Road slope deformation monitoring method and system

Abstract

The invention provides a road slope deformation monitoring method and system, and the method comprises the following steps: calculating an image displacement value of each grid based on a monitoring image, obtained in real time, of a road slope deformation region; for the grid marked with the center of the infrared light source, calculating an image displacement value of the grid based on image coordinates of the center of the corresponding infrared light source in the continuous monitoring image; for the grid which is not marked with the center of the infrared light source but marked with the natural ground feature points, when the average brightness of the monitoring image is greater than a preset threshold value, calculating an image displacement value based on the image coordinates of the natural ground feature points in the continuous monitoring image; when the average brightness of the monitored image is smaller than or equal to a preset threshold value, assigning the image displacement of the grid as the average value of the displacement values of the neighborhood grid; and based on the image displacement value of each grid, calculating the displacement of the corresponding slope area. According to the invention, the road slope deformation range can be extracted in a high-precision, low-cost and all-weather manner.

Description

Technical Field

[0001] The invention belongs to the technical field of highway slope deformation monitoring, and in particular relates to a highway slope deformation monitoring method and system. Background Art

[0002] Highway slope landslide disasters are a major threat to highway traffic safety. In recent years, roadbed collapse and bridge collapse accidents caused by highway landslide geological disasters have occurred frequently, causing a large number of casualties and huge property losses. In order to effectively prevent and respond to landslide disasters, highway slope deformation monitoring and early warning technology is particularly important.

[0003] Highway slope deformation monitoring technology is a research focus in the field of highway engineering. Its main goal is to timely warn of slope landslide risks, assess the scale and destructiveness of landslides, provide data support for slope maintenance, reinforcement and disaster warning, and ensure the safety and smoothness of highway traffic by monitoring the time series deformation and sliding scale of the slope. With the development of surveying and mapping remote sensing technology, highway slope deformation monitoring technology has developed rapidly and formed a variety of technical systems, mainly including the following common monitoring methods:

[0004] Ground measurement technology: The horizontal and vertical displacement changes of slope monitoring points are measured through equipment such as levels and total stations, which has high accuracy and strong adaptability. However, ground measurement technology is greatly restricted by terrain and visibility conditions, and the frequency of manual observation is low, making it impossible to achieve all-weather, real-time monitoring.

[0005] Satellite navigation positioning technology: By receiving satellite signals, the three-dimensional coordinates of the monitoring points are obtained in real time, thereby calculating the displacement changes of the slope. This technology has the advantages of all-weather, real-time, and high-precision. It can realize remote automatic monitoring, is not restricted by line-of-sight conditions, and is suitable for large-scale monitoring. However, in areas with severe signal obstruction such as mountainous areas, the accuracy is affected and the equipment cost is high.

[0006] Satellite remote sensing monitoring technology: Using synthetic aperture radar interferometry (InSAR) technology, radar images acquired at different times are interferometrically processed to obtain information on small deformations on the slope surface. This technology can monitor large-scale slope deformations with millimeter-level accuracy, and is not limited by weather and lighting conditions. However, data processing is complex, and the accuracy is low in areas with complex terrain and dense vegetation, and the cost of purchasing data is high.

[0007] Terrestrial laser scanning technology: The laser scanner is used to perform high-precision three-dimensional scanning of the slope to obtain point cloud data, and then calculate the deformation and range of the slope. This technology can quickly obtain three-dimensional information of the slope, with high measurement accuracy, and is suitable for comprehensive reflection of deformation conditions. However, the equipment cost is high, the data processing volume is large, the scanning range is limited, and the adaptability to complex terrain is poor.

[0008] Drone monitoring technology: Drones are equipped with optical cameras, lidar and other sensors to quickly obtain high-resolution images and three-dimensional point cloud data for slope monitoring. Drone monitoring is highly flexible, low-cost and can respond quickly to monitoring needs. However, due to the limitations of weather, flight distance and flight time, data processing also requires certain technical support.

