Highway bridge plate-type rubber support rapid inspection method and system based on unmanned aerial vehicle

Abstract

The invention discloses a highway bridge plate-type rubber support rapid inspection method and system based on an unmanned aerial vehicle, and the method comprises the steps: shooting a multi-view image of a bridge and ancillary facilities through the unmanned aerial vehicle, building a bridge surface three-dimensional live-action model with position information, determining the specific coordinate position of the support, and carrying out the detection of the position of the support. Planning a multi-point inspection flight route of the unmanned aerial vehicle support; the planned flight route is imported into an unmanned aerial vehicle remote controller, the unmanned aerial vehicle flies to the bridge position according to a preset route, and a high-definition camera device is used for collecting image data of the plate-type rubber support; the unmanned aerial vehicle transmits collected image data back to the ground control station in real time, and the ground control station performs primary processing and storage on the data; image data preliminarily processed by the ground control station is transmitted to a data analysis processing unit, the apparent state of the plate-type rubber support is comprehensively evaluated, and a detailed detection report is generated; according to the method, targeted detection can be carried out on the bridge support, and the disease type and the disease degree of the support can be quickly and accurately identified.

Description

Technical Field

[0001] The present invention belongs to the technical field of highway bridge inspection, and more specifically, relates to a method and system for rapid inspection of plate rubber bearings of highway bridges based on unmanned aerial vehicles (UAVs). Background Art

[0002] Highway bridges, as an important part of modern transportation infrastructure, their safety and stability are directly related to the efficiency of transportation and public safety. Plate rubber bearings are key components in bridge structures, mainly used to transfer loads, accommodate the displacement and rotation of bridges, and reduce vibration and impact. However, due to the long-term influence of environmental factors (such as temperature changes, humidity, ultraviolet rays, etc.) and load effects, the bearings are prone to damage such as aging, cracking, and deformation, which in turn affects the overall performance and safety of the bridges. Therefore, it is crucial to regularly inspect and maintain the plate rubber bearings of bridges. Traditional bearing inspection methods mainly rely on manual inspection, which requires technicians to climb the bridge structure for close observation and measurement. This method is not only inefficient but also poses safety hazards, especially in complex environments such as viaducts or river-crossing bridges. In addition, the accuracy and coverage of manual inspection are limited, making it difficult to comprehensively and quickly evaluate the condition of the bearings. Therefore, developing an efficient, safe, and accurate bearing inspection method has become an important requirement in the field of bridge maintenance.

[0003] In recent years, with the rapid development of UAV technology, UAV-based bridge inspection methods have gradually become a research hotspot. UAVs have the advantages of strong flexibility, wide coverage, and safe operation, and can effectively make up for the deficiencies of traditional manual inspection. In the prior art, some studies have proposed UAV-based bridge inspection schemes. For example, the patent document with the publication number CN117406781A discloses an intelligent bridge inspection system based on UAVs, which includes a server, a ground station, and a UAV. The ground station includes a modeling module that establishes a three-dimensional model of the bridge based on the data collected when the UAV orbits around the bridge, and a path planning module that plans and generates the cruise path for the UAV during inspection according to the three-dimensional model of the bridge. The UAV automatically flies around the bridge along the generated cruise route and collects image data, and the server analyzes the image data to find the disease locations on the bridge. By establishing a bridge model and automatically planning the inspection route, the present invention can increase the judgment of the overall bridge structure, avoid obstacles that may be encountered during inspection, and at the same time minimize the influence of the operator's control ability on the UAV during the inspection process, ensuring good inspection effect and efficiency. However, it mainly focuses on the inspection of the overall bridge structure, and lacks refined detection means for the inspection of specific structures such as bearings, and there are deficiencies in image data processing and analysis, and it is unable to quickly and accurately identify the disease types and degrees of bearings. Summary of the Invention

[0004] In view of the above defects or improvement requirements of the prior art, the present invention provides a method and system for rapid inspection of plate rubber bearings of highway bridges based on unmanned aerial vehicles (UAVs). By constructing a three-dimensional real-scene model of the bridge surface, determining the specific coordinate positions of the bearings, planning a multi-point inspection flight route of the UAV for the plate rubber bearings to take images and analyzing the image data, a comprehensive evaluation of the apparent state of the plate rubber bearings is carried out to quickly and accurately identify the disease types and disease degrees of the bearings.

