component of the pixel (u,v) in the original image, and B(u,v) represents the B component of the pixel (u,v) in the original image.

[0095] Step 3.2, Daytime Image Gray-Level Histogram Statistics. Calculate the gray-level value H corresponding to the maximum value of the gray-level histogram of all daytime images for each monitoring point. max In the embodiment, H max =187.

[0096] Step 3.3, Nighttime Image Spot Edge Detection and Parameter Calculation. Artificial light sources typically produce circular spots in nighttime images. For each monitoring point's nighttime image after grayscale processing, Hough transform is used to detect the circular target edge. The extracted edge information is then used for circle fitting to calculate the center coordinates and radius of the circle in each nighttime image. The formula is as follows:

[0097] (u k -u O ) 2 +(v k -v O ) 2 =r 2

[0098] Further calculation of the center coordinates using the least squares method yields (u) O ,v O ), r is the radius of the sphere, u and v represent the row and column coordinates of the edge points of the circular target in the image, k∈[1,N], and N is the number of circular edge points in each night image, (u k ,v k ) represents the coordinates of the k-th edge point.

[0099] Step 3.4, Center Coordinates and Radius Statistics. Grouping each monitoring target point, calculate the average center coordinates and average radius for each group, using the following formula:

[0100]

[0101] Where t is used to identify the t-th monitoring target point, t∈[1,3], and i is used to identify the i-th nighttime image of the current monitoring target point, i∈[1,10]. Let x and y represent the average row and column coordinates of the center of the circle in the image of the t-th monitoring target point, respectively. Let represent the average radius of the circle in the image of the t-th monitoring target point. Let represent the summation of the row and column coordinates of the center of the circle in all images acquired for the t-th monitoring target point, respectively. This represents the summation of the radii of the circles in all images acquired for the t-th monitoring target point, where n is the sum of the radii of the circles. t This represents the total number of nighttime images acquired for the current t-th monitoring target point.

[0102] In this embodiment, after statistically analyzing the center coordinates and radii of the target images at the three monitoring points, the following results were obtained (unit: pixels):

[0103] First monitoring target: center coordinates (2479, 1876), radius 72;

[0104] The second monitoring target: center coordinates (2423, 1805), radius 66;

[0105] The third monitoring target: center coordinates (2504, 1917), radius 74.

[0106] Step 4, setting visibility monitoring thresholds, including setting daytime grayscale histogram thresholds and nighttime artificial light source center coordinate thresholds and radius thresholds based on the statistical information from Step 3.

[0107] The steps to implement step 4 are as follows:

[0108] Step 4.1: Determine the daytime visibility monitoring threshold. For each monitoring target point, based on the daytime grayscale histogram results obtained in Step 3.2, the grayscale value H... max and its corresponding frequency F(H) max The highest (denoted as F(H)) max The threshold for daytime visibility monitoring is H = MAX. After performing grayscale histogram analysis on the images acquired during subsequent monitoring, if the grayscale value H... max The corresponding frequency F(H) max If the visibility is at its highest, then the visibility meets the requirements for automatic monitoring by the measurement robot.

[0109] Considering that the histogram statistics of images acquired during the day may differ from those of the sample images due to changes in lighting, H can be further optimized. max The corresponding frequency F(H) max ) is the highest MAX or H max The corresponding frequency F(H) max The second highest (MAX) 2nd And H max The corresponding frequency F(H) max The second highest (MAX) 2nd H maxThe corresponding frequency is not lower than the highest frequency F. max The p% threshold is used as the preferred threshold, meaning that visibility meets the automatic monitoring requirements of the measurement robot when the following condition is met. In actual monitoring, the preferred threshold is used to determine whether visibility meets the monitoring requirements of the measurement robot.

[0110] And F(H) max )≥p%×F max

[0111] In practice, p can take a preset value. In this example, based on the statistical results of step 3.2, the grayscale value is set to H. max =187, the percentage is preferably set to p=80, the corresponding frequency F(187) is the highest or the second highest, and when the frequency F(187) is the second highest, it is not lower than the highest frequency F. max 80% of that, i.e., 0.8F max , set as the preferred threshold.