[0009] Although the above technologies have made some progress in slope deformation monitoring, they still face the following challenges:

[0010] The challenge of all-weather monitoring: Except for satellite navigation and positioning technology that can achieve all-weather monitoring, other technologies are generally limited by environmental conditions such as lighting and weather, and cannot work stably at night or in low light conditions.

[0011] High monitoring costs: For example, satellite navigation positioning technology and ground laser scanning technology require expensive equipment, and satellite remote sensing technology faces high data purchase costs, which makes the monitoring cost high and difficult to be widely used.

[0012] Data processing complexity: The data processing complexity of most technologies is relatively high, especially remote sensing technology and laser scanning technology, which require a lot of post-data processing, increasing the difficulty of operation and maintenance.

[0013] Limitations of monitoring objects: Except for satellite remote sensing technology, most methods can only monitor a small number of discrete points, making it difficult to extract and locate slope deformation areas, resulting in difficulties in assessing the scope and hazard of landslides.

[0014] Therefore, it is difficult for existing technologies to simultaneously meet the requirements of low cost, all-weather, and convenient data processing, especially for slope deformation monitoring at night or in complex environments. In order to overcome these limitations, a high-precision, low-cost, all-weather highway slope deformation monitoring method that can extract deformation range is urgently needed. Summary of the invention

[0015] The purpose of the present invention is to solve the deficiencies of the above-mentioned background technology and to provide a high-precision, low-cost, all-weather, highway slope deformation monitoring method and system that can extract deformation range.

[0016] The technical solution adopted by the present invention is: a method for monitoring deformation of a highway slope, wherein an infrared light source is provided in the deformation zone of the highway slope; and the method comprises the following steps:

[0017] Grid the initial image of the highway slope deformation area, extract the natural feature points and the infrared light source center, and mark the corresponding grid and image coordinates;

[0018] Based on the real-time monitoring images of the highway slope deformation zone, the image displacement value of each grid is calculated;

[0019] For a grid marked with the center of an infrared light source, the image displacement value of the grid is calculated based on the image coordinates of the corresponding center of the infrared light source in the continuous monitoring images;

[0020] For the grids without marking the infrared light source center but with marking the natural feature points:

[0021] When the average brightness of the monitoring image is greater than a preset threshold, the image displacement value is calculated based on the image coordinates of the natural object feature points in the continuous monitoring image;

[0022] When the average brightness of the monitored image is less than or equal to the preset threshold, the image displacement of the grid is assigned the average value of the displacement values ​​of the neighboring grids;

[0023] For a grid that has neither infrared light source monitoring points nor natural feature points, the image displacement of the grid is assigned the average value of the displacement values ​​of the neighboring grids;

[0024] Based on the image displacement value of each grid, the displacement of the corresponding slope area is calculated.

[0025] In the above technical solution, a number of monitoring piles are buried from top to bottom along the deformation zone of the highway slope; an infrared light source is set on each monitoring pile; a reference pile is buried in a stable area outside the deformation zone of the highway slope, and a monitoring camera is installed on the reference pile to obtain an initial image and a monitoring image.

[0026] In the above technical solution, the process of calculating the image displacement value of the grid marked with the center of the infrared light source includes: for each grid marked with the center of the infrared light source, calculating the absolute difference between the vertical coordinates of the images of the center of the infrared light source in two consecutive monitoring images, as the image displacement value of the grid at the corresponding monitoring moment; for all monitoring moments within a specified time period, calculating the cumulative sum of all image displacement values ​​of the grid within the time period, as the image displacement value of the grid within the specified time period.

[0027] In the above technical solution, the process of calculating the image displacement value based on the image coordinates of the natural object feature points in the continuous monitoring images includes: for any grid marked with the natural object feature point, calculating the average value of the difference between the image vertical coordinates of all the natural object feature points in the grid in two consecutive monitoring images, as the image displacement value of the grid at the corresponding monitoring moment; for all monitoring moments within a specific time period, calculating the cumulative sum of all the image displacement values ​​of the grid within the time period, as the image displacement value of the grid within the specific time period; the specific time period is the time period from the specified monitoring moment to the monitoring moment corresponding to the previous monitoring image corresponding to the moment with a brightness greater than a preset threshold.

