Method and system for automatically monitoring global deformation of reinforced concrete beam

Abstract

The invention provides a reinforced concrete beam global deformation automatic monitoring method and system, and relates to the technical field of engineering structure intelligent monitoring, and the method comprises the steps: obtaining an original image of a reinforced concrete beam; inputting the original image into the image segmentation model, and outputting a binary mask image; determining a center point of a beam body according to a foreground region in the binary mask image; identifying the edge of the foreground region through an edge detection algorithm and a contour extraction algorithm, and extracting an outer contour point set of the beam body; redundant points in the beam body outer contour point set are removed, and a beam body candidate corner point set is obtained; determining a plurality of target beam body angular points in the beam body candidate angular point set through a heuristic optimization algorithm; dividing the binary mask image into a plurality of edge regions through a central radioactive ray division method; dividing each outer contour point in the outer contour point set of the beam body into a corresponding edge area; constructing a linear profile curve; and calculating the vertical global deformation of the reinforced concrete beam according to the linear profile curve.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring of engineering structures, and in particular to a method and system for automatically monitoring the global deformation of reinforced concrete beams. Background Art

[0002] With the continued development of infrastructure projects such as buildings and bridges, reinforced concrete beams, as key load-bearing components, inevitably undergo plastic deformation during their long-term service. These deformation characteristics directly reflect the degradation of structural performance and become important indicators for assessing structural health. Timely and accurate acquisition of global deformation information for reinforced concrete beams provides a crucial basis for structural health assessment and safety risk warning.

[0003] In the existing technology, deformation monitoring of reinforced concrete beams usually relies on traditional corner point extraction methods, which identify structural boundaries through fixed threshold judgment or local geometric features (such as curvature mutation).

[0004] However, in real-world projects, due to factors such as stress deformation, image capture angle, distortion, and apparent damage, the edges of reinforced concrete beams often no longer maintain a standard rectangular shape. Instead, they may exhibit slight curvature, blunt corners, or edge discontinuities. Consequently, traditional corner point extraction methods struggle to accurately extract the four true structural corners when faced with complex and irregular boundaries, compromising the accuracy of deformation measurements.

[0005] In addition, with the increasing complexity and deformation degree of building structures, the accuracy and robustness of traditional reinforced concrete beam full-area deformation automatic monitoring methods are gradually unable to meet actual monitoring needs. They are difficult to adapt to various non-standard contour forms and fail to effectively handle the deformation monitoring task of reinforced concrete beams in complex backgrounds. Summary of the Invention

[0006] In order to solve the technical problems that the traditional corner point extraction method in the prior art is difficult to accurately extract the four real structural corner points when facing complex and irregular boundaries, thereby affecting the accuracy of deformation measurement, and the accuracy and robustness of the traditional reinforced concrete beam full-area deformation automatic monitoring method are gradually unable to meet the actual monitoring needs, it is difficult to adapt to a variety of non-standard contour forms, and it fails to effectively handle the deformation monitoring task of reinforced concrete beams in complex backgrounds, the present invention provides a reinforced concrete beam full-area deformation automatic monitoring method and system.

[0007] The technical solutions provided by the embodiments of the present invention are as follows:

[0008] First aspect:

[0009] An embodiment of the present invention provides a method for automatically monitoring the global deformation of reinforced concrete beams, comprising:

[0010] S1: Get the original image of reinforced concrete beam;

[0011] S2: Input the original image into the image segmentation model, and output a binary mask image including the reinforced concrete beam;

[0012] S3: Determine the center point of the beam according to the foreground area in the binary mask image;

[0013] S4: Using edge detection and contour extraction algorithms, identify the edges of the foreground area and extract the outer contour point set of the beam;

[0014] S5: Using the Douglas-Peucker polygon approximation algorithm, redundant points in the beam body contour point set are eliminated to obtain a set of candidate beam corner points.

[0015] S6: With the goal of minimizing the contour reconstruction error and minimizing the number of beam corner points, a heuristic optimization algorithm is used to determine multiple target beam corner points from the set of candidate beam corner points;

[0016] S7: Divide the binary mask image into multiple edge regions according to the beam center point and each target beam corner point by using a center radial line division method;

[0017] S8: Divide each outer contour point in the beam outer contour point set into a corresponding edge area;

[0018] S9: extract all outer contour points in multiple edge regions and construct a linear contour curve;

[0019] S10: Calculate the vertical global deformation of the reinforced concrete beam according to the linear profile curve and the reference benchmark curve in the historical monitoring process, so as to automatically monitor the global deformation of the reinforced concrete beam.

[0020] Second aspect:

[0021] An embodiment of the present invention provides a reinforced concrete beam global deformation automatic monitoring system, comprising:

[0022] processor;

[0023] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for automatic monitoring of the global deformation of reinforced concrete beams as described in the first aspect is implemented.