[0005] To achieve the above object, according to one aspect of the present invention, the present application provides a method for rapid inspection of plate rubber bearings of highway bridges based on UAVs, including the following specific steps:

[0006] S1: Taking multi-perspective images of the bridge and its ancillary facilities by UAVs, establishing a three-dimensional real-scene model of the bridge surface with position information, determining the specific coordinate positions of the bearings, and planning a multi-point inspection flight route of the UAV for the bearings;

[0007] S2: Importing the planned flight route into the UAV remote controller, flying to the bridge position according to the predetermined route, and using a high-definition camera device to collect image data of the plate rubber bearings;

[0008] S3: The UAV transmits the collected image data back to the ground control station in real time, and the ground control station preliminarily processes and stores the data;

[0009] S4: Transmitting the image data preliminarily processed by the ground control station to the data analysis and processing unit, comprehensively evaluating the apparent state of the plate rubber bearings, and generating a detailed inspection report;

[0010] S5: According to the inspection report, marking and classifying the problematic plate rubber bearings, providing accurate basis for subsequent maintenance and repair decisions, and attaching recommended measures based on data analysis.

[0011] Further, the step S1 includes:

[0012] S11: Initializing and configuring the UAV body, remote controller, data storage unit, and sensor devices;

[0013] S12: Dividing the shooting range according to the bridge to be measured and the surrounding real-scene conditions, including the bridge deck and its ancillary facilities, bridge piers and abutments, beam bottom and bearing connections, setting an appropriate flight altitude and overlap rate, and following the order from left to right, from top to bottom, and from the main body to the ancillary structure to complete the shooting;

[0014] S13: Arrange the aerial images, use the method of matching homologous image points of multi-view images to calculate the spatial position and shooting angle of the camera, generate a dense point cloud, connect the point cloud data, construct a triangular mesh model, map the bridge material texture, establish a three-dimensional real scene model of the bridge surface, and determine the spatial position of the bridge bearings;

[0015] S14: Divide the bearing measurement sections and measurement points according to the determined positions of the plate rubber bearings;

[0016] S15: Plan the multi-point inspection route of the bearing and plan the fixed-point shooting actions of the UAV.

[0017] Furthermore, the establishment of the three-dimensional real scene model of the bridge surface in step S13 includes:

[0018] S131: Import the multi-view image data taken, extract the feature points with significant pixel positions in the images, and according to the collinearity equation of photogrammetry, find the image points representing the same actual ground point in different images by means of homologous image point matching. With the help of the matching information of a large number of homologous image points obtained, use the least squares method for adjustment calculation to minimize the sum of the squared residuals between the image point coordinates and the theoretical values calculated by the model, so as to calculate the three-dimensional coordinates of the ground points and the spatial position and attitude of the camera with high precision, providing an accurate camera parameter basis for subsequent three-dimensional modeling;

[0019] The collinearity equation is shown as follows:

[0020]

[0021] where, (x, y) are the coordinates of the image point in the image plane coordinate system; (x0, y0) are the coordinates of the principal point of the image; f is the camera focal length; (X, Y, Z) are the rectangular coordinates of the ground point in the object space; (X s , Y s , Z s ) are the rectangular coordinates of the object space at the moment when the camera takes the picture; a i , b i , c i (i = 1, 2, 3) are the direction cosines composed of the three attitude angles of the camera heading angle, side angle and image rotation angle;

[0022] S132: After completing the overall adjustment of the area, perform multi-view image matching based on the obtained camera parameters, use the gray information and geometric constraint relationship of the images to perform pixel-by-pixel matching operations between adjacent images, extract the corresponding relationships from the images of different perspectives, and then generate dense point cloud data to accurately represent the three-dimensional spatial shape of the object;

[0023] S133: Using the dense point cloud data obtained by multi-view image matching, connect adjacent points into a series of triangles to construct a three-dimensional irregular triangular network;

[0024] S134: According to the constructed three-dimensional irregular triangular network, further generate a three-dimensional white model through rendering;

[0025] S135: On the basis of the three-dimensional white model, according to the camera parameters and the three-dimensional coordinates of the points on the model surface, establish a mapping relationship from the model space to the image space, determine the image texture pixel positions corresponding to each model surface point, map the texture information of the disease to the model surface. During the texture mapping process, for each patch on the model surface, find its corresponding texture area in the image; when the texture area is discontinuous or deformed, use the bilinear texture interpolation method to calculate the value of the target pixel according to the values of the surrounding four known pixels in the texture space, so that the transition of the texture on the model surface is more natural, and finally generate a three-dimensional real scene model of the bridge surface.