[0028] In the above technical solution, the process of obtaining the natural object feature points of each monitoring image within a specific time period except for the specified monitoring moment includes: constructing a search space based on the average pixel displacement of the monitoring images within the specific time period and combining the natural object feature points matched at the specified monitoring moment; matching the natural object feature points of each monitoring image based on the search space.

[0029] In the above technical solution, for the monitoring image p at a specified time i Natural feature points pt i_q_k The search space is defined as follows:

[0030] Area i_r_q_k

[0031] ={x i_q_k -1 <x<x i_q_k +1,y i_q_k -2*Avg_shift i_r <y<y i_q_k +1}

[0032] Among them, natural feature points pt i_q_k The image coordinates are (x i_q_k ,y i_q_k ), Avg_shift i_r Represents the average pixel displacement of the monitored image within a specific time period.

[0033] In the above technical solution, for all monitoring moments within a specific time period, the cumulative sum of the average pixel displacements of the monitoring images within the time period is calculated as the average pixel displacement of the monitoring images within the time period; for any monitoring image, the average value of the absolute difference between the vertical coordinates of the centers of all infrared light sources in the previous monitoring image is calculated as the average pixel displacement of the monitoring image.

[0034] In the above technical solution, there is at least one grid marked with the center of the infrared light source in the neighborhood of each grid.

[0035] The above technical solution also includes the following steps: connecting the grids whose image displacement values ​​are greater than a set threshold within a specific time period to form a closed area, marking the slope area corresponding to the closed area as the slope landslide range, and calculating the area of ​​the slope area.

[0036] The present invention also provides a highway slope deformation monitoring system, which is used to implement the highway slope deformation monitoring method described in the above technical solution.

[0037] The beneficial effects of the present invention are as follows: the present invention can accurately calibrate the monitoring area by dividing the deformation area of ​​the highway slope into grids and extracting the natural feature points and the center of the infrared light source. In this way, monitoring can be performed grid by grid in the deformation area, so that the deformation data of each grid can be accurately calculated, thereby improving the monitoring accuracy. The present invention can select different processing methods for images under different conditions (such as brightness greater than or less than a preset threshold), ensuring that effective monitoring can continue even in insufficient light conditions (such as at night), overcoming the limitations of traditional methods under low light conditions. By combining infrared light sources with natural feature points, the present invention can simultaneously monitor the slight deformation and large displacement of the slope, and reflect the overall deformation trend of the slope in real time.

[0038] Furthermore, the present invention can ensure the stability and accuracy of the monitoring system by burying multiple monitoring piles in the slope deformation area and installing reference piles in the stable area. The reference pile provides a fixed reference point, so that each acquisition of the monitoring image can be compared with the initial image to ensure the accuracy of the data; setting up multiple monitoring piles and reference piles to ensure full coverage of the deformation area and the stable area can not only detect the deformation area, but also monitor the situation in the stable area in real time, thereby improving the comprehensiveness of the entire highway slope deformation monitoring.

[0039] Furthermore, the present invention can accurately obtain the displacement value of each grid by calculating the image ordinate difference of the center of the infrared light source. By comparing the continuous monitoring images, the displacement trajectory and range of the deformation area can be clearly depicted; the cumulative sum of the image displacement values ​​in a specific time period can reflect the overall deformation trend in the entire time period, thereby providing early warning of disasters such as slope landslides.

[0040] Furthermore, the present invention uses natural feature points to enhance monitoring accuracy: the displacement value is calculated based on the image coordinates of the natural feature points in the continuous monitoring image, which can provide more feature point data, making the displacement calculation more accurate and comprehensive. This method combines the comparison between images to improve the ability to identify deformed areas; by adjusting the displacement calculation according to the brightness of the monitoring image, when the lighting conditions are insufficient, the mean of the neighborhood displacement value can be used to fill in, ensuring that the monitoring data will not be lost due to insufficient lighting.

