Concrete vibration quality evaluation method and system based on image analysis

Abstract

The invention relates to the technical field of image data processing, in particular to a concrete vibration quality evaluation method and system based on image analysis. The method comprises the following steps: acquiring a grayscale image of a newly vibrated concrete surface; constructing a region adjacent graph and extracting multi-dimensional features; calculating a physical entropy weight based on the multi-dimensional features; carrying out region merging operation; identifying the bubble clusters, and calculating quality evaluation indexes of the concrete surface according to statistical characteristics of the bubble clusters; and outputting a concrete vibration quality evaluation result according to the quality evaluation index. According to the method, a concrete surface is segmented through a watershed algorithm and a region adjacent graph, multi-dimensional features are extracted to construct a physical entropy weight model to evaluate significance, a boundary gray priority queue and a self-adaptive multi-threshold intelligent merging region are utilized, excessive segmentation is solved, bubble clusters are identified in combination with a Gaussian mixture model and a Bayesian information criterion, and the accuracy of bubble cluster recognition is improved. Therefore, the concrete vibration quality is comprehensively evaluated, and objectivity and accuracy are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image data processing, and in particular to a concrete vibration quality evaluation method and system based on image analysis. BACKGROUND

[0002] In the field of building construction and civil engineering, the quality of concrete vibration is a key factor in determining the strength and safety of the structure. Insufficient vibration can result in a large number of air bubbles remaining in the concrete, reducing the load-bearing capacity of the structure, while excessive vibration can lead to segregation phenomena such as aggregate sinking and paste floating, also damaging the mechanical properties of the concrete. Therefore, accurate evaluation of the surface quality of the concrete after vibration is a necessary step to ensure the quality of the project.

[0003] Currently, the construction site usually uses manual observation methods for evaluation, which is highly subjective and difficult to evaluate effectively. With the development of computer vision technology, automated evaluation based on image analysis has become a trend. However, existing image analysis techniques are ineffective in dealing with the complex surface of concrete, as the microstructure of the concrete surface is complex, containing cement paste matrix, aggregate of different particle sizes, and air bubbles generated by vibration. The gray scale distribution of these components is extremely uneven and the boundaries are blurred, making it difficult to analyze effectively.

[0004] In traditional methods, the standard watershed algorithm can capture the topological structure, but it is extremely sensitive to small gray scale changes on complex surfaces, often misjudging texture fluctuations on the surface of the concrete paste as regional boundaries, resulting in numerous meaningless over-segmented regions in a single image. In this severe over-segmentation case, the real bubble features are overwhelmed by a large number of randomly distributed noise regions. Since there is a lack of models that can effectively measure the essential physical differences between bubbles, aggregate, and noise, existing techniques cannot establish a reliable mapping relationship between surface image features and internal vibration quality, resulting in inaccurate identification and evaluation of bubble parameters, which reduces the reliability of subsequent evaluation of concrete vibration quality. SUMMARY

[0005] To solve the problem of severe over-segmentation, difficulty in distinguishing between bubbles and aggregate noise, and inability to accurately evaluate the quality of concrete vibration when using the standard watershed algorithm for concrete surface image analysis, the present application provides a concrete vibration quality evaluation method and system based on image analysis.

[0006] In a first aspect, the present application provides a concrete vibration quality evaluation method based on image analysis, which adopts the following technical solution:

[0007] A concrete vibration quality evaluation method based on image analysis, comprising: acquiring a gray image of a concrete surface after new vibration; performing a watershed algorithm on the gray image to obtain a label image containing multiple connected regions, constructing a region adjacency graph based on the label image, and extracting a region area, a region texture entropy, and a region gray level difference of each connected region; normalizing the region area, the region texture entropy, and the region gray level difference of each connected region, calculating a physical entropy weight measuring the structural saliency of each connected region based on the normalized region area, the region texture entropy, and the region gray level difference of each connected region; constructing a priority queue based on a boundary minimum gray value of adjacent connected regions in the region adjacency graph, determining a merging termination threshold value by using an adaptive multi-threshold algorithm, merging adjacent connected regions in the order of the priority queue until the boundary minimum gray value exceeds the merging termination threshold value, and obtaining a merged region; extracting a feature vector of each merged region, identifying a bubble cluster by using a Gaussian mixture model and a Bayesian information criterion, and calculating a quality evaluation index of the concrete surface according to statistical characteristics of the bubble cluster, wherein the quality evaluation index at least includes a bubble density, a bubble size uniformity, and a surface compactness; and outputting a concrete vibration quality evaluation result according to the quality evaluation index.