[0024] The third aspect:

[0025] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for automatically monitoring the global deformation of reinforced concrete beams as described in the first aspect is implemented.

[0026] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0027] In an embodiment of the present invention, redundant points in the outer contour point set of the beam body are eliminated through the Douglas-Peucker polygon approximation algorithm to obtain a set of candidate corner points of the beam body, and with the goal of minimizing the contour reconstruction error and minimizing the number of beam body corner points, a heuristic optimization algorithm is used to determine multiple target beam body corner points in the candidate corner point set of the beam body. When faced with complex and irregular boundaries, four real structural corner points can be accurately extracted, thereby improving the accuracy of deformation measurement. By extracting all outer contour points in multiple edge areas, a linear contour curve is constructed, and based on the linear contour curve and the reference benchmark curve in the historical monitoring process, the vertical global deformation of the reinforced concrete beam is calculated to automatically monitor the global deformation of the reinforced concrete beam. The accuracy and robustness can meet actual monitoring needs, can adapt to a variety of non-standard contour forms, and can also effectively handle the deformation monitoring task of reinforced concrete beams in complex backgrounds. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A schematic flow chart of a method for automatically monitoring the global deformation of reinforced concrete beams provided by an embodiment of the present invention;

[0030] Figure 2 A schematic structural diagram of a system for automatically monitoring the global deformation of reinforced concrete beams provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0032] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0033] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0034] In the embodiment of the present invention, sometimes a subscript such as W1 may be written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0035] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0036] Reference Manual Figure 1 , which shows a flow chart of a method for automatic monitoring of full-area deformation of reinforced concrete beams provided by an embodiment of the present invention.

[0037] An embodiment of the present invention provides a method for automatically monitoring the global deformation of reinforced concrete beams. This method can be implemented using a device for automatically monitoring the global deformation of reinforced concrete beams, which can be a terminal or a server. The process flow of the method for automatically monitoring the global deformation of reinforced concrete beams may include the following steps:

[0038] S1: Get the original image of reinforced concrete beam.

[0039] Optionally, an industrial camera or other image acquisition device is used to acquire an original image of the reinforced concrete beam.

[0040] In this embodiment of the present invention, by using an industrial camera or other image acquisition device to capture raw images of reinforced concrete beams, the accuracy, efficiency, and adaptability of image acquisition can be improved, while reducing errors caused by manual operation, ensuring that subsequent image processing and deformation monitoring processes are more accurate and efficient. This provides more reliable technical support for structural health monitoring, intelligent construction, and preventive maintenance.

[0041] S2: Input the original image into the image segmentation model and output a binary mask image including the reinforced concrete beam.

[0042] Among them, the image segmentation model is specifically: an image segmentation model based on a deep neural network.

[0043] Optionally, the deep neural network is specifically: U-Net or Mask R-CNN or SAM.

[0044] U-Net is a deep neural network architecture for image segmentation, originally designed for medical image segmentation. It uses a symmetrical encoder-decoder structure, where the encoder extracts image features and the decoder restores the image's spatial resolution through upsampling. U-Net is unique in that it passes feature maps from the encoder directly to the decoder via skip connections, preserving high-resolution spatial information while restoring image details. This enables U-Net to effectively handle pixel-level segmentation tasks, especially when data is limited.

[0045] Mask R-CNN is an extension of the Region Convolutional Neural Network (R-CNN) for simultaneous object detection and image segmentation. It builds on Faster R-CNN by adding a branch to generate pixel-level masks for each detected object. Mask R-CNN first performs region proposal generation (RPN), then classifies, regresses bounding boxes, and generates masks for each proposal. It can accurately generate a pixel-level segmentation mask for each object while detecting it, making it suitable for complex instance segmentation tasks.

[0046] SAM (Segment Anything Model) is a general-purpose image segmentation model based on deep learning, launched by Meta (formerly Facebook). SAM is designed to handle a wide range of image segmentation tasks, including semantic, instance, and panoptic segmentation. Combining large-scale pre-trained data with powerful image segmentation capabilities, SAM can automatically identify and segment object boundaries in any input image. The model can perform high-quality segmentation with minimal user interaction and has demonstrated excellent performance in applications across diverse domains.

[0047] It should be noted that U-Net, Mask R-CNN and SAM are all existing technologies and will not be described in detail in the present invention.

[0048] In an embodiment of the present invention, inputting the original image into a deep neural network image segmentation model enables automated, accurate, and efficient structural extraction of reinforced concrete beams, reducing human intervention, improving monitoring accuracy, enhancing model adaptability, and supporting real-time monitoring. Deep learning segmentation models (such as U-Net, Mask R-CNN, and SAM) not only improve processing efficiency but also provide robust segmentation performance in various complex environments, providing a reliable foundation for subsequent deformation monitoring, structural assessment, and other tasks.

[0049] S3: Determine the center point of the beam according to the foreground area in the binary mask image.