[0041] Furthermore, the present invention can improve the matching accuracy of image feature points by calculating the average pixel displacement within a specific time period and combining the natural feature points at the specified monitoring time to construct a search space. This ensures that the feature point matching between monitoring images is more accurate and reduces errors caused by image distortion or camera angle changes; in each monitoring image, feature point matching based on the search space can reduce unnecessary calculations, thereby improving the real-time performance and response speed of the monitoring system.

[0042] Furthermore, the present invention can effectively limit the search range and improve the accuracy and efficiency of feature point matching by defining the search space of natural feature points in the monitoring image at a specific time. It avoids the waste of computing resources caused by too large a search space; through a more precise search space definition, the displacement of natural feature points can be matched more accurately, which helps to monitor the slight deformation of the slope more finely.

[0043] Furthermore, the present invention can dynamically adjust the calculation results of image displacement by calculating the cumulative sum of the average pixel displacements of all monitoring images within a specific time period, thereby making the monitoring results within the entire time period more accurate; for each monitoring image, its average pixel displacement is calculated by the difference in the vertical coordinate of the infrared light source center of the previous image, which helps to detect deformation in time, avoid delayed feedback, and provide early landslide warning.

[0044] Furthermore, the present invention can ensure the stability of the monitoring system and avoid monitoring blind spots caused by lack of data source in a certain grid by ensuring that there is at least one grid marked with the center of the infrared light source in the neighborhood of each grid; ensuring that each grid has a corresponding monitoring data source is conducive to providing more comprehensive and accurate slope deformation monitoring.

[0045] Furthermore, the present invention can accurately identify the scope of slope landslides by calculating the grids whose image displacement values ​​are greater than a set threshold and connecting these grids to form a closed area. This method provides data support for landslide disaster assessment, can effectively delineate the impact range of landslides and carry out subsequent processing; by calculating the area of ​​the landslide area, it can provide a reference for the impact assessment of landslide disasters, and then provide data support for disaster warning and rescue work. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments to facilitate a clear understanding of the present invention, but they do not constitute a limitation on the present invention.

[0048] Example 1

[0049] like Figure 1 As shown, the present invention provides a method for monitoring deformation of a highway slope, wherein an infrared light source is provided in the deformation zone of the highway slope; the method comprises the following steps:

[0050] Grid the initial image of the highway slope deformation area, extract the natural feature points and the infrared light source center, and mark the corresponding grid and image coordinates;

[0051] Based on the real-time monitoring images of the highway slope deformation zone, the image displacement value of each grid is calculated;

[0052] For a grid marked with the center of an infrared light source, the image displacement value of the grid is calculated based on the image coordinates of the corresponding center of the infrared light source in the continuous monitoring images;

[0053] For the grids without marking the infrared light source center but with marking the natural feature points:

[0054] When the average brightness of the monitoring image is greater than a preset threshold, the image displacement value is calculated based on the image coordinates of the natural object feature points in the continuous monitoring image;

[0055] When the average brightness of the monitored image is less than or equal to the preset threshold, the image displacement of the grid is assigned the average value of the displacement values ​​of the neighboring grids;

[0056] For a grid that has neither infrared light source monitoring points nor natural feature points, the image displacement of the grid is assigned the average value of the displacement values ​​of the neighboring grids;

[0057] Based on the image displacement value of each grid, the displacement of the corresponding slope area is calculated.

[0058] The principle of the present invention is further explained below with reference to specific embodiments.

[0059] This embodiment specifically includes the following steps:

[0060] Step 1: Layout of monitoring cameras and infrared light source monitoring points, and division of the initial image into grids.

[0061] Bury monitoring piles from top to bottom along the monitoring slope, and the infrared light source is firmly installed on the monitoring piles. The number of monitoring piles for the same monitoring slope is no less than 2, and the number of monitoring piles is recorded as a. Bury reference piles in the stable area outside the slope deformation zone, and install monitoring cameras on the reference piles. The focal length of the monitoring camera is f (in meters), and the pixel size of the monitoring camera is μ (in meters). Adjust the shooting angle of the monitoring camera to ensure that the monitoring camera is facing the infrared light source. Measure the distance D = d from the monitoring camera to the infrared light source. 1 d 2 …d a}(unit: meter), where a≥2.