[0008] The present application obtains initial connected regions by a watershed algorithm, realizes preliminary segmentation of a complex concrete surface by combining region adjacency graph construction, constructs an evaluation model of a physical entropy weight by extracting multi-dimensional features such as region area, texture entropy, and gray level difference, effectively measures the structural saliency of each region, realizes intelligent merging of adjacent regions by constructing a priority queue based on a boundary minimum gray value, ensures accurate control of the merging process by an adaptive multi-threshold algorithm, effectively solves the problem of over-segmentation of a traditional watershed algorithm, realizes accurate identification of a bubble cluster by combining a Gaussian mixture model and a Bayesian information criterion, fully utilizes the essential differences in physical characteristics of bubbles, aggregates, and noise, establishes a reliable mapping relationship between surface image features and internal vibration quality, realizes comprehensive evaluation of concrete vibration quality by multi-dimensional quality evaluation indexes such as bubble density, bubble size uniformity, and surface compactness, improves the objectivity and accuracy of concrete surface quality evaluation, and provides reliable technical support for engineering quality control.

[0009] Further, the gray image is a gray image of a concrete surface after smoothing processing by a Gaussian filter.

[0010] Further, the region texture entropy is obtained by: calculating a gray level co-occurrence matrix for each connected region in the label image; compressing the gray level number of the gray level co-occurrence matrix and calculating the average value of the gray level co-occurrence matrix in multiple directions; and calculating the region texture entropy of the connected region based on the average gray level co-occurrence matrix.

[0011] Further, the physical entropy weight satisfies: ; in the formula, is the physical entropy weight of the connected region in the label image, is the region gray difference of the connected region in the label image, is the region area of the connected region in the label image, is the region texture entropy of the connected region in the label image, is a hyperparameter for preventing the denominator from being 0.

[0012] The application realizes scientific evaluation of the physical entropy weight by constructing a ratio of a product of the region gray difference and area to the texture entropy, comprehensively considers the geometric features and texture features of the region, the product of the gray difference and area reflects the structural saliency of the region, and the normalization of the texture entropy ensures the comparability of regions of different complexities, so that the real structure region and the noise region can be effectively distinguished, a reliable weight basis is provided for subsequent region merging and bubble identification, and the accuracy of the concrete surface feature analysis is improved.

[0013] Further, the merging of the adjacent connected regions in the order of the priority queue comprises: applying a Multi-Otsu algorithm to the gray image, dividing the gray of the gray image into dark areas, middle areas and bright areas, and selecting a boundary value between the middle areas and the bright areas as a merging termination threshold; when the minimum gray value of the boundary of the adjacent connected region currently processed in the priority queue is not greater than the merging termination threshold, merging the two adjacent connected regions, the physical entropy weight of the merged region being the average of the physical entropy weights of the two connected regions, and the gray difference of the merged region being the maximum of the gray differences of the two connected regions.

[0014] The application divides the gray image into dark areas, middle areas and bright areas through the Multi-Otsu algorithm, realizes intelligent distinction of different components of the concrete surface, takes the boundary value between the middle areas and the bright areas as the merging termination threshold, ensures that the merging process stops in a reasonable gray range, the merging strategy controls the merging process according to the comparison result of the minimum gray value of the boundary and the threshold, avoids excessive merging, the calculation mode of taking the average of the physical entropy weight and the maximum of the gray difference of the merged region maintains the rationality and representativeness of the features of the merged region, and the accuracy and reliability of the region merging are improved.

