Water conservancy project concrete quality detection method and system based on image recognition technology

Abstract

This invention relates to a method and system for quality inspection of concrete in hydraulic engineering projects based on image recognition technology. By integrating image sequence analysis and ultrasonic detection technology, it achieves real-time and continuous quality monitoring of concrete in hydraulic engineering projects, effectively solving the inherent defects of traditional periodic inspection methods. By comparing the differences between current and historical images, ultrasonic detection is automatically triggered to accurately extract visual and ultrasonic roughness features. Combined with a preset fitting model, visual roughness and actual roughness are calculated, enabling early warning of seepage risks. Simultaneously, a dynamic comparison mechanism of crack feature areas can track crack propagation in real time, avoiding the missed detection of sudden hidden dangers. This reduces the response time to potential hazards from several weeks to real-time, providing a scientific basis for preventing engineering accidents and optimizing maintenance decisions. It not only ensures the structural safety and durability of hydraulic engineering projects but also reduces the cost of periodic inspections, demonstrating significant engineering practical value and promotion potential.

Description

Technical Field

[0001] This invention relates to the field of visual processing technology, specifically to a method and system for detecting the quality of concrete in water conservancy projects based on image recognition technology. Background Technology

[0002] Concrete testing is a crucial step in ensuring the structural safety and durability of building projects. Its importance lies in the fact that concrete, as a core component of modern architecture, directly affects the safety, service life, and overall stability of buildings. Scientific and systematic testing can promptly detect potential problems in concrete, such as cracks, insufficient strength, and steel corrosion, ensuring that the performance of engineering materials meets design standards and providing a reliable guarantee for the long-term safe operation of buildings. At the same time, it provides a scientific basis for construction quality control, structural assessment, and accident handling. It is an indispensable professional technical means to prevent engineering quality problems, avoid safety hazards, and extend the service life of buildings.

[0003] Existing concrete testing technologies in the field of water conservancy engineering, such as core drilling, ultrasonic testing, and rebound testing, are all periodic testing methods. However, during the long-term use of concrete, sudden problems such as dangerous cracks and water seepage may occur. These technologies are difficult to detect hidden dangers in a timely manner due to their long testing cycle, limited coverage, and inability to achieve continuous monitoring. Therefore, it is urgent to introduce vision-based real-time detection technology to make up for the shortcomings of periodic testing and achieve all-weather, all-round dynamic monitoring of the condition of concrete structures. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method and system for detecting the quality of concrete in water conservancy projects based on image recognition technology, so as to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The present invention provides a method for detecting the quality of concrete in hydraulic engineering based on image recognition technology, comprising the following steps: An image sequence of the concrete area to be inspected in a hydraulic engineering project is acquired, wherein the image sequence is acquired based on a pre-set image acquisition device; Extract the original image at the current time point and the original image at the previous time point from the image sequence, and calculate the difference between the original image at the current time point and the original image at the previous time point; When the difference is greater than a preset difference threshold, the ultrasonic detection device is controlled to emit an ultrasonic signal toward the area to be detected and an echo signal is obtained. Visual roughness features and crack feature regions are extracted from the original image at the current time point, and ultrasonic roughness features are extracted from the echo signal; Visual roughness is calculated based on a pre-built first fitting model and the visual roughness feature, and ultrasonic smoothness is calculated based on a pre-built second fitting model and the ultrasonic roughness feature, wherein the first fitting model characterizes the relationship between visual roughness feature and roughness, and the second fitting model characterizes the relationship between ultrasonic roughness feature and roughness. The visual roughness is compared with the ultrasonic smoothness to obtain a roughness comparison result, and the water seepage situation is determined based on the roughness comparison result; and the crack feature area is compared with the crack feature reference area of ​​the previous detection cycle to obtain a crack comparison result. The concrete quality of water conservancy projects is monitored in real time based on the comparison results of the water seepage and cracks.

[0006] In one embodiment of this application, calculating the difference between the original image at the current time point and the original image at the previous time point includes: The original image at the current time point The original image from the previous time point Preprocessing is performed separately to obtain the first preprocessed image. Second preprocessed image The preprocessing includes contrast enhancement, grayscale conversion, and filtering. Calculate the first preprocessed image Second preprocessed image Differences , wherein the difference The mathematical expression is:

[0007] In the formula, Indicates the image width. Indicates the image height. This indicates that the coordinates in the first preprocessed image are... The pixel value of the pixel, This indicates that the coordinates in the second preprocessed image are... The pixel value of the pixel.