[0062] Before starting the monitoring process, the initial image of the highway slope deformation area is obtained through the monitoring camera, and the initial image of the highway slope deformation area is gridded, and the natural feature points and the center of the infrared light source are extracted, and the corresponding grids and image coordinates are marked. It should be noted that after starting the monitoring process, the current highway slope deformation area is obtained as a monitoring image at each monitoring moment, and the monitoring image corresponds to the monitoring moment one by one.

[0063] For the initial image p 0 Divide the grid into grids, the length and width of the grid are both 128 pixels, and the grid is numbered G 0_q The slope area corresponding to a single grid is n=1,2,...,a.

[0064] If the monitoring image p i The average brightness value L i If ≤40, the image is taken at night or in low light conditions, the image is dark, and no natural feature point extraction is performed.

[0065] If the monitoring image p i The average brightness value L i >40, the initial image is taken under sufficient lighting conditions, and its average brightness is greater than 40. 0 Each grid uses SIFT algorithm to extract image feature points and obtain the natural feature point set PT 0_q ={pt 0_q_1 , pt 0_q_2 ,…,pt 0_q_k}. Record the natural feature point pt 0_q_k The image coordinates are (x 0_q_k ,y 0_q_k ).

[0066] The initial value of the slope displacement value corresponding to each grid in the initial image is assigned to 0.

[0067] Step 2: Time-series monitoring image acquisition and lighting condition determination

[0068] The monitoring camera automatically captures the time-series monitoring images of the slope at a fixed time interval, and the time interval can be set to 15 minutes, 30 minutes or 1 hour. The time-series monitoring image dataset is P = {p 1 p 2 …p i}, i is the monitoring image number, p i Indicates the most recently captured surveillance image.

[0069] Each time a monitoring image is taken, the average brightness value of the monitoring image is calculated as Among them, s is the total number of pixels of the monitoring image, g j To monitor the gray value of each pixel in the image.

[0070] If L i ≤40, then the current image p i The shooting time is at night or under insufficient light conditions;

[0071] If Li >40, then determine the current image p i The photos were taken under sufficient lighting conditions.

[0072] Step 3: Obtaining the time series displacement value of the infrared light source monitoring point

[0073] The image coordinates of the center of the infrared light source in the monitoring image are extracted by fitting the center of the ellipse. Each time a monitoring image is taken, the monitoring image p is recorded. i The image coordinates of the center of the nth infrared light source are marked as O i_n =(x i_n ,y i_n )(unit is pixel), where n = (1, 2, …a).

[0074] The monitoring image p i Relative to the previous monitoring image p i-1 , the pixel displacement of the infrared light source center in the monitoring image is expressed as shift i_i-1_n =|y i_n -y i-1_n |.

[0075] For a specified time period (i.e., capturing monitoring images p r To capture monitoring imagesp i The cumulative sum of all image displacement values ​​of the grid in the time period is calculated as the image displacement value of the grid in the specified time period. Based on the image displacement value of each grid, the displacement of the corresponding slope area is calculated.

[0076] Preferably, when taking the monitoring image p i-1 To capture monitoring imagesp i During the time period between the monitoring points where the infrared light source is located, the deformation of the slope area corresponding to the grid is

[0077] The monitoring image p i Relative to any previous monitoring image p r The cumulative deformation of the monitoring point where the infrared light source is located r≤j≤i, 1≤r≤i-1.

[0078] Based on the above calculation method, the cumulative deformation at the monitoring point where each infrared light source is located can be obtained.

[0079] Step 4: Image grid division and natural feature point extraction

[0080] For monitoring image p i Divide the grid into grids, the length and width of the grid are both 128 pixels, and the grid is numbered G i_q The slope area corresponding to a single grid is

[0081] If the monitoring image p i The average brightness value L i If ≤40, the image is taken at night or in low light conditions, the image is dark, and no natural feature point extraction is performed.