[0112] Step 4.2: Determine the nighttime visibility monitoring threshold. For each monitoring target point, based on the calculation results and statistical results of the nighttime image center and radius from Steps 3.3 and 3.4, This is the preferred threshold for daytime visibility monitoring. During subsequent monitoring, the center of the circular light spot detected in the acquired image is... And the radius is The visibility then meets the requirements for automatic monitoring by the measurement robot.

[0113] Considering that images acquired at night may differ from sample images, leading to discrepancies in the detection results of circular light spots, further steps can be taken to... and As the preferred threshold, Δ represents the difference fluctuation value. t v′ t 、r′ t These represent the row and column coordinates of the actual obtained light spot center and the radius, respectively. Visibility meets the automatic monitoring requirements of the measurement robot when the following formula is satisfied. During actual monitoring, an optimal threshold is used to determine whether the visibility meets the monitoring requirements of the measurement robot.

[0114]

[0115] In this embodiment, based on the calculation and statistical results of the center and radius of the nighttime image from steps 3.3 and 3.4, the difference fluctuation value Δ is set to 5. and Set as the preferred threshold.

[0116] Step 5, visibility monitoring and data verification, includes collecting daytime or nighttime images of the monitoring target points during the actual deformation monitoring process, judging whether the visibility meets the requirements for clear imaging when the measurement robot observes based on the threshold set in Step 4, and verifying the judgment results. The verification process requires collecting image information of other monitoring target points, and judging whether the measurement robot can conduct observation based on the threshold. If the image judgment results of each detection target point are consistent, it is confirmed that the measurement robot can conduct target point observation.

[0117] The steps to implement step 5 are as follows:

[0118] Step 5.1: Visibility Monitoring Information Acquisition and Preliminary Judgment. During the actual deformation monitoring process of the measurement robot, the visibility monitoring device first determines whether the visibility meets the observation requirements (i.e., clear imaging). The current target point to be monitored by the measurement robot (target point 1) is selected, and the CNC gimbal is rotated using the control software on the control terminal. The high-resolution camera is aimed at the target point, and 10 images are acquired. Image processing is performed according to the method described in Step 3, and the average of the processing results is the final result. If it is a daytime image, the processing result is compared with the preferred threshold for daytime visibility monitoring; if it is a nighttime image, the processing result is compared with the preferred threshold for nighttime visibility monitoring. If the threshold requirements are met, the visibility is determined to meet the observation requirements, and the measurement robot can then measure the target point.

[0119] In this embodiment, the monitoring time is during the day, the monitoring target point is No. 1, and F(187) = MAX, which meets the monitoring requirements of the measurement robot.

[0120] Step 5.2, Visibility Monitoring Information Verification. To avoid errors in visibility monitoring information statistics caused by image acquisition results from non-monitoring target points due to obstructions or other reasons in the current image acquisition direction, after the initial judgment result, it is necessary to perform L more judgments using other monitoring points. If the judgment results are consistent, the final judgment result is obtained.

[0121] In this embodiment, the number of verifications is set to L=2, that is, the visibility monitoring results of monitoring target point 2 and monitoring target point 3 are selected as verifications. For monitoring target point 2, F(187)=MAX; for monitoring target point 3, F(187)=MAX; the verification results are consistent with the results of monitoring target point 1, therefore it can be determined that the visibility conditions meet the monitoring requirements of the measurement robot.

[0122] In practice, computer software technology can be used to automate the process and obtain accurate judgment results.