[0050] It should be noted that the foreground area refers to the set of pixels corresponding to the reinforced concrete beam in the binary mask image. These pixels usually represent the actual structure of the beam. The background area refers to the area unrelated to the beam, and the pixel values ​​in the background area are set to 0.

[0051] The foreground area is specifically:

[0052] F={(x j ,y j )|M(x j ,y j )=1,j=1,2,…,M}

[0053] Among them, F represents the foreground area, x j Indicates the horizontal coordinate of the j-th pixel in the foreground area, y j represents the ordinate of the jth pixel in the foreground area, and M represents the total number of pixels in the foreground area.

[0054] In an embodiment of the present invention,

[0055] In a possible implementation, S3 is specifically:

[0056] Determine the center point of the beam according to the following formula:

[0057]

[0058] Among them, x0 represents the horizontal coordinate of the center point of the beam, and y0 represents the vertical coordinate of the center point of the beam.

[0059] In this embodiment of the present invention, by calculating the beam center point based on a weighted average of foreground pixels, a highly accurate, rapid, and automated center point location method can be provided. This not only avoids errors caused by manual intervention but also adapts to beams of various shapes and deformations, providing a reliable foundation for subsequent deformation monitoring. This method significantly improves the accuracy, automation, and robustness of the monitoring system, making it an indispensable component of structural health monitoring.

[0060] S4: Using edge detection and contour extraction algorithms, identify the edges of the foreground area and extract the outer contour point set of the beam.

[0061] Edge detection is an image processing technique designed to identify areas of image brightness with significant variations, typically corresponding to object boundaries. By analyzing changes in pixel intensity, the algorithm identifies the areas of fastest change within the image and thus marks the image's edges. Common edge detection algorithms include Canny edge detection, the Sobel operator, and the Prewitt operator. Canny edge detection is a classic algorithm that effectively extracts edge information from images through a multi-stage process involving noise removal, gradient calculation, non-maximum suppression, and edge connection.

[0062] Contour extraction algorithms are used to identify and extract boundaries or object outlines from images and are typically applied after edge detection. These algorithms connect edge points in an image to form continuous closed curves, thereby obtaining information about the object's geometric shape. Common contour extraction algorithms include the findContours function in OpenCV, which extracts a set of contour points from an image and organizes them into a hierarchical structure, enabling further analysis and processing of the contours, such as shape analysis, area calculation, and contour matching. These algorithms are widely used in fields such as object recognition, shape analysis, and image segmentation.

[0063] Specifically, the binary mask image is first processed by an edge detection algorithm (such as Canny edge detection or Sobel operator) to identify the edges of the foreground area. The edge detection algorithm extracts the locations of significant changes from the background to the foreground, i.e., the edges, by analyzing the changes in pixel intensity in the image. Next, the image after edge detection is further processed using a contour extraction algorithm (such as the findContours function in OpenCV) to extract the boundaries of the foreground area. The contour extraction algorithm aggregates connected edge pixels in the image into closed contour segments and returns a set of coordinate points for all contours. Ultimately, the extracted outer contour point set of the beam contains the boundary information of the foreground area and can be used as basic data for subsequent analysis (such as corner extraction and deformation calculation).

[0064] Among them, the specific external contour point set of the beam is:

[0065] C={(x i ,y i )|i=1,2,…,N}

[0066] Among them, C represents the external contour point set of the beam, x i Indicates the horizontal coordinate of the outer contour point of the i-th beam, y i represents the ordinate of the i-th external contour point of the beam, and N represents the total number of external contour points of the beam.

[0067] In this embodiment of the present invention, edge detection and contour extraction algorithms are used to extract the outer contour point set of reinforced concrete beams. This allows for precise identification of the beam's geometry, removal of irrelevant information, and reduction of noise interference. This process also provides reliable data support for subsequent deformation analysis and corner point extraction. This process not only improves the accuracy of deformation monitoring but also ensures the automation and efficiency of the monitoring system, adapting to complex real-world environments and structural configurations, providing a strong technical foundation for structural health monitoring.

[0068] S5: Using the Douglas-Peucker polygon approximation algorithm, redundant points in the beam outer contour point set are eliminated to obtain the beam candidate corner point set.

[0069] Among them, the Douglas-Peucker polygon approximation algorithm is an algorithm used to simplify polygons or curves. Its main goal is to reduce the number of points in the polygon or curve by minimizing the geometric error while preserving its original shape as much as possible. The basic idea of ​​the algorithm is to start from the two ends of the curve, recursively select the farthest point, and determine whether the point sufficiently affects the overall shape. If the vertical distance of the point is greater than the given tolerance error, then the point is retained and the remaining part is processed recursively. Otherwise, the point is removed and the two end points are directly connected. By continuously iterating this process, a simplified polygon or curve is eventually obtained, which not only reduces the number of points but also maintains the main characteristics of the curve. It is often used in applications such as map simplification and path optimization.