[0082] If the monitoring image p i The average brightness value L i >40, the image is taken under sufficient lighting conditions. i Each grid uses SIFT algorithm to extract image feature points and obtain the natural feature point set PT i_q =

[0083] pt i_q_1 , pt i_q_2 ,…,pt i_q_k}. Record the natural feature point pt i_q_k The image coordinates are (x i_q_k ,y i_q_k ).

[0084] Step 5: Matching of natural feature points in time-series monitoring images and obtaining time-series displacement values

[0085] Pick

[0086] If the monitoring image p i The average brightness value L i >40, select monitoring image p i The previous monitoring image with an average brightness value greater than 40 is denoted as p r .

[0087] Matching monitoring image p i and p r In order to speed up the matching speed and narrow the range of candidate matching points, according to the monitoring image p i Relative to the monitoring image p r The average pixel shift is Avg_shift i_r Build the search space.

[0088] The monitoring image p is calculated by using the pixel displacement of a infrared light source set up on the slope. i

[0089] Relative to the previous monitoring image p i-1 The average pixel displacement is

[0090] Calculate the captured monitoring image p r To capture monitoring imagespi The cumulative sum of the average pixel displacement of the monitored image during the time period is taken as the average pixel displacement Avg_shift of the monitored image during the time period. i_r .

[0091] For the monitoring image p i Natural feature points pt i_q_k , which is in the monitoring image p r

[0092] The search space of candidate matching points on is defined as:

[0093] Area i_r_q_k

[0094] ={x i_q_k -1 <x<x i_q_k +1,y i_q_k -2*Avg_shift i_r <y<y i_q_k +1

[0095] For the monitoring image p i Natural feature points pt i_q_k , select monitoring image p i-1 Located in the search space Area i_r_q_k The SIFT feature matching algorithm is used to match the natural feature points within the space.

[0096] Record monitoring images i With monitoring image p i-1 In Grid G i_q Using the grid G i_q The average value of the Y coordinate difference of all matching points in the monitoring image p i Relative to the monitoring image p i-1 In Grid G i_q The average pixel displacement within the monitoring image p i The image displacement value corresponding to the monitoring time shift i_i-1_G .

[0097] For capturing monitoring images p r To capture monitoring imagesp i At all monitoring moments in the time period between the two, the cumulative sum of all image displacement values ​​of the grid in the time period is calculated as the image displacement value of the grid in the specific time period. Based on the image displacement value of the grid, the displacement of the corresponding slope area is calculated.

[0098] Preferably, when capturing the monitoring image p i With monitoring image p i-1 The time period between grid G i_qThe corresponding slope displacement is

[0099] When shooting the monitoring image p i With any monitoring image p r Between, grid G i_q The corresponding cumulative displacement of the slope area is Among them, r≤j≤i, 1≤r≤i-1.

[0100] Step 6: Extract the range of slope landslide, using the following calculation strategy:

[0101] (1) For a grid where infrared light source monitoring points are arranged, the displacement value calculated by the infrared light source monitoring points according to the method described in step 3 is used as the displacement value of the slope corresponding to the grid;

[0102] (2) For the grids without the infrared light source center marked but with natural feature points marked:

[0103] When the average brightness of the monitoring image is greater than a preset threshold, the displacement value calculated based on the natural object point according to the method described in step 5 is used as the displacement value of the slope corresponding to the grid;

[0104] When the average brightness of the monitored image is less than or equal to the preset threshold, the image displacement is assigned the average value of the eight-neighborhood image displacement values, and then the corresponding slope area displacement value is calculated.

[0105] (3) For grids that have neither infrared light source monitoring points nor natural object points extracted, the image displacement is assigned the average value of the eight-neighborhood image displacement values, and then the corresponding slope area displacement value is calculated.

[0106] Specifically, the process of assigning the grid image displacement to the average displacement value of the eight-neighborhood grids includes: counting the grid set H with non-zero displacement values ​​in the eight-neighborhood grids of the grid, the number of elements in H is z, and taking the average displacement value of the grid set H as the displacement value of the slope corresponding to the grid

[0107] Based on the above strategy, the time series cumulative displacement value of the slope corresponding to each grid divided by the monitoring image can be obtained.