[0015] ​​​​Further, the bubble cluster is identified by using the Gaussian mixture model and the Bayesian information criterion, comprising: constructing a feature vector set containing area, texture entropy and gray level difference of the region; clustering the feature vector set by using the Gaussian mixture model to obtain a single cluster model and a double cluster model, and calculating the Bayesian information criterion value of the single cluster model and the Bayesian information criterion value of the double cluster model ; in response to , it is determined that there is a bubble cluster, and the cluster with the maximum sum of gray level differences and the minimum sum of texture entropies of all connected regions in the double cluster model is determined as the bubble cluster; otherwise, it is determined that there is no bubble.

[0016] The present application realizes comprehensive representation of the composition of the concrete surface by constructing a multi-dimensional feature vector containing area, texture entropy and gray level difference, realizes single cluster and double cluster modeling by using the Gaussian mixture model, and determines the optimal model by comparing the Bayesian information criterion; the bubble cluster is selected by the criterion of the maximum sum of gray level differences and the minimum sum of texture entropies, the bubbles, aggregates and noise regions are effectively distinguished, and the accuracy and robustness of bubble identification are improved.

[0017] Further, the bubble size uniformity satisfies: ; in the formula, is the bubble size uniformity of the concrete surface, is the standard deviation of the area of all merged regions in the bubble cluster, is the mean value of the area of all merged regions in the bubble cluster.

[0018] Further, the surface density satisfies: ; in the formula, is the surface density of the concrete surface, is the sum of the areas of all merged regions in the bubble cluster, is the total area of the label image.

[0019] Further, the output concrete vibration quality evaluation result comprises: in response to the surface density being less than a set density threshold or the bubble density being greater than a set density threshold, outputting that the concrete vibration is insufficient; in response to the bubble size uniformity being less than a set uniformity threshold and the bubble density being greater than a set density threshold, outputting that the concrete local vibration is insufficient; and in response to the surface density being not less than a set density threshold and the bubble density being not greater than a set density threshold, outputting that the concrete vibration is good, realizing the evaluation of the concrete vibration quality based on image analysis.

[0020] The present application realizes accurate classification of concrete vibration quality by constructing a multi-index judgment logic based on surface compactness, bubble density and bubble size uniformity, can accurately identify different quality defect types such as insufficient vibration and local insufficient vibration, provides an objective and reliable quality judgment basis for concrete quality control, and improves the accuracy and consistency of quality evaluation.

[0021] In the second aspect, the present application provides a concrete vibration quality evaluation system based on image analysis, which adopts the following technical scheme: A concrete vibration quality evaluation system based on image analysis, comprising a processor and a memory, the memory storing computer program instructions, when the computer program instructions are executed by the processor, the above-mentioned concrete vibration quality evaluation method based on image analysis is realized.

[0022] By adopting the above technical scheme, the above-mentioned concrete vibration quality evaluation method based on image analysis is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is made according to the memory and the processor, and the use is convenient.

[0023] The present application has the following technical effects: (1) For the over-segmentation problem caused by the sensitivity of the traditional watershed algorithm to the small gray scale change of the concrete surface, the physical entropy weight is calculated by extracting the area of the connected region, the region texture entropy and the region gray scale difference, the structural saliency of each connected region is more accurately measured, such as the physical characteristic difference between the bubble region and the cement paste texture, noise, etc., and then a priority queue is constructed based on the minimum gray scale value of the boundary of the adjacent connected region, and the adaptive multi-threshold algorithm is combined to control the merging termination condition, so that the normal texture fluctuation on the surface of the concrete paste is avoided to be misjudged as the region boundary, the meaningless noise segmentation region is effectively merged, and the characteristics of the connected region corresponding to the real bubble are completely preserved, and the problem that the characteristics of the real bubble are submerged by a large number of random noise regions is solved.

[0024] (2) Break through the limitation of the prior art that lacks the essential physical difference model of bubbles, aggregates and noise, based on the feature vector of the merged region, use the Gaussian mixture model and the Bayesian information criterion to realize accurate classification and identification of bubble clusters, evaluate the physical characteristic difference of different regions through the model, such as the gray scale distribution rule of the bubble, the texture entropy range and the essential difference between the aggregate and the noise, avoid misjudging the aggregate particles or random noise as bubbles, and establish a reliable mapping relationship between the concrete surface image features and the internal bubble parameters, so as to provide a more accurate and interference-free data basis for subsequent vibration quality evaluation.