[0026] Further, the step S14 includes:

[0027] After generating the three-dimensional model of the bridge surface, import the three-dimensional model of the bridge surface into the UAV ground control station. Based on the position of the plate rubber bearing in the bridge design drawing and the three-dimensional model, taking the longitudinal center line of the bridge as the reference, combined with the key elements such as the spacing of the bearings, the span of the beam body, the pier and abutment layout method, and the river navigation conditions, divide the plate rubber bearings to be measured into different independent measurement segments; within each measurement segment, then select the top center, edge, and connection part with the beam body of the bearing as the representative measurement points according to the key stress parts and vulnerable areas.

[0028] Further, the step S3 includes:

[0029] Reduce the interference of aerial images by using the dark channel prior algorithm, model dehazing algorithm, image contrast adjustment, and super-resolution reconstruction means; and perform image stitching on multiple overlapping aerial images through the feature extraction and matching algorithm described in S131, extract the key feature points in the images, and then find the corresponding relationship between the images by matching these feature points, and then use perspective transformation to seamlessly stitch multiple images into a complete panoramic view of the bearing, providing a macroscopic perspective for the bearing state analysis.

[0030] Further, the step S4 includes:

[0031] S41: Unify the image size and input it into the pre-trained visual image processing module;

[0032] S42: Through the image recognition algorithm, automatically identify and classify the cracks, bulges, deformations, voids, and aging appearance defects on the surface of the bearing according to the disease category and severity level;

[0033] S43: The visual image processing module generates detection frames to frame out the appearance defect areas and mark and output their relative position information;

[0034] S44: The health status evaluation module classifies and grades the appearance defects according to the appearance defect area and the geometric dimension disease classification and grading standards, and gives the status evaluation results;

[0035] S45: The data storage and management module summarizes the analysis results of the data analysis and processing unit and issues a complete inspection report.

[0036] Further, for the appearance defects described in step S44, according to the crack development form, bulge length, shear deformation angle, and position crosstalk length characteristics, the four detection indexes are assigned scores according to the weight ratio of 4:2:2:2, and are classified into three grades: minor, moderate, and severe.

[0037] Further, the inspection report described in step S45 includes: the inspection indexes and status evaluation results of each plate rubber bearing listed in detail, and at the same time, the bearings with disease risks are highlighted and classified.

[0038] According to another aspect of the present invention, the present application provides an unmanned aerial vehicle-based rapid inspection system for highway bridge plate rubber bearings, which is used to implement the steps of the above-mentioned unmanned aerial vehicle-based rapid inspection method for highway bridge plate rubber bearings, including:

[0039] The unmanned aerial vehicle flight platform is the core execution unit of the entire inspection system, which includes an unmanned aerial vehicle body, a sensor device, a high-definition camera device, and a data transmission module, and is used to complete the shooting of the bridge structure and the inspection task of the bearing;

[0040] The ground control station is connected to the unmanned aerial vehicle flight platform through wireless communication, and includes a remote controller and a data storage unit, and is used to control the flight path and operation tasks of the unmanned aerial vehicle, receive and store the collected image data from the unmanned aerial vehicle and perform preliminary processing;

[0041] The data analysis and processing unit is connected to the ground control station, and is used to analyze the collected image data, judge the appearance defects of the bearing to give the status evaluation results, and generate an inspection report.

[0042] Further, the data analysis and processing unit includes:

[0043] Visual image processing module: Analyze the collected images of bearings through image recognition algorithms, and automatically identify appearance defects such as cracks, bulges, deformations, voids, and aging.

[0044] Health status assessment module: Combine the results of image analysis to evaluate and classify the health status of the bearings, and give the status assessment results.

[0045] Data storage and management module: Used to store the collected images and analysis results in the database, generate detailed inspection reports, and facilitate the query and comparison of historical data, providing support for the long-term health monitoring of bridges.

[0046] Generally speaking, compared with the prior art through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0047] 1. The rapid inspection method of the highway bridge plate rubber bearing based on UAV of the present invention pre-constructs a three-dimensional real scene model of the bridge surface with position information, uses this model to plan the UAV inspection path under the beam, arranges measuring points in different measurement sections, sets different shooting actions, clarifies the specific task objectives to be detected, and then obtains the apparent disease images of the bearings, which can provide data support for bearing detection and disease analysis, improve the bearing detection efficiency, and ensure the safety performance and bearing capacity of the bridge.

[0048] 2. The rapid inspection method of the highway bridge plate rubber bearing based on UAV of the present invention can automatically identify and classify appearance defects such as cracks, bulges, deformations, voids, and aging on the bearing surface according to the image recognition algorithm by the data analysis and processing module, and classify them according to the disease degree, so as to quickly and accurately formulate maintenance and repair plans to ensure the safe operation of the bridge.