[0108] For shooting monitoring images p i With any monitoring image p r The time period between grid G i_q The corresponding cumulative displacement of the slope area is SumV i_r_G , set the displacement threshold Thre.

[0109] If SumV i_r_G >Thre, then the grid G ​​is determined i_qThe corresponding slope when capturing the monitoring image p i and the monitoring image p r have a relatively large cumulative displacement between them;

[0110] If SumV i_r_G < Thre, then it is determined that the grid G i_q The corresponding slope when capturing the monitoring image p i and the monitoring image p r have a relatively small cumulative displacement between them.

[0111] Preferably, the monitoring image and the divided grid are displayed through visualization software, and the grids where SumV i_r_G > Thre are connected in the visualization interface to form a closed area, which is the slope landslide range.

[0112] Preferably, the areas of the grids where SumV i_r_G > Thre are accumulated through a background program, which is the total landslide area of the slope and is displayed.

[0113] Compared with the prior art, the present invention has the following remarkable advantages and effects:

[0114] (1) Low monitoring cost. For a single highway slope, the present invention only needs to deploy 1 camera and several infrared light source targets, and the cost is greatly reduced compared with satellite navigation positioning technology, satellite remote sensing monitoring technology, etc.;

[0115] (2) All-weather and all-time uninterrupted monitoring. The present invention uses infrared light source targets to replace conventional targets, enabling high-precision identification and positioning of monitoring targets at night or under low-light conditions. Under sufficient sunlight conditions, both infrared light source targets and natural feature targets are monitored simultaneously, and the displacement of the infrared light source target is used as the initial displacement value of the natural feature target to assist in the displacement calculation of the natural feature target, realizing all-weather and all-time uninterrupted monitoring;

[0116] (3) Simple data processing process. Using the displacement value of the infrared light source target as the initial value of the natural feature point displacement value greatly reduces the matching search range of natural feature points, improving the matching accuracy and calculation efficiency;

[0117] (4) Realize slope surface monitoring. Existing monitoring technologies all monitor the displacement of a small number of discrete points on the slope. The present invention uses the method of setting infrared light source monitoring points and extracting natural feature monitoring points to achieve uniform distribution of slope monitoring points, thereby realizing slope surface monitoring and facilitating the assessment of the scale and destructiveness of slope landslides.

[0118] Embodiment 2

[0119] The present invention also provides a highway slope deformation monitoring system, which is used to implement the highway slope deformation monitoring method described in the above technical solution, including:

[0120] The grid division module is used to grid the initial image of the highway slope deformation area, extract the natural feature points and the infrared light source center, and mark the corresponding grid and image coordinates;

[0121] A displacement calculation module is used to calculate the image displacement value of each grid based on the monitoring image of the highway slope deformation area acquired in real time;

[0122] For a grid marked with the center of an infrared light source, the image displacement value of the grid is calculated based on the image coordinates of the corresponding center of the infrared light source in the continuous monitoring images;

[0123] For the grids without marking the infrared light source center but with marking the natural feature points:

[0124] When the average brightness of the monitoring image is greater than a preset threshold, the image displacement value is calculated based on the image coordinates of the natural object feature points in the continuous monitoring image;

[0125] When the average brightness of the monitored image is less than or equal to the preset threshold, the image displacement of the grid is assigned the average value of the displacement values ​​of the neighboring grids;

[0126] For a grid that has neither infrared light source monitoring points nor natural feature points, the image displacement of the grid is assigned the average value of the displacement values ​​of the neighboring grids;

[0127] Based on the image displacement value of each grid, the displacement of the corresponding slope area is calculated.

[0128] Example 3

[0129] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the highway slope deformation monitoring method described in the above technical solution is implemented.

[0130] Example 4

[0131] The present invention also provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the highway slope deformation monitoring method described in the above technical solution by executing the computer instructions.