[0008] In one embodiment of this application, extracting visual coarse features from the original image at the current time point includes: The original image at the current time point is preprocessed to obtain a first preprocessed image. ; Calculate the first preprocessed image Gray-level co-occurrence matrix ; Based on the gray-level co-occurrence matrix Extract the first preprocessed image Contrast ,energy ,entropy and homogeneity characteristics This yields visually coarse features.

[0009] In one embodiment of this application, extracting the crack feature region from the original image at the current time point includes: The original image at the current time point is preprocessed to obtain a first preprocessed image. ; Calculate the first preprocessed image Each pixel Hessian matrix ; According to the Hessian matrix Calculate pixels First eigenvalue Second eigenvalue ; Based on the first feature value of the pixel Second eigenvalue Calculate the degree of anisotropy Wherein, the anisotropy degree The mathematical expression is:

[0010] Will satisfy: The pixels are used as crack pixels, where, Indicates the heterogeneity threshold; Density clustering is performed on the crack pixels to obtain multiple pixel clusters, and pixel clusters with a number of pixels greater than or equal to a preset number threshold are taken as crack clusters; and crack regions are constructed based on the crack clusters.

[0011] In one embodiment of this application, the ultrasonic roughness features include echo peak amplitude, spectral center frequency, and attenuation coefficient, wherein extracting the ultrasonic roughness features from the echo signal includes: The echo signal is preprocessed to obtain a preprocessed echo signal, wherein the preprocessing includes filtering, amplification and gain compensation; The time window for surface reflected waves is extracted from the preprocessed echo signal, and the absolute maximum value of the signal is extracted from the time window to obtain the echo peak amplitude. ; Extract the spectral center frequency from the preprocessed echo signal ; Extract the echo envelope of the preprocessed echo signal. Construct a decay model within a selected time window, wherein the mathematical expression of the decay model is:

[0012] In the formula, Indicates the initial amplitude. Indicates the attenuation coefficient. Indicates the time of transmission; Sampling is performed within the selected time window to obtain multiple sampling time points and their envelope amplitude values. The attenuation model is then linearly fitted based on these multiple sampling time points and their envelope amplitude values ​​to obtain the attenuation coefficient. .

[0013] In one embodiment of this application, the method for constructing the first fitting model or the second fitting model includes: Acquire multiple concrete surface image samples and ultrasonic echo signal samples of the concrete surface; Visual roughness feature samples are extracted from the concrete surface image samples, and ultrasonic roughness feature samples are extracted from the ultrasonic echo signal samples, wherein the visual roughness feature samples include contrast samples. Energy sample Entropy samples and homogeneous feature samples The ultrasonic roughness feature sample includes echo peak amplitude samples. Spectrum center frequency sample and attenuation coefficient samples ,in, For concrete surface image sample index, For the ultrasonic echo signal sample index; The ultrasonic coarse feature samples and the visual coarse feature samples are normalized respectively to obtain normalized ultrasonic coarse feature samples and normalized visual coarse feature samples. Construct a first multivariate linear relationship and a second multivariate linear relationship, wherein the mathematical expression of the first multivariate linear relationship is:

[0014] The mathematical expression for the second multivariate linear relationship is:

[0015] In the formula, Indicates the visual roughness of the annotation. This indicates the labeled ultrasonic smoothness. and Both represent intercepts. Represents the contrast coefficient. Indicates normalized contrast. Represents the coefficient of the energy term. Represents normalized energy. Represents the coefficient of the entropy term. Represents the normalized entropy. Represents the coefficient of the homogeneity characteristic term. Indicates normalized homogeneity characteristics. This represents the coefficient of the peak echo amplitude term. Indicates the normalized echo peak amplitude. Represents the coefficient of the center frequency term in the spectrum. Indicates the center frequency of the normalized spectrum. This represents the coefficient of the attenuation coefficient term. Represents the normalized attenuation coefficient. and All are error terms; Substituting the normalized visual roughness feature samples into the first multivariate linear relationship and combining it with the least squares method for fitting, a first fitting model is obtained; and substituting the normalized ultrasonic roughness feature samples into the second multivariate linear relationship and combining it with the least squares method for fitting, a second fitting model is obtained.