[0123] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A visibility monitoring method suitable for automated deformation monitoring, characterized in that: A digitally controlled gimbal, a high-resolution camera, a monocular telescope, and a control terminal were set up at the observation and shooting point. The control terminal is connected to the CNC pan-tilt unit and the high-resolution camera, respectively. The CNC gimbal is connected to the monocular telephoto lens; The monocular telephoto lens is connected to the high-resolution camera. The control terminal controls the high-resolution camera to acquire images and transmits the acquired images to the control terminal. The control terminal controls the rotation of the CNC gimbal; At each monitoring target point, prisms, artificial light sources, and light sensors are installed. An artificial light source is connected to the light sensor; the light sensor is connected to a prism used to measure the coordinates of the target point observed by the robot. The visibility monitoring process includes the following steps: Step 1, instrument setup, including setting up a CNC pan-tilt head and a high-resolution camera next to the measuring robot station, and setting up a prism, artificial light source and light sensor at the selected visibility monitoring target point; Step 2, sample image acquisition, including daytime image acquisition and nighttime image acquisition. Daytime image acquisition includes, when the image is clear, rotating a high-resolution camera to the direction of each monitoring target point under the control of a CNC pan-tilt head to acquire target images, including those of the prism; Nighttime data acquisition includes automatically controlling the artificial light source to start using a light sensor when the image is clear, and rotating the high-resolution camera to the direction of each monitoring target point under the control of a CNC pan-tilt head to acquire target images including the artificial light source. When the CNC pan-tilt unit first controls the high-resolution camera to rotate and aim at each monitoring target point, the required rotation angle is preset, and the angle information is recorded after aiming at the target monitoring point. In subsequent monitoring processes, the rotation of the CNC pan-tilt unit is automatically controlled by the control terminal. Step 3, monitoring information statistics, including the statistics of grayscale histograms of images collected during the day for each monitoring target point, and edge extraction of targets after imaging with circular light spots from artificial light sources in images collected at night for each monitoring target point, and further obtaining the center coordinates and radius of the circle through least squares fitting; Step 4, setting visibility monitoring thresholds, including setting daytime grayscale histogram thresholds and nighttime artificial light source center coordinate thresholds and radius thresholds based on the statistical information from Step 3; Step 5, visibility monitoring and data verification, includes collecting daytime or nighttime images of the monitoring target points during the actual deformation monitoring process, judging whether the visibility meets the requirements for clear imaging when the measurement robot observes based on the threshold set in Step 4, and verifying the judgment results. During the verification process, image information of other monitoring target points is collected, and the measurement robot is judged whether it can perform observation based on the threshold. If the image judgment results of each detection target point are consistent, it is confirmed that the measurement robot can perform target point observation.

2. The visibility monitoring method for automated deformation monitoring according to claim 1, characterized in that: The implementation of step 2 is that a total of D day and the measured robot deformation monitoring time scheme selects Q time periods and selects S monitoring target points for image sample collection, and each monitoring point target collects M images in each time period, wherein D , Q , S , M is a preset value.

3. The visibility monitoring method for automated deformation monitoring as claimed in claim 1, wherein: The steps to implement step 3 are as follows: Step 3.1, grayscale processing, includes grayscale processing of all images acquired during the day and night, using the following formula: f ( u , v ) = P 1 × R ( u , v ) + P 2 × G ( u , v ) + P 3 × B ( u , v ) in, u , v These represent the row and column coordinates of the image pixels, respectively. f ( u , v ) represents a grayscale image pixel ( u , v The grayscale value of ) R ( u , v ) represents the original image pixel ( u , v )of R Quantity, G ( u , v ) represents the original image pixel ( u , v )of G Quantity, B ( u , v ) represents the original image pixel ( u , v )of B Quantity; P 1. P 2. P 3 represents the weighting coefficient; Step 3.2, Daytime Image Gray-Level Histogram Statistics, including calculating the gray-level value corresponding to the maximum value of the gray-level histogram of all daytime images for each monitoring point. H max ; Step 3.3, Nighttime Image Spot Edge Detection and Parameter Calculation, includes detecting circular light spots in the image caused by artificial light sources at night. For each monitoring point's nighttime image after grayscale processing, Hough transform is used to detect the circular target edges. The extracted edge information is then used for circle fitting to calculate the center coordinates and radius of the circle in each nighttime image. The formula is as follows: The coordinates of the circle center are further calculated using the least squares method. , r Let be the radius of the sphere. u , v These represent the row and column coordinates of the image at the edge of the circular target, respectively. k ∈ [1, N ], N The number of circular edge points on each nighttime image. For the first k The coordinates of the edge points; Step 3.4, Center Coordinates and Radius Statistics, including grouping each monitoring target point and calculating the average center coordinates and average radius within each group, using the following formula: in, t Used to identify the current number. t One monitoring target point, i The first one used to identify the current monitoring target point i Zhang Nighttime Images, , They represent the first t The average row and column coordinates of the center of the circle in the image of each monitoring target point. Indicates the first t The average radius of the circle in the image of each monitoring target point. , They represent the first, second, and third parts respectively. t Sum the row and column coordinates of the center of the circle in all images acquired from each monitoring target point. Indicates the first t Sum the radii of the circles in all images acquired from each monitoring target point. Indicates the current number t The total number of nighttime images acquired from each monitoring target point.