[0049] 3. The rapid inspection method of the highway bridge plate rubber bearing based on UAV of the present invention preliminarily processes the aerial images through the ground control station to reduce interference, and stitches multiple images seamlessly into a complete panoramic view of the bearing through the feature extraction and matching algorithm, providing a macroscopic perspective for bearing state analysis and improving the detection accuracy.

[0050] 4. The data analysis and processing unit of the rapid inspection system of the highway bridge plate rubber bearing based on UAV of the present invention can realize few-shot learning for new data sets and task requirements, so it can quickly complete training on a large scale of extensive data and be generalized to downstream tasks, improving the generalization ability of the model. Under different scenario requirements, even when the bearing damage situation category is not clear or multiple damage types may be involved, the bearing defects can be accurately detected, and it can be applied to the detection of bridge bearings with various different configurations and spans. Description of the Drawings

[0051] Figure 1 Flow chart of a rapid inspection method for plate rubber bearings of highway bridges based on unmanned aerial vehicles according to an embodiment of the present invention;

[0052] Figure 2 Flow chart of data analysis and processing of a rapid inspection method for plate rubber bearings of highway bridges based on unmanned aerial vehicles according to an embodiment of the present invention;

[0053] Figure 3 Schematic diagram of a rapid inspection system for plate rubber bearings of highway bridges based on unmanned aerial vehicles according to an embodiment of the present invention. Detailed implementation manners

[0054] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] Embodiment 1

[0056] As Figure 3 shown, an embodiment of the present invention provides a rapid inspection system for plate rubber bearings of highway bridges based on unmanned aerial vehicles, including:

[0057] An unmanned aerial vehicle flight platform, which is the core execution unit of the entire inspection system, includes an unmanned aerial vehicle body, a sensor device, a high-definition camera device, and a data transmission module, and is used to complete the shooting of the bridge structure and the inspection task of the bearings;

[0058] The high-definition camera device is used to obtain clear images of the plate rubber bearings to capture appearance defects such as cracks, bulges, deformations, voids, and aging on the surface of the bearings. The data transmission module transmits the collected images and data to the ground control station in real time. At the same time, the ground control station is allowed to perform real-time control and task adjustment on the unmanned aerial vehicle;

[0059] A ground control station, connected to the unmanned aerial vehicle flight platform through wireless communication, includes a remote controller and a data storage unit, and is used to control the flight path and operation tasks of the unmanned aerial vehicle, and receive and store the images and data collected from the unmanned aerial vehicle and perform preliminary processing;

[0060] A data analysis and processing unit, connected to the ground control station, is used to analyze the collected image data, judge the appearance defects of the bearings and give a status evaluation result, and generate an inspection report. It includes:

[0061] Visual image processing module: Through image recognition algorithms, analyze the collected images of bearings, automatically identify appearance defects such as cracks, bulges, and deformations. The recognition accuracy can reach the millimeter level, and it can accurately locate the disease sites.

[0062] Health status assessment module: Combine the image analysis results to evaluate and classify the health status of the bearings, give the status assessment results, and provide a basis for subsequent maintenance and repair.

[0063] Data storage and management module: Used to store the collected images and analysis results in the database, generate detailed inspection reports, and facilitate the query and comparison of historical data, providing support for the long-term health monitoring of bridges.

[0064] Embodiment 2

[0065] As Figure 1 shown, based on the above system, an embodiment of the present invention provides a rapid inspection method for highway bridge plate rubber bearings based on unmanned aerial vehicles, including the following steps:

[0066] S1: Use an unmanned aerial vehicle to take multi-perspective images of the bridge and its ancillary facilities, establish a three-dimensional real scene model of the bridge surface with position information, determine the specific coordinate positions of the bearings, and plan a multi-point inspection flight route for the bearings of the unmanned aerial vehicle so that it can cover all the positions of the plate rubber bearings of the bridge to be detected.