[0016] In one embodiment of this application, visual roughness is calculated based on a pre-built first fitting model and the visual roughness feature, and ultrasonic smoothness is calculated based on a pre-built second fitting model and the ultrasonic roughness feature, including: The visual roughness features are normalized to obtain normalized visual roughness features; and the ultrasonic roughness features are normalized to obtain normalized ultrasonic roughness features. Substituting the normalized visual roughness features into the first fitting model, we obtain the visual roughness. The normalized ultrasonic roughness features are then substituted into the second fitting model to obtain the ultrasonic smoothness. .

[0017] In one embodiment of this application, the visual roughness is compared with the ultrasonic smoothness to obtain a roughness comparison result, and the water seepage situation is determined based on the roughness comparison result, including: Calculate the visual roughness With the aforementioned ultrasonic smoothness Difference rate , wherein the difference rate The mathematical expression is:

[0018] The difference rate If the difference exceeds the preset threshold, it is determined that there is water seepage on the concrete surface.

[0019] In one embodiment of this application, real-time monitoring of the concrete quality of a hydraulic engineering project is performed based on a comparison of the water seepage and the cracks, including: When water seeps into the concrete surface, and the area of ​​the cracked area in the current inspection cycle is larger than the area of ​​the cracked area in the previous inspection cycle, an alarm message will be sent to the target object.

[0020] This application also provides a concrete quality inspection system for hydraulic engineering based on image recognition technology, including: The acquisition module is used to acquire an image sequence of the concrete area to be inspected in a hydraulic engineering project, wherein the image sequence is acquired based on a pre-set image acquisition device; The difference calculation module is used to extract the original image at the current time point and the original image at the previous time point from the image sequence, and to calculate the difference between the original image at the current time point and the original image at the previous time point; An ultrasonic detection module is used to control an ultrasonic detection device to emit ultrasonic signals toward the area to be detected and obtain echo signals when the difference is greater than a preset difference threshold. The feature extraction module is used to extract visual roughness features and crack feature regions from the original image at the current time point, and to extract ultrasonic roughness features from the echo signal; The roughness calculation module is used to calculate visual roughness based on a pre-built first fitting model and the visual roughness feature, and to calculate ultrasonic smoothness based on a pre-built second fitting model and the ultrasonic roughness feature, wherein the first fitting model characterizes the relationship between visual roughness feature and roughness, and the second fitting model characterizes the relationship between ultrasonic roughness feature and roughness. The comparison module is used to compare the visual roughness with the ultrasonic smoothness to obtain a roughness comparison result, and to determine the water seepage situation based on the roughness comparison result; and to compare the crack feature area with the crack feature reference area of ​​the previous detection cycle to obtain a crack comparison result. The detection module is used to perform real-time detection of the concrete quality of water conservancy projects based on the comparison results of the water seepage and the cracks.

[0021] The beneficial effects of this invention are as follows: The method and system for quality inspection of concrete in water conservancy projects based on image recognition technology, by integrating image sequence analysis and ultrasonic detection technology, achieves real-time and continuous quality monitoring of concrete in water conservancy projects, effectively solving the inherent defects of traditional periodic inspection methods. By comparing the differences between current and historical images, ultrasonic detection is automatically triggered to accurately extract visual roughness features and ultrasonic roughness features. Combined with a preset fitting model, visual roughness and actual roughness are calculated to achieve early warning of seepage risks. At the same time, the dynamic comparison mechanism of crack feature areas can track the crack expansion status in real time, avoiding the omission of sudden hidden dangers (such as dangerous cracks and seepage). This technology significantly shortens the inspection cycle, reducing the response time of hidden dangers from several weeks to the real-time level, providing a scientific basis for preventing engineering accidents and optimizing maintenance decisions. It not only ensures the structural safety and durability of water conservancy projects but also reduces the cost of periodic inspections, demonstrating significant engineering practical value and promotion potential. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is an illustration of an application scenario of a concrete quality inspection method for water conservancy projects based on image recognition technology, as shown in one embodiment of this application. Figure 2 This is a flowchart illustrating a concrete quality inspection method for hydraulic engineering based on image recognition technology in one embodiment of this application; Figure 3 This is a schematic diagram of the data processing flow in one embodiment of this application; Figure 4 This is a structural diagram of a concrete quality inspection system for water conservancy projects based on image recognition technology, as shown in one embodiment of this application. Figure 5 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0023] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0024] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the layers related to the present invention and are not drawn according to the actual number, shape and size ratio of the layers in the actual implementation. In the actual implementation, the form and number of each layer can be arbitrarily changed, and the layer layout may also be more complex.