[0070] Specifically, the Douglas-Peucker polygon approximation algorithm recursively simplifies the outer contour point set and eliminates redundant points, thereby obtaining a simpler contour. The core idea of ​​the algorithm is to approximate the contour curve with the least number of broken line segments while preserving the overall shape of the contour. First, starting from the first and last two points of the outer contour point set, the algorithm calculates the perpendicular distance of all points to this straight line, and finds the point farthest from the straight line based on the preset error tolerance. If the distance of the point exceeds the tolerance, the point is retained and used as the endpoint of the new broken line segment. If the distance is less than the tolerance, it is considered that the point has little effect on the contour shape and can be eliminated. This process is repeated recursively until all points are simplified to the fewest possible broken line segments. Ultimately, the simplified point set obtained is the candidate corner point set of the beam, which contains the points that have the greatest impact on the shape of the beam and can accurately represent the main features of the beam.

[0071] Among them, the candidate corner point set of the beam body is specifically:

[0072] P={(x1′,y1′),(x2′,y2′),…,(x′ K ,y′ K )},4≤K≤N

[0073] Among them, P represents the set of candidate corner points of the beam, x ′ k Indicates the horizontal coordinate of the kth candidate corner point of the beam, y′ k Represents the ordinate of the kth candidate corner point of the beam, where k = 1, 2, …, K, and K represents the total number of candidate corner points of the beam.

[0074] In this embodiment of the present invention, the Douglas-Peucker polygon approximation algorithm is used to eliminate redundant points and simplify the beam's outer contour point set. This not only reduces data volume and improves computational efficiency, but also preserves the beam's primary shape features, ensuring the accuracy of subsequent corner point extraction and deformation monitoring. This algorithm is highly adaptable, capable of handling complex structural deformations, and effectively improves the automation and robustness of the entire monitoring system.

[0075] S6: With the goal of minimizing the contour reconstruction error and minimizing the number of beam corner points, a heuristic optimization algorithm is used to determine multiple target beam corner points from the set of candidate beam corner points.

[0076] Optionally, the heuristic optimization algorithm is specifically: a genetic algorithm, a particle swarm optimization algorithm, or a simulated annealing algorithm.

[0077] Among them, a genetic algorithm is an optimization algorithm that simulates natural selection and heredity, finding the optimal solution by simulating the biological evolution process. Its basic steps include selection, crossover, mutation, and evaluation. In a genetic algorithm, each solution is represented as an "individual" or "chromosome." By selecting the winning individuals and performing crossover and mutation, new solutions are generated, and the quality of each solution is evaluated according to a fitness function. After multiple generations of evolution, the fitness of the population gradually improves, and the optimal solution is eventually found. Genetic algorithms are applicable to a variety of optimization problems and are particularly effective when the solution space is large or complex.

[0078] Among them, the particle swarm optimization algorithm is an optimization algorithm that simulates the foraging behavior of bird flocks. In PSO, each solution in the solution space is considered a "particle," each with a position and velocity. Particles continuously update their positions and velocities to search for the optimal solution. Each particle not only adjusts its position based on its own historical experience but also adjusts its search direction based on information about the optimal solution in the swarm. Through collaboration and information exchange between particles, PSO can efficiently search the solution space and achieve good results in a variety of optimization problems.

[0079] Among them, the simulated annealing algorithm is a random search-based optimization algorithm inspired by the physical annealing process. By simulating the atomic motion of metal during cooling, the algorithm gradually lowers the "temperature" during the search process, thereby gradually reducing the range of variation in the solution. Initially, the algorithm tolerates less favorable solutions to avoid falling into local optima. As the temperature decreases, the algorithm gradually converges to the global optimal solution. The simulated annealing algorithm is particularly well-suited for solving large-scale or complex combinatorial optimization problems, and is particularly effective when dealing with optimization problems with multiple local extremes.

[0080] It should be noted that the genetic algorithm, particle swarm optimization algorithm and simulated annealing algorithm are all existing technologies and will not be described in detail in the present invention.

[0081] In this embodiment of the present invention, heuristic optimization algorithms (such as genetic algorithms, particle swarm optimization, and simulated annealing) are used to minimize contour reconstruction errors and the number of corner points. This optimizes the number and quality of corner points while maintaining the geometric accuracy of the beam. This method improves the accuracy of corner point extraction, reduces redundant points, and enhances the robustness of the system, providing efficient and accurate data support for subsequent deformation monitoring and structural health assessment.

[0082] In a possible implementation, S6 specifically includes sub-steps S601 and S602:

[0083] S601: With the goal of minimizing the contour reconstruction error and minimizing the number of beam corner points, a dual-objective fitness function is constructed:

[0084] Fitness=w1·E rec +w2·N pts

[0085]

[0086] Among them, Fitness represents the dual-objective fitness function, w1 represents the weight coefficient of the contour reconstruction error, E rec represents the contour reconstruction error, w2 represents the weight coefficient of the number of beam corner points, N pts represents the number of corner points of the beam, l represents the edge, L represents the edge set of the closed quadrilateral formed by the corner points, and dist 2 (x i ,l) represents the vertical distance from the outer contour point of the i-th beam to the edge l.