[0067] Specifically, it includes the following steps:

[0068] S11: Initialize and configure the unmanned aerial vehicle body, remote controller, data storage unit, and sensor equipment;

[0069] S12: According to the actual situation of the bridge to be measured and its surrounding real scene, divide the shooting range, including the bridge deck and its ancillary facilities, bridge piers and abutments, the bottom of the beam and the connection of the bearings, etc., and set an appropriate flight altitude and overlap rate, and follow the order from left to right, from top to bottom, and from the main body to the ancillary structure to complete the shooting; specifically, it includes:

[0070] According to the structural characteristics of the bridge to be measured and the surrounding building environment, delineate the measurement range, set an appropriate flight altitude, ensure that the image overlap rate > 70%, and plan a five-way oblique photography flight route to shoot the bridge deck system and its superstructure. After completing the five-way photography, the flight operator controls the unmanned aerial vehicle to gradually lower the altitude, overlook the bridge, and manually circle around to supplement the shooting;

[0071] According to the characteristics of the bridge side and its ancillary facilities, ensure that the image overlap rate > 70%, and plan a "bow"-shaped flight route to patrol and shoot the bridge body. After completing the "bow"-shaped patrol shooting, lower the altitude of the aircraft below the bottom of the beam and conduct multi-point sweeping and swinging to supplement the shooting;

[0072] According to the structural characteristics of bridge piers and abutments, plan specific flight routes to ensure an image overlap rate > 70%. Plan a "zigzag" flight route. In cases where piers and abutments are relatively dense, manually control the drone and turn off the positioning and obstacle avoidance functions for aerial photography.

[0073] After the shooting is completed, control the drone to fly under the beam and maintain a safe distance to supplement the shooting of the bearing information. During the flight, keep the sensor device level with the bearing position. Combine the laser rangefinder and the drone positioning function to obtain the bearing position information.

[0074] S13: Organize the aerial images, use the method of matching homologous image points of multi-view images to calculate the spatial position and shooting angle of the camera, generate a dense point cloud; connect the point cloud data, construct a triangular mesh model, map the bridge material texture, establish a three-dimensional real scene model of the bridge surface, and determine the spatial position of the bridge bearings; specifically, it includes the following steps:

[0075] S131: According to the collinearity equation in photogrammetry, import the multi-view image data taken, extract the feature points with significant pixel positions such as corner points and edge points in the images, and find the image points representing the same actual ground point in different images through the method of homologous image point matching. With the help of the obtained matching information of a large number of homologous image points, use the least squares method for adjustment calculation to minimize the sum of the squares of the residuals between the image point coordinates and the theoretical values calculated by the model, so as to calculate the three-dimensional coordinates of the ground points and the spatial position and attitude of the camera with high precision, providing an accurate camera parameter basis for subsequent three-dimensional modeling. The collinearity equation is shown as follows:

[0076]

[0077] Among them, (x, y) are the coordinates of the image point in the image plane coordinate system; (x0, y0) are the coordinates of the principal point of the image; f is the camera focal length. (X, Y, Z) are the rectangular coordinates of the ground point in the object space; (X s , Y s , Z s ) are the rectangular coordinates of the object space at the moment when the camera takes the picture; a i , b i , c i (i = 1, 2, 3) are the direction cosines composed of the three attitude angles of the camera heading angle, side angle and image rotation angle.

[0078] S132: After completing the overall adjustment of the area, perform multi-view image matching based on the already obtained camera parameters. Use the gray information and geometric constraint relationships of the images to perform pixel-by-pixel matching operations between adjacent images, extract the corresponding relationships from the images of different perspectives, and then generate dense point cloud data, accurately representing the three-dimensional spatial shape of the object, providing rich data support for constructing a high-precision three-dimensional model of the bridge.

[0079] S133: Construct a three-dimensional irregular triangular mesh using the dense point cloud data obtained from multi-view image matching. According to the spatial distribution of the point cloud, connect adjacent points into a series of triangles, and ensure that no other points in the point cloud set are contained within the circumcircle of each triangle to guarantee the shape quality of the mesh and not affect the accuracy and stability of the model. All the triangular meshes form a network structure covering the surface of the object, fitting the complex three-dimensional geometry of the bridge surface, which is convenient for maintaining topological relationships and geometric calculations in subsequent model processing.

[0080] S134: Further generate a three-dimensional white model based on the constructed three-dimensional irregular triangular mesh. The three-dimensional white model is a three-dimensional geometric model without texture information. Based on the triangular mesh, it presents the geometric shape of the model in a visual way through rendering and other means. During the rendering process, according to the topological relationship and geometric coordinates of the triangular mesh, calculate the position and orientation of each triangular patch in three-dimensional space, and then determine the light and dark effects on the model surface through the lighting model to generate a white model with only geometric shape, preparing for adding bridge texture materials later.

[0081] S135: On the basis of the three-dimensional white model, establish a mapping relationship from the model space to the image space according to the camera parameters and the three-dimensional coordinates of the points on the model surface, determine the image texture pixel positions corresponding to each point on the model surface, and map the texture information of the disease to the model surface. During the texture mapping process, for each patch on the model surface, find its corresponding texture area in the image. When the texture area is discontinuous or deformed, the bilinear texture interpolation method is used. In the texture space, calculate the value of the target pixel according to the values of the surrounding four known pixels, so that the transition of the texture on the model surface is more natural, and finally generate a three-dimensional real scene model of the bridge surface;

[0082] S136: Determine the spatial position of the bridge bearing by combining professional measurement data and the spatial coordinate information in the model.