[0025] (3) It replaces the traditional subjective evaluation method of manual observation. By extracting quantitative quality evaluation indicators such as bubble density, bubble size uniformity, and surface compaction, it realizes the automated and standardized evaluation of concrete vibration quality. It does not rely on the experience judgment of operators. The evaluation results have a unified standard, which reduces human subjective error and improves evaluation efficiency. It is suitable for the large-scale and continuous quality control needs of building construction and civil engineering, and avoids the problems of low efficiency and inconsistent results caused by manual evaluation.

[0026] (4) By suppressing over-segmentation and accurately identifying bubble clusters, the quality assessment indicators can be ensured to truly reflect the vibration situation inside the concrete. For example, when vibration is insufficient, the bubble density is high and the size uniformity is poor. When vibration is excessive, the bubble distribution is abnormal and the surface density is not up to standard due to aggregate segregation. Based on the output assessment results, it is possible to accurately determine whether the vibration meets the quality requirements, promptly identify problems of insufficient or excessive vibration, avoid problems such as a large number of residual bubbles and aggregate segregation inside the concrete due to vibration quality defects, and thus ensure the bearing capacity and mechanical properties of the concrete structure, providing key technical support for the strength and safety of the engineering structure. Attached Figure Description

[0027] Figure 1 This is a flowchart of a method for evaluating the quality of concrete vibration based on image analysis, according to an embodiment of the present invention.

[0028] Figure 2 This is an original image of the concrete surface in an image analysis-based concrete vibration quality assessment method according to an embodiment of the present invention.

[0029] Figure 3 This is a labeled image of a concrete surface segmented by a watershed algorithm in an image analysis-based concrete vibration quality assessment method according to an embodiment of the present invention.

[0030] Figure 4 This is a labeled image of the concrete surface after performing a region merging operation in a concrete vibration quality assessment method based on image analysis according to an embodiment of the present invention.

[0031] Figure 5 This is a binary image of the merged region within a bubble cluster after cluster analysis in a concrete vibration quality assessment method based on image analysis according to an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] This invention discloses a method for evaluating the quality of concrete vibration based on image analysis, referring to... Figure 1 This includes steps S001-S006: S001: Obtain a grayscale image of the newly vibrated concrete surface.

[0034] It should be noted that concrete surfaces have complex and multi-scale texture features, including microbubbles, aggregates of different sizes, and cement paste matrix. The initial grayscale image is processed using the standard watershed algorithm to obtain an initial segmentation region containing accurate topological boundaries and local minima, capturing all the microscopic details of the concrete surface and providing complete basic data for subsequent processing.

[0035] Specifically, while ensuring uniform illumination during data collection, such as Figure 2 As shown, an industrial camera is used to capture the original image of the newly vibrated concrete surface, and the original image is converted into an initial grayscale image.

[0036] Apply a Gaussian filter with a standard deviation of 1, such as a Gaussian distribution, to the initial input grayscale image to remove high-frequency noise and obtain a smoothed grayscale image.

[0037] like Figure 3 As shown, the standard watershed algorithm is applied to the initial grayscale image, and the label image is obtained by immersion simulation.

[0038] Assign a unique numeric identifier to each connected region in the labeled image, such as from to This forms a set of connected regions.

[0039] At this point, the label image is segmented into a large number of small regions, preserving the local minimum structure of the surface.

[0040] S002: Construct a region adjacency graph and extract multidimensional features.

[0041] It is important to note that after obtaining the segmented regions, the core issue becomes how to distinguish the different physical components of the concrete surface, including air bubbles, aggregates, and cement paste. This is because different physical components manifest as complex grayscale and texture patterns in the image, making them impossible to identify using a single feature. This step constructs a region adjacency map and extracts multi-dimensional features to establish a mapping relationship with physical properties. Air bubbles are represented by regions with large grayscale differences and uniform textures, while aggregates are represented by complex textures and gradual grayscale differences. Multi-dimensional feature fusion provides a basis for calculating physical entropy weights, thus solving the problem that a single feature cannot distinguish complex surface components.