[0087] S602: According to the dual-objective fitness function, a heuristic optimization algorithm is used to determine multiple target beam corner points in the beam candidate corner point set, wherein the target beam corner points include the upper left corner point A of the beam, the upper right corner point B of the beam, the lower right corner point C of the beam, and the lower left corner point D of the beam.

[0088] In this embodiment of the present invention, by constructing a dual-objective fitness function and employing a heuristic optimization algorithm to simultaneously minimize contour reconstruction error and the number of beam corner points, it is possible to reduce redundant corner points while maintaining structural shape accuracy, thereby improving subsequent computational efficiency and the accuracy of deformation monitoring. The heuristic optimization algorithm ensures robustness and accuracy in corner point extraction through a global search. Overall, this optimization method not only improves automation and intelligence, but also enhances the system's adaptability and stability, making it particularly suitable for large-scale, complex structural health monitoring tasks.

[0089] S7: According to the beam center point and each target beam corner point, the binary mask image is divided into multiple edge regions by a center radial line division method.

[0090] The center-radial segmentation method is a method for segmenting image regions based on the geometric center and radial rays. In this method, the geometric center of the image is first determined as a reference point. Then, rays are emitted from the center point toward the four corners of the image, dividing the entire image into multiple sectors. These rays divide the image edges into distinct sectors, each corresponding to a specific boundary or structural component. This method is often used to segment complex structures. It can effectively segment images based on the radial distribution of the center point, facilitating subsequent edge identification, analysis, and processing.

[0091] In this embodiment of the present invention, by using a central radial segmentation method, complex reinforced concrete beam structure images can be efficiently and accurately divided into multiple edge regions, thereby providing more accurate basic data for subsequent deformation analysis, corner point extraction, and other tasks. This method is not only automated, robust, and highly adaptable, but also improves monitoring accuracy and processing efficiency, significantly enhancing system performance and reliability, especially in large-scale structural monitoring and real-time monitoring scenarios.

[0092] Optionally, the edge area includes an upper edge area AOB, a right edge area BOC, a lower edge area COD, and a left edge area AOD.

[0093] In a possible implementation, S7 specifically includes sub-steps S701 to S705:

[0094] S701: With the center point of the beam as the origin O of the binary mask image, four rays are emitted in sequence to the upper left corner point A of the beam, the upper right corner point B of the beam, the lower right corner point C of the beam, and the lower left corner point D of the beam to obtain the first ray OA, the second ray OB, the third ray OC, and the fourth ray OD.

[0095] Optionally, the first ray OA is specifically:

[0096]

[0097] Among them, y OA,z represents the ordinate of the zth point on the first ray OA, x A Indicates the horizontal coordinate of point A at the upper left corner of the beam, y A represents the vertical coordinate of the upper left corner point A of the beam, x0 represents the horizontal coordinate of the center point of the beam, y0 represents the vertical coordinate of the center point of the beam, and x OA,z Represents the horizontal coordinate of the zth point on the first ray OA.

[0098] Optionally, the second ray OB is specifically:

[0099]

[0100] Among them, y OB,z′ represents the ordinate of the z′th point on the second ray OB, x B Indicates the horizontal coordinate of point B at the upper right corner of the beam, y B Indicates the vertical coordinate of the upper right corner point B of the beam, X OB,z′ Represents the abscissa of the z′th point on the second ray OB.

[0101] Optionally, the third ray OC is specifically:

[0102]

[0103] Among them, y OC,z″ represents the ordinate of the z″th point on the third ray OC, x C Indicates the horizontal coordinate of the lower right corner point C of the beam, y C Indicates the vertical coordinate of the lower right corner point C of the beam, x OC,z″ Represents the abscissa of the z″th point on the third ray OC.

[0104] Optionally, the fourth ray OD is specifically:

[0105]

[0106] Among them, y OD,z″′ represents the ordinate of the z″′th point on the fourth ray OD, x D Indicates the horizontal coordinate of the lower left corner point D of the beam, y D Indicates the vertical coordinate of the lower left corner point D of the beam, x OD,z″′ Represents the abscissa of the z″′th point on the fourth ray OD.

[0107] S702: The area between the first ray OA and the second ray OB is used as the upper edge area AOB.

[0108] S703: The area between the second ray OB and the third ray OC is taken as the right edge area BOC.

[0109] S704: The area between the third ray OC and the fourth ray OD is used as the lower edge area COD.

[0110] S705: Using the area between the first ray OA and the fourth ray OD as the left edge area AOD, the division of the binary mask image is completed.