[0083] S14: Divide the bearing measurement sections and measurement points according to the determined positions of the plate rubber bearings;

[0084] Specifically, after generating the three-dimensional model of the bridge surface, import the three-dimensional model of the bridge surface into the UAV ground control station. First, based on the positions of the plate rubber bearings in the bridge design drawings and the three-dimensional model, with the longitudinal center line of the bridge as the reference, combined with key elements such as the spacing of the bearings, the span of the beam body, the pier and abutment layout, and the river navigation conditions, divide the plate rubber bearings to be measured into different independent measurement sections. Within each measurement section, the top center, edge, and connection parts with the beam body of the bearing can be selected as representative measurement points according to the key stress parts and vulnerable areas.

[0085] S15: Plan the multi - point inspection route of the bearings, plan the fixed - point shooting actions of the UAV, and set specific parameters including the camera shooting angle, the pitching range of the pan - tilt head, the lens zoom factor, optical shooting, and infrared shooting.

[0086] S2: Import the planned flight route into the UAV remote controller, fly the UAV to the bridge position according to the predetermined route, and collect image data of the plate rubber bearings using a high - definition imaging device. Specifically, it includes the following steps:

[0087] S21: Initialize the configuration of the UAV body, remote controller, data storage unit, and sensor devices.

[0088] S22: Take off the UAV at the predetermined position and execute the bearing detection route task.

[0089] S23: Use a high - definition zoom camera to take clear appearance images of the plate rubber bearings.

[0090] During the shooting process, according to the light conditions, such as different scenarios like sunny days, cloudy days, backlighting, etc., reasonably adjust the aperture size, shutter speed, and ISO to ensure that the captured image is clear, bright, and has a high color restoration degree.

[0091] S3: The UAV transmits the collected image data back to the ground control station in real - time, and the ground control station conducts preliminary processing and storage of the data. Specifically, it includes the following contents:

[0092] Aerial images may be affected by various interferences, such as rain and fog interference, strong light interference, lens defocusing, etc. By using relevant means such as the dark channel prior algorithm, model de - fogging algorithm, image contrast adjustment, super - resolution reconstruction, etc., the interference can be reduced. When the ground control station receives multiple aerial images with overlapping areas, the images can also be stitched through the aforementioned feature extraction and matching algorithms, extract the key feature points in the images, and then find the corresponding relationship between the images by matching these feature points. Furthermore, use perspective transformation to seamlessly stitch multiple images into a complete panoramic view of the bearing, providing a macroscopic perspective for bearing state analysis.

[0093] Use a solid - state drive or network storage device with fast read - write speed and large storage space to store the data, and manage it according to a reasonable organizational structure and classification naming.

[0094] S4: Transmit the image data preliminarily processed by the ground control station to the data analysis and processing unit, comprehensively evaluate the apparent state of the plate rubber bearings, and generate a detailed inspection report. Specifically, as Figure 2 shown, it includes the following specific steps:

[0095] S41: Unify the image size and input it into the pre - trained visual image processing module.

[0096] S42: Through the image recognition algorithm, according to the disease category and severity, automatically identify the appearance defects such as cracks, bulges, deformations, voids, aging, etc. on the surface of the bearing and classify them.

[0097] S43: The visual image processing module generates a detection frame to frame out the appearance defect area and mark and output its relative position information.

[0098] S44: The health status assessment module classifies and grades the appearance defects according to the disease classification and grading criteria such as the appearance defect area and geometric dimensions, and gives the status assessment result. Among them, according to the characteristics such as the crack development form, bulge length, shear deformation angle, and position crosstalk length of the appearance defect, the four detection indicators are scored according to the weight ratio of 4:2:2:2, and are divided into three grades: minor, moderate, and severe, generating the status assessment result, and providing suggested measures and maintenance plans.

[0099] S45: The data storage and management module summarizes and analyzes the analysis results of the data analysis processing unit, and issues a complete detection report. The detection report includes: the detailed detection indicators and status assessment results of each plate rubber bearing, and at the same time, the bearings with disease risks are highlighted and classified, so as to quickly and accurately formulate the maintenance plan and ensure the safe operation of the bridge.