[0042] Specifically, create a region adjacency graph. ,in, For a set of nodes, Let it be the set of edges.

[0043] In the label image, create a corresponding node for each connected region. If two connected components are adjacent on the label image, then at the corresponding nodes of the two connected components... and Add edges between .

[0044] Calculate the area of ​​each connected region, which is equal to the total number of pixels within the connected region. Then, find the minimum gray value inside the connected region that is not on the region boundary. .

[0045] Calculate the region texture entropy for each connected region. On the grayscale image, calculate the gray-level co-occurrence matrix (GLCM) for each connected region. The parameters of the GLCM are set as follows: gray levels compressed to 32 levels, spatial distance... The average GLCM in four directions (0°, 45°, 90°, and 135°) is calculated, and then the regional texture entropy is obtained by calculating the Shannon entropy in information theory. The reason for using GLCM to calculate texture entropy is that GLCM can accurately assess the texture complexity of different regions of the concrete surface. For example, low entropy indicates that the inside of the bubble is uniform, while high entropy indicates that the aggregate surface is rough. As a second-order statistical method, GLCM captures the spatial relationship between pixels, so that the subsequent physical entropy weight can effectively distinguish between real features and noise, while also possessing robustness and computational efficiency.

[0046] Calculate the grayscale difference for each connected region. Since air bubbles on the concrete surface typically appear as recessed areas with large grayscale differences, the regional grayscale difference is key to distinguishing air bubbles from ordinary textures; traverse the connected regions. All edges Find connected components Minimum gray value on the shared boundary The minimum gray value at the boundary represents the gray-level barrier height between two connected regions. The gray-level difference between regions is obtained as follows: .

[0047] S003: Calculate physical entropy weights based on multidimensional features.

[0048] It should be noted that the oversegmentation results contain a mixture of real physical features and random noise. The physical entropy weight is designed in this step because it comprehensively considers the gray-level saliency and internal texture order of the connected region, and can measure the structural saliency of the region. Real bubbles have the characteristics of large gray-level difference, large area and low entropy, so they are given high weights, while noise regions have small areas and complex textures, so they are given low weights. This allows the subsequent merging process to retain real physical features and eliminate irrelevant details, thereby solving the oversegmentation problem.

[0049] Specifically, the multidimensional features are normalized by dividing the region gray-level difference, region area, and region texture entropy of each connected region by the maximum value among the corresponding dimensional features of all connected regions, thus obtaining the normalized features. .

[0050] If the area of ​​a connected region is less than 3 pixels, the connected region is considered as minimal noise, and its physical entropy weight is recorded as 0. Otherwise, the physical entropy weight is calculated, and the physical entropy weight satisfies the following: ; In the formula, Connected regions in the label image Physical entropy weight, Connected regions in the label image The regional grayscale difference Connected regions in the label image The area of ​​the region, Connected regions in the label image The region texture entropy, For example, to prevent hyperparameters with a denominator of 0, .

[0051] in, The grayscale terrain capacity refers to the theoretical amount of water required to fill a connected region in a grayscale terrain model. This index is derived from the terrain flooding theory of the watershed algorithm (Meyer, 1994) and can effectively assess the salience of a connected region as an independent physical structure. The larger the value, the more salience the region is and the more likely it is to be the target bubble. This introduces a texture penalty. The more complex the texture, such as the aggregate region, the larger the value and the lower the physical entropy weight. By calculating the physical entropy weight, connected regions with high physical entropy weights are marked as salient structures, while connected regions with low physical entropy weights are marked as background or noise to be merged.

[0052] S004: Perform a region merge operation.