[0111] In an embodiment of the present invention, by using the central radial partitioning method, the binary mask image of the reinforced concrete beam can be effectively divided into multiple edge regions, thereby simplifying the geometric structure of the image and improving the accuracy of edge recognition. This method not only improves the accuracy and automation of deformation analysis, but also enhances the system's adaptability to different beam shapes, environmental interference, and deformation. Overall, the central radial partitioning method provides an efficient, accurate, and robust structural monitoring solution, especially in large-scale and complex environments, showing obvious advantages.

[0112] S8: Divide each outer contour point in the beam outer contour point set into a corresponding edge area.

[0113] Specifically, first traverse all the outer contour points in the outer contour point set of the beam, and determine which edge area the point belongs to based on the coordinate value of each outer contour point. According to the preset conditions, determine the edge area where each outer contour point is located one by one: select the points on the first ray OA, the second ray OB, the third ray OC and the fourth ray OD that have the same horizontal coordinates as the outer contour point, and compare the vertical coordinates. When the vertical coordinates of the outer contour point are both greater than or equal to y OC and y OD When the ordinate of the outer contour point is less than or equal to y OA and y OB When the vertical coordinate of the outer contour point is greater than or equal to y OA and less than or equal to y OD When the ordinate of the outer contour point is greater than or equal to y OB and less than or equal to y OC When , the point is judged to be in the right edge area. Through these judgments, each outer contour point is accurately divided into the corresponding edge area.

[0114] In this embodiment of the present invention, by dividing each outer contour point into corresponding edge regions, precise edge recognition and deformation analysis can be achieved, improving the accuracy and efficiency of deformation monitoring. This automated segmentation process avoids manual intervention and errors, adapts to complex beam deformation situations, and provides high-quality data support for subsequent tasks such as health assessment and deformation calculation. This makes the monitoring system more efficient and intelligent, capable of providing accurate data and decision support for real-time, complex structural monitoring.

[0115] S9: Extract all outer contour points in the edge area and construct a linear contour curve.

[0116] In this embodiment of the present invention, by extracting all outer contour points from multiple edge regions and constructing linear contour curves, the accuracy and precision of deformation analysis are improved while also ensuring contour integrity and data accuracy. This method efficiently and accurately captures structural deformation and boundary information, providing robust data support for subsequent deformation calculations and structural health assessments.

[0117] In a possible implementation, S9 specifically includes sub-steps S901 to S903:

[0118] S901: Extract all outer contour points in the upper edge area or the lower edge area.

[0119] S902: Arrange all the extracted outer contour points in ascending order of the horizontal coordinates.

[0120] S903: Connect all outer contour points in order of arrangement to construct a linear contour curve.

[0121] In this embodiment of the present invention, by extracting all outer contour points from the upper and lower edge regions, arranging them in ascending order of the horizontal coordinates, and then connecting them sequentially, the constructed linear contour curve is ensured to be accurate, continuous, and smooth. This process not only improves the accuracy of deformation analysis and health assessment, but also enhances the automation and efficiency of the monitoring system, avoids human error, and provides strong data support for subsequent structural deformation analysis, visualization, and health assessment.

[0122] S10: Calculate the vertical global deformation of the reinforced concrete beam according to the linear profile curve and the reference benchmark curve in the historical monitoring process, so as to automatically monitor the global deformation of the reinforced concrete beam.

[0123] In this embodiment of the present invention, a highly accurate, automated deformation monitoring system is provided by comparing a linear profile curve with a reference baseline curve to calculate the vertical global deformation of a reinforced concrete beam. This not only accurately assesses beam deformation and provides early warning of potential structural risks, but also provides reliable technical support for long-term structural health monitoring, maintenance decision-making, and intelligent construction.

[0124] In a possible implementation, the calculation formula for the vertical global deformation is specifically:

[0125] ΔY i =y″ i -y′ i

[0126] Among them, ΔYi Indicates the vertical global deformation of the i-th outer contour point in the upper edge area or the lower edge area, y″ i y′ is the ordinate of the i-th outer contour point in the upper edge area or lower edge area of ​​the current frame binary mask image. i is the ordinate of the i-th point in the upper edge area or lower edge area in the previous frame of the binary mask image.

[0127] Reference Manual Figure 2 , which shows a structural schematic diagram of an automatic monitoring system for the full-area deformation of reinforced concrete beams provided by the present invention.

[0128] The present invention further provides a reinforced concrete beam global deformation automatic monitoring system 20, which is applied to the above-mentioned reinforced concrete beam global deformation automatic monitoring method, comprising:

[0129] Processor 201;

[0130] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the method for automatically monitoring the global deformation of reinforced concrete beams as described in the method embodiment is implemented.

[0131] The reinforced concrete beam global deformation automatic monitoring system 20 provided by the present invention can execute the above-mentioned reinforced concrete beam global deformation automatic monitoring method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate on it.