[0100] S5: According to the detection report, mark and classify the problematic plate rubber bearings, provide accurate basis for subsequent maintenance and repair decisions, and attach suggested measures based on data analysis. If the bearing is in the minor disease stage, it is recommended to be listed as an observation object and regularly rechecked and monitored; when it is in the moderate disease stage, a targeted maintenance plan is proposed; when it is in the severe disease stage, a warning is immediately issued to the relevant management department.

[0101] It is easy for those skilled in the art to understand that the above is only a preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A rapid inspection method for plate rubber bearings of highway bridges based on unmanned aerial vehicles, characterized in that, It includes the following specific steps: S1: Take multi - perspective images of the bridge and its ancillary facilities through a drone, establish a three - dimensional real - scene model of the bridge surface with position information, determine the specific coordinate positions of the bearings, and plan the multi - point inspection flight route of the drone for the bearings; S2: Import the planned flight route into the drone remote controller, fly to the bridge position according to the predetermined route, and use a high - definition imaging device to collect image data of the plate rubber bearings; S3: The drone transmits the collected image data back to the ground control station in real time, and the ground control station preliminarily processes and stores the data; S4: Transmit the image data preliminarily processed by the ground control station to the data analysis and processing unit, comprehensively evaluate the apparent state of the plate rubber bearings, and generate a detailed inspection report; S5: According to the inspection report, mark and classify the plate rubber bearings with problems, providing an accurate basis for subsequent maintenance and repair decisions.

2. The rapid inspection method of the highway bridge plate rubber bearing based on the unmanned aerial vehicle according to claim 1, characterized in that, The step S1 includes: S11: Initialize and configure the drone body, remote controller, data storage unit, and sensor devices; S12: According to the situation of the bridge to be measured and the surrounding real scene, divide the shooting range, including the bridge deck and its ancillary facilities, bridge piers and abutments, beam bottom and bearing connections, and set an appropriate flight altitude and overlap rate. Follow the order from left to right, from top to bottom, and from the main body to the ancillary structure to complete the shooting; S13: Organize the aerial images, use the method of matching corresponding image points in multi - perspective images, solve the spatial position and shooting angle of the camera, generate a dense point cloud, connect the point cloud data, construct a triangular mesh model, map the bridge material texture, establish a three - dimensional real - scene model of the bridge surface, and determine the spatial position of the bridge bearings; S14: According to the determined positions of the plate rubber bearings, divide the bearing measurement sections and measurement points; S15: Plan the multi - point inspection flight route of the bearings and plan the fixed - point shooting actions of the drone.

3. The rapid inspection method for the plate rubber bearings of highway bridges based on drones according to claim 2, characterized in that, The establishment of the three - dimensional real - scene model of the bridge surface in step S13 includes: S131: Import the multi - perspective image data taken, extract the feature points with significant pixel positions in the images. According to the collinearity equation in photogrammetry, find the image points representing the same actual ground point in different images through the method of matching corresponding image points. With the help of the matching information of a large number of corresponding image points, use the least - squares method for adjustment calculation to minimize the sum of the squares of the residuals between the image point coordinates and the theoretical values calculated by the model, so as to solve the three - dimensional coordinates of the ground points and the spatial position and attitude of the camera with high precision, providing an accurate camera parameter basis for subsequent three - dimensional modeling; The collinearity equation is shown as follows: where (x, y) are the coordinates of the image point in the image plane coordinate system; (x0, y0) are the coordinates of the principal point of the image; f is the focal length of the camera; (X, Y, Z) are the object space rectangular coordinates of the ground point; (X s , Y s , Z s ) are the object space rectangular coordinates of the camera's position at the moment of shooting; a i , b i , c i (i = 1, 2, 3) are the direction cosines formed by the three attitude angles of the camera's heading angle, cross-track angle, and image rotation angle; S132: After completing the overall adjustment of the area, perform multi - perspective image matching based on the obtained camera parameters. Use the gray - scale information and geometric constraint relationships of the images to perform pixel - by - pixel matching operations between adjacent images, excavate the corresponding relationships from images with different perspectives, and then generate dense point cloud data, accurately representing the three - dimensional spatial shape of the object; S133: Use the dense point cloud data obtained by multi - perspective image matching to connect adjacent points into a series of triangles to construct a three - dimensional irregular triangular network; S134: Further generate a 3D white model through rendering based on the constructed 3D irregular triangular mesh; S135: On the basis of the 3D white model, establish a mapping relationship from the model space to the image space according to the camera parameters and the 3D coordinates of the model surface points, determine the image texture pixel positions corresponding to each model surface point, and map the texture information of the disease to the model surface. During the texture mapping process, for each patch on the model surface, find its corresponding texture area in the image; when the texture area is discontinuous or deformed, use the bilinear texture interpolation method to calculate the value of the target pixel according to the values of the surrounding four known pixels in the texture space, so as to make the transition of the texture on the model surface more natural, and finally generate a 3D real-scene model of the bridge surface.