[0053] It should be noted that the core challenge of image segmentation is to eliminate oversegmentation while preserving the true physical features and avoiding the erroneous merging of bubble regions and aggregate regions. This step adopts a hierarchical merging strategy based on physical entropy weights because this strategy follows the principle of gray-scale topography: connected regions with high physical entropy weights and true bubble features can absorb connected regions with low physical entropy weights caused by noise. The adaptive threshold dynamically determines the natural boundaries of different materials, and the pre-termination condition ensures that only naturally connected regions on the gray-scale topography are merged. Merging is terminated when the boundary height first exceeds the threshold, stopping precisely when it connects bubble regions but does not cross into aggregate regions. This preserves the true distribution structure of concrete surface features and lays an accurate foundation for quality assessment.

[0054] Specifically, construct a priority queue. Arranging all edges in ascending order of their minimum grayscale value means that the region with the lowest grayscale barrier will be processed first.

[0055] Determine the merging threshold and apply it to the smoothed image. The Multi-Otsu algorithm is used to obtain the dark / middle boundary threshold. And mid / brightness threshold ,because This corresponds to the boundary between the background matrix and significant protrusions. Exceeding this value indicates crossing into the aggregate region, and merging should be stopped. Therefore, a merging termination threshold is set. .

[0056] Perform iterative region merging operations, such as Figure 4 As shown, from Take out the edges in order If the side corresponding If the condition is met, the merging process will terminate; otherwise, the connected regions will be merged. Connected regions The physical entropy weight of the merged region is the average of the physical entropy weights of the two connected regions, and the grayscale difference of the merged region is the maximum grayscale difference between the two connected regions. The method for obtaining the region area and region texture entropy of the merged region is the same as the method for calculating the region area and region texture entropy of the connected regions, and will not be repeated here.

[0057] For a given connected region, if it has the same minimum gray value across multiple contributing boundaries, then they are merged in the order in which each connected region in the label image is assigned a unique numerical identifier.

[0058] For the merged region, all adjacency relationships of the two connected regions before the merger are transferred to the merged region. When the boundaries of the two connected regions before the merger are involved in subsequent processes, they are treated as edges of the merged region.

[0059] S005: Identify bubble clusters and calculate the quality assessment index of the concrete surface based on the statistical characteristics of the bubble clusters.

[0060] It should be noted that traditional vibration quality assessment relies on a single indicator or linear judgment process, which cannot identify multiple simultaneous defect types, such as insufficient vibration and aggregate segregation, leading to potential quality risks. This step involves multi-dimensional diagnosis because concrete vibration quality manifests on the surface as multiple independent yet interrelated physical characteristics: insufficient vibration is characterized by high air bubble density and low surface compaction; excessive vibration is characterized by uneven aggregate distribution. By setting independent status markers for parallel evaluation, multiple defect states can be detected simultaneously, especially identifying compound defects. This overcomes the limitations of traditional linear judgment, provides construction personnel with process improvement suggestions, and achieves a mapping from surface characteristics to internal quality.

[0061] Specifically, the feature vector of the merged region is extracted. The feature vector includes the region area, region texture entropy, and region grayscale difference after Z-score normalization.

[0062] Gaussian mixture models were used to fit a monocluster model assuming no bubbles and a bicluster model assuming bubbles and background, respectively, and the Bayesian information criterion value was calculated. and According to Raftery's (1995) Bayesian information criterion, the strength of evidence between models is defined by the |ΔBIC| value: when Time is considered weak evidence, indicating that the model differences are not significant. Provide positive evidence to support the superior model. This constitutes strong evidence, and This represents very strong evidence and is the standard for judging whether a bicluster model is superior to a monocluster model in engineering practice. Therefore, if If no cluster meets the criteria of high grayscale difference and low texture entropy, then the cluster with the highest grayscale difference and lowest texture entropy is selected as the bubble cluster. If no cluster meets the criteria of high grayscale difference and lowest texture entropy, then the average physical entropy weights of all regions of the two clusters are compared, and the cluster with the larger average physical entropy weight is selected as the bubble cluster. The number and total area of ​​the merged regions within the bubble cluster are then calculated. Otherwise, no bubbles are detected, and the output "Concrete vibration quality is good" is output. Figure 5As shown, the Bayesian information criterion is used to strictly compare single and double cluster models to ensure that bubbles are identified only when the data distribution significantly exhibits two types of characteristics, thus avoiding misjudging surface texture noise as defects. At the same time, based on the physical characteristics of bubbles, the real bubble areas are accurately screened, providing a reliable basis for subsequent vibration quality diagnosis. This solves the problems of strong subjectivity and susceptibility to interference from complex surface textures in traditional methods.