[0132] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0133] In an embodiment of the present invention, redundant points in the outer contour point set of the beam body are eliminated through the Douglas-Peucker polygon approximation algorithm to obtain a set of candidate corner points of the beam body, and with the goal of minimizing the contour reconstruction error and minimizing the number of beam body corner points, a heuristic optimization algorithm is used to determine multiple target beam body corner points in the candidate corner point set of the beam body. When faced with complex and irregular boundaries, four real structural corner points can be accurately extracted, thereby improving the accuracy of deformation measurement. By extracting all outer contour points in multiple edge areas, a linear contour curve is constructed, and based on the linear contour curve and the reference benchmark curve in the historical monitoring process, the vertical global deformation of the reinforced concrete beam is calculated to automatically monitor the global deformation of the reinforced concrete beam. The accuracy and robustness can meet actual monitoring needs, can adapt to a variety of non-standard contour forms, and can also effectively handle the deformation monitoring task of reinforced concrete beams in complex backgrounds.

[0134] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), but may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0135] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0136] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0137] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0138] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0139] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0140] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0141] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0142] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0143] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0144] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0145] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0146] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for automatically monitoring the global deformation of reinforced concrete beams as described in the method embodiment.

[0147] The computer-readable storage medium provided by the present invention can implement the steps and effects of the method for automatic monitoring of the global deformation of reinforced concrete beams in the above-mentioned method embodiment. To avoid repetition, the present invention will not elaborate on them.

[0148] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0149] In an embodiment of the present invention, redundant points in the outer contour point set of the beam body are eliminated through the Douglas-Peucker polygon approximation algorithm to obtain a set of candidate corner points of the beam body, and with the goal of minimizing the contour reconstruction error and minimizing the number of beam body corner points, a heuristic optimization algorithm is used to determine multiple target beam body corner points in the candidate corner point set of the beam body. When faced with complex and irregular boundaries, four real structural corner points can be accurately extracted, thereby improving the accuracy of deformation measurement. By extracting all outer contour points in multiple edge areas, a linear contour curve is constructed, and based on the linear contour curve and the reference benchmark curve in the historical monitoring process, the vertical global deformation of the reinforced concrete beam is calculated to automatically monitor the global deformation of the reinforced concrete beam. The accuracy and robustness can meet actual monitoring needs, can adapt to a variety of non-standard contour forms, and can also effectively handle the deformation monitoring task of reinforced concrete beams in complex backgrounds.

[0150] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0151] There are a few points to note:

[0152] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0153] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or intervening elements may be present.

[0154] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0155] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for automatically monitoring the global deformation of reinforced concrete beams, characterized in that: include: S1: Acquire the original image of the reinforced concrete beam; S2: inputting the original image into an image segmentation model, and outputting a binary mask image including the reinforced concrete beam; S3: determining the center point of the beam according to the foreground area in the binary mask image; S4: using an edge detection algorithm and a contour extraction algorithm, identifying the edge of the foreground area and extracting a set of outer contour points of the beam body; S5: using the Douglas-Peucker polygon approximation algorithm, eliminating redundant points in the beam body contour point set to obtain a set of candidate beam body corner points; S6: with the goal of minimizing the contour reconstruction error and minimizing the number of beam corner points, a plurality of target beam corner points in the set of candidate beam corner points are determined by a heuristic optimization algorithm; S7: Dividing the binary mask image into a plurality of edge regions by a center radial division method according to the beam center point and each of the target beam corner points; S8: Divide each outer contour point in the beam outer contour point set into a corresponding edge area; S9: extracting all outer contour points in the edge area and constructing a linear contour curve; S10: Calculating the vertical global deformation of the reinforced concrete beam according to the linear profile curve and the reference benchmark curve in the historical monitoring process, so as to automatically monitor the global deformation of the reinforced concrete beam.

2. The method for automatically monitoring the global deformation of reinforced concrete beams according to claim 1, characterized in that: The image segmentation model is specifically: an image segmentation model based on a deep neural network.

3. The method for automatic monitoring of global deformation of reinforced concrete beams according to claim 1, characterized in that: The foreground area is specifically: F={(x j ,y j )|M(x j ,y j )=1,j=1,2,…,M} Among them, F represents the foreground area, x j Indicates the horizontal coordinate of the j-th pixel in the foreground area, y j represents the ordinate of the jth pixel in the foreground area, and M represents the total number of pixels in the foreground area; The beam external contour point set is specifically: C={(x i ,y i )|i=1,2,…,N} Among them, C represents the external contour point set of the beam, x i Indicates the horizontal coordinate of the i-th beam external contour point, y i represents the ordinate of the i-th beam external contour point, and N represents the total number of beam external contour points; The candidate corner point set of the beam body is specifically: P={(x′1,y′1),(x′2,y′2),…,(x′ K ,and' K )},4≤K≤N Where P represents the set of candidate corner points of the beam, x′ k Indicates the horizontal coordinate of the kth candidate corner point of the beam, y′ k Represents the ordinate of the kth candidate corner point of the beam, where k = 1, 2, …, K, and K represents the total number of candidate corner points of the beam.