4. The visibility monitoring method for automated deformation monitoring according to claim 3, characterized in that: The steps to implement step 4 are as follows: Determine the daytime visibility monitoring threshold, including, for each monitoring target point, the grayscale value based on the daytime grayscale histogram results obtained in step 3.

2. H max and its corresponding highest frequency F ( H max ) = MAX is the preferred threshold for daytime visibility monitoring. After performing grayscale histogram statistics on the images acquired during subsequent monitoring, if the grayscale values... H max Corresponding frequency F ( H max If the visibility is at its highest, then the visibility meets the requirements for automatic monitoring by the measurement robot. Determine the nighttime visibility monitoring threshold, including, for each monitoring target point, based on the calculation results and statistical results of the nighttime image center and radius from steps 3.3 and 3.

4. , , As the preferred threshold for daytime visibility monitoring, the center of the circular light spot detected in the acquired image during subsequent monitoring is... And the radius is If the visibility meets the requirements of the measurement robot's automatic monitoring, then the visibility will be within acceptable limits.

5. The visibility monitoring method for automated deformation monitoring according to claim 4, characterized in that: In step 4, when determining the daytime visibility monitoring threshold, the optimization is to... H max Corresponding frequency F ( H max ) is the highest or H max Corresponding frequency F ( H max ) is the second highest, and H max Corresponding frequency F ( H max (Second highest time) H max The corresponding frequency is no less than the highest frequency. F max of p % as a threshold p Use preset values.

6. The visibility monitoring method for automated deformation monitoring according to claim 4, characterized in that: In step 4, when determining the nighttime visibility monitoring threshold, the optimization is to... , ,and As the preferred threshold, Δ represents the difference fluctuation value. , , These are the row and column coordinates of the actual obtained light spot center and the radius, respectively. When the following requirement is met, the visibility meets the automatic monitoring requirements of the measurement robot. Here, Δ takes a preset value.

7. The visibility monitoring method for automated deformation monitoring according to claim 1, 2, 3, 4, 5, or 6, characterized in that: The steps to implement step 5 are as follows: Step 5.1, Visibility monitoring information acquisition and preliminary judgment, including the actual deformation monitoring process of the measuring robot, firstly the visibility monitoring device determines whether the visibility meets the observation requirements, selects the current target point to be monitored by the measuring robot, uses the control terminal to rotate the CNC gimbal, aims the high-resolution camera at the monitoring target point, collects multiple images, performs image processing according to the processing method in step 3, and takes the average of the processing results as the final result; If it is a daytime image, the processing result is compared with the preferred threshold for daytime visibility monitoring; if it is a nighttime image, the processing result is compared with the preferred threshold for nighttime visibility monitoring. If the threshold requirement is met, the visibility is determined to meet the observation requirements, and the measurement robot measures the monitoring target point. Step 5.2, visibility monitoring information verification, includes making multiple judgments using other monitoring points after the initial judgment result. If the judgment results are consistent, the final judgment result is obtained.

8. A visibility monitoring device suitable for automated deformation monitoring, characterized in that: This is used to implement a visibility monitoring method suitable for automated deformation monitoring as described in any one of claims 1-6.

9. The visibility monitoring device for automated deformation monitoring according to claim 8, characterized in that: Includes a CNC pan-tilt head, a high-resolution camera, a monocular telephoto lens, a prism, an artificial light source, a light sensor, and a control terminal; A digitally controlled gimbal, a high-resolution camera, a monocular telescope, and a control terminal were set up at the observation and shooting point. The control terminal is connected to the CNC pan-tilt unit and the high-resolution camera, respectively. The CNC gimbal is connected to the monocular telephoto lens; The monocular telephoto lens is connected to the high-resolution camera. The control terminal controls the high-resolution camera to acquire images and transmits the acquired images to the control terminal. The control terminal controls the rotation of the CNC gimbal; At each monitoring target point, prisms, artificial light sources, and light sensors are installed. An artificial light source is connected to the light sensor; the light sensor is connected to a prism used to measure the coordinates of the target point observed by the robot.

10. The visibility monitoring device for automated deformation monitoring according to claim 9, characterized in that: The prism is a circular prism.

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