[0063] Calculate quality assessment indicators for concrete surfaces, including: bubble density, bubble size uniformity, and surface compactness.

[0064] The bubble density is the ratio of the number of merged regions within a bubble cluster to its total area.

[0065] The bubble size uniformity satisfies: ; In the formula, To ensure uniformity of bubble size on the concrete surface, The standard deviation of the area of ​​all merged regions in the bubble cluster. This represents the average area of ​​all merged regions within the bubble cluster. The closer the value is to 1, the more uniform the bubble size and the better the process.

[0066] The surface density satisfies: ; In the formula, The surface density of the concrete surface. This is the sum of the areas of all merged regions within the bubble cluster. The total area of ​​the label image. The closer to 1, the smaller the proportion of bubbles and the denser the surface.

[0067] S006: Output the concrete vibration quality assessment results based on the aforementioned quality assessment indicators.

[0068] Specifically, in response to the surface compaction being less than a set compaction threshold or the bubble density being greater than a set density threshold, the system outputs that the concrete is not sufficiently vibrated. When the bubble size uniformity is less than a set uniformity threshold and the bubble density is greater than a set density threshold, the system outputs that the concrete is locally under-vibrated. When the surface compaction is not less than a set compaction threshold and the bubble density is not greater than a set density threshold, the system outputs that the concrete is well compacted, thus achieving an image analysis-based assessment of the concrete compaction quality.

[0069] The compaction threshold, density threshold, and uniformity threshold are all obtained through image calibration of the concrete surface under standard vibration conditions. The core logic behind not including the comparison of bubble size uniformity with the corresponding threshold in the criteria for good vibration quality is as follows: when the bubble density is not greater than the set threshold, it indicates that the number of bubbles on the concrete surface is at an extremely low level. At this point, regardless of whether the bubble size is uniform, its negative impact on the overall quality of the concrete is already constrained by the dual conditions of meeting the surface compaction standard and the bubble density compliance. Even if a small number of bubbles have size differences, their sparse distribution and extremely low volume proportion will not have a substantial impact on the core mechanical and durability properties of the concrete, such as structural integrity, load-bearing strength, impermeability, and frost resistance, and are insufficient to constitute a quality hazard. Therefore, provided that both surface compaction and bubble density meet the set requirements, there is no need to additionally use bubble size uniformity as a criterion for good vibration.

[0070] This invention also discloses an image analysis-based concrete vibration quality assessment system, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an image analysis-based concrete vibration quality assessment method according to the present invention.

[0071] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0072] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for evaluating the quality of concrete vibration based on image analysis, characterized by, The method comprises the following steps: acquiring a gray image of a new vibrated concrete surface; performing a watershed algorithm on the gray image to obtain a label image containing a plurality of connected regions, constructing a region adjacency graph based on the label image, and extracting a region area, a region texture entropy, and a region gray level difference of each connected region; normalizing the region area, the region texture entropy, and the region gray level difference of each connected region, and calculating a physical entropy weight measuring structural saliency of each connected region based on the normalized region area, the normalized region texture entropy, and the normalized region gray level difference of each connected region; constructing a priority queue based on a boundary minimum gray value of adjacent connected regions in the region adjacency graph, determining a merging termination threshold value by using an adaptive multi-threshold algorithm, and merging adjacent connected regions in the order of the priority queue until the boundary minimum gray value exceeds the merging termination threshold value, to obtain merged regions; extracting a feature vector of each merged region, identifying a bubble cluster by using a Gaussian mixture model and a Bayesian information criterion, and calculating a quality evaluation index of the concrete surface according to statistical characteristics of the bubble cluster, wherein the quality evaluation index at least includes a bubble density, a bubble size uniformity, and a surface compactness; outputting a concrete vibration quality evaluation result according to the quality evaluation index.