4. A rapid inspection method for highway bridge plate rubber bearings based on unmanned aerial vehicles according to claim 3, characterized in that The step S14 includes: After generating the 3D model of the bridge surface, import the 3D model of the bridge surface into the UAV ground control station. Based on the position of the plate rubber bearing in the bridge design drawing and the 3D model, taking the longitudinal center line of the bridge as the reference, and combining key elements such as the spacing of the bearings, the span of the beam body, the pier and abutment layout method, and the river navigation conditions, divide the plate rubber bearings to be measured into different independent measurement segments; within each measurement segment, select the top center, edge, and connection part with the beam body of the bearing as representative measurement points according to the key stress parts and vulnerable areas.

5. A rapid inspection method for highway bridge plate rubber bearings based on drones according to any one of claims 1-4, characterized in that, The step S3 includes: Reduce the interference of aerial images by using the dark channel prior algorithm, model dehazing algorithm, image contrast adjustment, and super-resolution reconstruction means; and perform image stitching on multiple aerial images with overlapping areas through the feature extraction and matching algorithm described in S131, extract the key feature points in the images, and then find the corresponding relationship between the images by matching these feature points, and then use perspective transformation to seamlessly stitch multiple images into a complete panoramic view of the bearing, providing a macroscopic perspective for the bearing state analysis.

6. A method for rapid inspection of plate rubber bearings of highway bridges based on unmanned aerial vehicles according to any one of claims 1-4, characterized in that, The step S4 includes: S41: Unify the image size and input it into the pre-trained visual image processing module; S42: Through the image recognition algorithm, automatically identify and classify the cracks, bulges, deformations, voids, and aging appearance defects on the bearing surface according to the disease category and severity; S43: The visual image processing module generates a detection frame, frames the appearance defect area, and marks and outputs its relative position information; S44: The health status evaluation module classifies and grades the appearance defects according to the appearance defect area and the geometric size disease classification and grading standards, and gives the status evaluation results; S45: The data storage and management module statistically summarizes the analysis results of the data analysis and processing unit and issues a complete detection report.

7. The rapid inspection method of a highway bridge plate rubber bearing based on an unmanned aerial vehicle according to claim 6, wherein In step S44, the appearance defects are scored according to the crack development form, bulge length, shear deformation angle, and position crosstalk length characteristics, and the four detection indicators are assigned scores according to the weight ratio of 4:2:2:2, and are divided into three grades: minor, moderate, and severe.

8. A rapid inspection method for plate rubber bearings of highway bridges based on unmanned aerial vehicles according to claim 7, characterized in that, The detection report described in step S45 includes: the detection indicators and status evaluation results of each plate rubber bearing listed in detail, and at the same time, the bearings with disease risks are prominently marked and classified.

9. A rapid inspection system for highway bridge plate rubber bearings based on unmanned aerial vehicles, which is used to implement the steps of a rapid inspection method for highway bridge plate rubber bearings based on unmanned aerial vehicles as described in any one of claims 1-8, characterized in that, including: The UAV flight platform is the core execution unit of the entire inspection system. It includes a UAV airframe, sensor devices, a high-definition camera device, and a data transmission module, and is used to complete the shooting of the bridge structure and the inspection task of the bearings; The ground control station is connected to the UAV flight platform through wireless communication. It includes a remote controller and a data storage unit, and is used to control the flight path and operation tasks of the UAV, receive and store the image data collected from the UAV and perform preliminary processing; The data analysis and processing unit is connected to the ground control station, and is used to analyze the collected image data, judge the appearance defects of the bearings and give the status evaluation results, and generate inspection reports.

10. The rapid inspection system for highway bridge plate rubber bearings based on unmanned aerial vehicles according to claim 9, characterized in that The data analysis and processing unit includes: Visual image processing module: Analyze the collected bearing images through image recognition algorithms, and automatically identify appearance defects such as cracks, bulges, deformations, voids, and aging; Health status evaluation module: Combine the image analysis results to evaluate and grade the health status of the bearings, and give the status evaluation results; Data storage and management module: Used to store the collected images and analysis results in the database, generate detailed inspection reports, and facilitate the query and comparison of historical data, providing support for the long-term health monitoring of the bridge.

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