[0132] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0137] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A method for monitoring highway slope deformation, characterized in that: The deformation zone of the highway slope is provided with an infrared light source; the process comprises the following steps: Grid the initial image of the highway slope deformation area, extract the natural feature points and the infrared light source center, and mark the corresponding grid and image coordinates; Based on the real-time monitoring images of the highway slope deformation zone, the image displacement value of each grid is calculated; For a grid marked with the center of an infrared light source, the image displacement value of the grid is calculated based on the image coordinates of the corresponding center of the infrared light source in the continuous monitoring images; For the grids without marking the infrared light source center but with marking the natural feature points: When the average brightness of the monitoring image is greater than a preset threshold, the image displacement value is calculated based on the image coordinates of the natural object feature points in the continuous monitoring image; When the average brightness of the monitored image is less than or equal to the preset threshold, the image displacement of the grid is assigned the average value of the displacement values ​​of the neighboring grids; For a grid that has neither infrared light source monitoring points nor natural feature points, the image displacement of the grid is assigned the average value of the displacement values ​​of the neighboring grids; Based on the image displacement value of each grid, the displacement of the corresponding slope area is calculated.

2. The method according to claim 1, characterized in that: Several monitoring piles are buried from top to bottom along the deformation zone of the highway slope; an infrared light source is set on each monitoring pile; a reference pile is buried in a stable area outside the deformation zone of the highway slope, and a monitoring camera is installed on the reference pile to obtain an initial image and a monitoring image.

3. The method according to claim 1, characterized in that: The process of calculating the image displacement value of the grid marked with the center of the infrared light source includes: for each grid marked with the center of the infrared light source, calculating the absolute difference between the vertical coordinates of the images of the center of the infrared light source in two consecutive monitoring images as the image displacement value of the grid at the corresponding monitoring moment; for all monitoring moments within a specified time period, calculating the cumulative sum of all image displacement values ​​of the grid within the time period as the image displacement value of the grid within the specified time period.

4. The method according to claim 1, characterized in that: The process of calculating the image displacement value based on the image coordinates of the natural object feature points in the continuous monitoring images includes: for any grid marked with the natural object feature point, calculating the average value of the difference between the image vertical coordinates of all the natural object feature points in the grid in two consecutive monitoring images as the image displacement value of the grid at the corresponding monitoring moment; for all monitoring moments in a specific time period, calculating the cumulative sum of all the image displacement values ​​of the grid in the time period as the image displacement value of the grid in the specific time period; the specific time period is the time period from the specified monitoring moment to the monitoring moment corresponding to the previous monitoring image whose brightness is greater than a preset threshold value corresponding to the moment.

5. The method according to claim 4, characterized in that: The process of obtaining the natural feature points of each monitoring image within a specific time period except for the specified monitoring moment includes: constructing a search space based on the average pixel displacement of the monitoring images within the specific time period and combining the natural feature points matched at the specified monitoring moment; matching the natural feature points of each monitoring image based on the search space.

6. The method according to claim 4, characterized in that: For the monitoring image p at a specified time i Natural feature points pt i_q_k The search space is defined as follows: i_r_q_k ={x i_q_k -1 <x<x i_q_k +1,y i_q_k -2*Avg_shift i_r <y<y i_q_k +1} Among them, natural feature points pt i_q_k The image coordinates are (x i_q_k ,y i_q_k ), Avg_shift i_r Represents the average pixel displacement of the monitored image within a specific time period.

7. The method according to claim 6, characterized in that: For all monitoring moments within a specific time period, the cumulative sum of the average pixel displacements of the monitoring images within the time period is calculated as the average pixel displacement of the monitoring images within the time period; for any monitoring image, the average value of the absolute difference between the vertical coordinates of the centers of all infrared light sources in the previous monitoring image is calculated as the average pixel displacement of the monitoring image.

8. The method according to claim 1, characterized in that: There is at least one grid marked with the center of an infrared light source in the neighborhood of each grid.

9. The method according to claim 4, characterized in that: The method also includes the following steps: connecting the grids whose image displacement values ​​are greater than a set threshold within a specific time period to form a closed area, marking the slope area corresponding to the closed area as the slope landslide range, and calculating the area of ​​the slope area.

10. A highway slope deformation monitoring system, characterized in that: The system is used to implement the highway slope deformation monitoring method described in any one of claims 1-9.

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