4. The method for automatically monitoring the global deformation of reinforced concrete beams according to claim 3, characterized in that: The S3 is specifically: Determine the center point of the beam according to the following formula: Among them, x0 represents the horizontal coordinate of the center point of the beam, and y0 represents the vertical coordinate of the center point of the beam.

5. The method for automatic monitoring of global deformation of reinforced concrete beams according to claim 1, characterized in that: The S6 specifically includes: S601: Constructing a dual-objective fitness function with the goal of minimizing the contour reconstruction error and minimizing the number of beam corner points: Fitness=w1·E rec +w2·N pts Among them, Fitness represents the dual-objective fitness function, w1 represents the weight coefficient of the contour reconstruction error, E rec represents the contour reconstruction error, w2 represents the weight coefficient of the number of beam corner points, N pts represents the number of corner points of the beam, l represents the edge, L represents the edge set of the closed quadrilateral formed by the corner points, and dist 2 (x i ,l) represents the vertical distance from the outer contour point of the i-th beam to the edge l; S602: According to the dual-objective fitness function, the heuristic optimization algorithm is used to determine multiple target beam corner points in the set of candidate beam corner points, wherein the target beam corner points include the upper left corner point A of the beam, the upper right corner point B of the beam, the lower right corner point C of the beam, and the lower left corner point D of the beam.

6. The method for automatically monitoring the global deformation of reinforced concrete beams according to claim 5, characterized in that: The edge areas include an upper edge area AOB, a right edge area BOC, a lower edge area COD and a left edge area AOD; The S7 specifically includes: S701: With the center point of the beam as the origin O of the binary mask image, four rays are emitted in sequence toward the upper left corner point A of the beam, the upper right corner point B of the beam, the lower right corner point C of the beam, and the lower left corner point D of the beam to obtain a first ray OA, a second ray OB, a third ray OC, and a fourth ray OD; S702: Using the area between the first ray OA and the second ray OB as the upper edge area AOB; S703: Using the area between the second ray OB and the third ray OC as the right edge area BOC; S704: Using the area between the third ray OC and the fourth ray OD as the lower edge area COD; S705: Using the area between the first ray OA and the fourth ray OD as the left edge area AOD, completing the division of the binary mask image.

7. The method for automatically monitoring the global deformation of reinforced concrete beams according to claim 6, characterized in that: The first ray OA is specifically: Among them, y OA,z represents the ordinate of the zth point on the first ray OA, x A Indicates the horizontal coordinate of the upper left corner point A of the beam, y A represents the vertical coordinate of the upper left corner point A of the beam, x0 represents the horizontal coordinate of the center point of the beam, y0 represents the vertical coordinate of the center point of the beam, and x OA,z represents the horizontal coordinate of the zth point on the first ray OA; The second ray OB is specifically: Among them, y OB,z′ represents the ordinate of the z′th point on the second ray OB, x B Indicates the horizontal coordinate of point B at the upper right corner of the beam, y B Indicates the vertical coordinate of the upper right corner point B of the beam, x OB,z′ represents the abscissa of the z′th point on the second ray OB; The third ray OC is specifically: Among them, y OC,z″ represents the ordinate of the z″th point on the third ray OC, x C Indicates the horizontal coordinate of the lower right corner point C of the beam, y C Indicates the vertical coordinate of the lower right corner point C of the beam, x OC,z″ represents the abscissa of the z″th point on the third ray OC; The fourth ray OD is specifically: Among them, y OD,z″′ represents the ordinate of the z″′th point on the fourth ray OD, x D Indicates the horizontal coordinate of the lower left corner point D of the beam, y D Indicates the vertical coordinate of the lower left corner point D of the beam, x OD,z″′ Represents the abscissa of the z″′th point on the fourth ray OD.

8. The method for automatically monitoring the global deformation of reinforced concrete beams according to claim 6, characterized in that: The S9 specifically includes: S901: Extract all outer contour points in the upper edge area or the lower edge area; S902: Arrange all the extracted outer contour points in ascending order of the horizontal coordinates; S903: Connect all the outer contour points in sequence according to the arrangement order to construct the linear contour curve.

9. The method for automatic monitoring of global deformation of reinforced concrete beams according to claim 1, characterized in that: The calculation formula of the vertical global deformation is specifically: ΔY i =y″ i -y′ i Among them, ΔY i Indicates the vertical global deformation of the i-th outer contour point in the upper edge area or the lower edge area, y″ i y′ is the ordinate of the i-th outer contour point in the upper edge area or lower edge area of ​​the current frame binary mask image. i is the ordinate of the i-th point in the upper edge area or lower edge area in the previous frame of the binary mask image.

10. An automatic monitoring system for global deformation of reinforced concrete beams, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for automatic monitoring of the global deformation of reinforced concrete beams according to any one of claims 1 to 9 is implemented.

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