2. The method for evaluating the quality of concrete vibration based on image analysis according to claim 1, characterized in that, The gray image is a gray image of the concrete surface after smoothing processing by a Gaussian filter.

3. The method for evaluating the quality of concrete vibration based on image analysis according to claim 1, characterized in that, The region texture entropy is obtained in the following manner: for each connected region in the label image, calculating a gray level co-occurrence matrix; compressing the gray level number of the gray level co-occurrence matrix and calculating an average value of the gray level co-occurrence matrix in multiple directions; calculating the region texture entropy of the connected region based on the average gray level co-occurrence matrix.

4. The method for evaluating the quality of concrete vibration based on image analysis according to claim 1, characterized in that, The physical entropy weight satisfies: ； In the formula, Connected regions in the label image Physical entropy weight, Connected regions in the label image The regional grayscale difference Connected regions in the label image The area of ​​the region, Connected regions in the label image Regional texture entropy, This is a hyperparameter used to prevent the denominator from being zero.

5. The method for evaluating the quality of concrete vibration based on image analysis according to claim 1, characterized in that, the merging of adjacent connected regions in the order of the priority queue comprises: applying a Multi-Otsu algorithm to the gray image to divide the gray level of the gray image into a dark region, a middle region, and a bright region, and selecting a boundary value between the middle region and the bright region as the merging termination threshold value; when the boundary minimum gray value of the adjacent connected regions currently processed in the priority queue is not greater than the merging termination threshold value, merging the adjacent two connected regions, wherein a physical entropy weight of the merged region is an average value of the physical entropy weights of the two connected regions, and a gray level difference of the merged region is a maximum value of the gray level differences of the two connected regions.

6. The method for evaluating the quality of concrete vibration based on image analysis according to claim 1, characterized in that, The identification of the bubble cluster by using the Gaussian mixture model and the Bayesian information criterion comprises: constructing a feature vector set containing the region area, the region texture entropy, and the region gray level difference; Clustering the set of feature vectors using a Gaussian mixture model to obtain a single cluster model and a double cluster model, and calculating a Bayesian information criterion value for the single cluster model and a Bayesian information criterion value for the double cluster model ; in response to If so, it is determined that there is a bubble cluster, and the cluster with the maximum sum of gray level difference and the minimum sum of texture entropy of all connected regions in the double-cluster model is determined as the bubble cluster; otherwise, it is determined that there is no bubble.

7. The method for evaluating the quality of concrete vibration based on image analysis according to claim 1, characterized in that, the bubble size uniformity satisfies: ； wherein is the uniformity of the bubble size of the concrete surface, is the standard deviation of the area of all merged regions in the bubble cluster, is the mean of the area of all merged regions in the bubble cluster.

8. The method for evaluating the quality of concrete vibration based on image analysis according to claim 1, characterized in that, the surface compactness satisfies: ； wherein is the surface density of the concrete surface, is the sum of the area of all merged regions in the cluster of bubbles, is the total area of the label image.

9. The method for evaluating the quality of concrete vibration based on image analysis according to claim 1, characterized in that, the output of the concrete vibration quality evaluation result comprises: outputting that the concrete vibration is insufficient in response to the surface compactness being less than a set compactness threshold value or the bubble density being greater than a set density threshold value; outputting that the concrete local vibration is insufficient in response to the bubble size uniformity being less than a set uniformity threshold value and the bubble density being greater than a set density threshold value. In response to the surface compactness being not less than a set compactness threshold and the bubble density being not greater than a set density threshold, outputting that the concrete is well vibrated, and realizing the evaluation of the concrete vibration quality based on the image analysis.

10. An image analysis-based concrete vibration quality evaluation system, characterized by, The method comprises the steps of: A processor and a memory, the memory storing computer program instructions which, when executed by the processor, implement a method for evaluating the quality of concrete vibration based on image analysis according to any one of claims 1-9.

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