[0025] Numerous details are explored in the following description to provide a more thorough explanation of embodiments of the invention; however, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details.

[0026] Figure 1 This is an illustration of an application scenario of a concrete quality inspection method for hydraulic engineering based on image recognition technology, as shown in one embodiment of this application. Figure 1 As shown, this application relates to the detection of concrete facades or slopes. The detection system includes an edge controller 110, a camera 120, an ultrasonic testing device 130, and a remote server 140. The edge controller 110, camera 120, and ultrasonic testing device 130 are deployed at the detection site. The edge controller 110 controls the camera 120 to periodically acquire images of the concrete surface, forming an image sequence. Using a simple comparison algorithm, the edge controller 110 extracts dynamic frames showing changes. These dynamic frames are then sent to the remote server 140 via the network. The remote server 140 analyzes the dynamic frames to determine features such as the presence of water seepage and the spread of cracks. If an alarm is triggered, the alarm information is sent to the management personnel 150.

[0027] The aforementioned edge monitoring system can be installed in key monitoring areas where existing cracks exist, to promptly monitor for further spread of cracks and water seepage.

[0028] Figure 2 This is a flowchart illustrating a concrete quality inspection method for hydraulic engineering based on image recognition technology, as shown in one embodiment of this application. Figure 2 The concrete quality inspection method for hydraulic engineering based on image recognition technology in this embodiment may include steps S210 to S270: S210, acquire an image sequence of the concrete area to be inspected in the hydraulic engineering project, wherein the image sequence is acquired based on a pre-set image acquisition device; S220, extract the original image at the current time point and the original image at the previous time point from the image sequence, and calculate the difference between the original image at the current time point and the original image at the previous time point; S230, when the difference is greater than a preset difference threshold, control the ultrasonic detection device to emit an ultrasonic signal to the area to be detected and obtain an echo signal; S240, extract visual roughness features and crack feature regions from the original image at the current time point, and extract ultrasonic roughness features from the echo signal; S250, calculate visual roughness based on a pre-built first fitting model and the visual roughness feature, and calculate ultrasonic smoothness based on a pre-built second fitting model and the ultrasonic roughness feature, wherein the first fitting model characterizes the relationship between visual roughness feature and roughness, and the second fitting model characterizes the relationship between ultrasonic roughness feature and roughness. S260, compare the visual roughness with the ultrasonic smoothness to obtain a roughness comparison result, and determine the water seepage situation based on the roughness comparison result; and compare the crack feature area with the crack feature reference area of ​​the previous detection cycle to obtain a crack comparison result; S270, Real-time monitoring of concrete quality in water conservancy projects based on the comparison results of water seepage and cracks.

[0029] Figure 3 This is a schematic diagram of the data processing flow in one embodiment of this application. The following will combine... Figure 3 Each step of the above scheme is explained, specifically including: S210, acquire an image sequence of the concrete area to be inspected in the hydraulic engineering project, wherein the image sequence is acquired based on a pre-set image acquisition device; Among them, image sequence Multiple frames of images acquired at regular intervals constitute.

[0030] S220, extract the original image at the current time point and the original image at the previous time point from the image sequence, and calculate the difference between the original image at the current time point and the original image at the previous time point, specifically including: S221, the original image at the current time point. The original image from the previous time point Preprocessing is performed separately to obtain the first preprocessed image. Second preprocessed image The preprocessing includes contrast enhancement, grayscale conversion, and filtering. Contrast enhancement can improve the visibility of details in an image; grayscale conversion can convert a color image to a grayscale image, reducing computational complexity; filtering can remove image noise and smooth the image.

[0031] S222, Calculate the first preprocessed image Second preprocessed image Differences , wherein the difference The mathematical expression is:

[0032] In the formula, Indicates the image width. Indicates the image height. This indicates that the coordinates in the first preprocessed image are... The pixel value of the pixel, This indicates that the coordinates in the second preprocessed image are... The pixel value of the pixel.

[0033] It can accurately quantify the degree of change of concrete surface between two time points and effectively detect minute changes in concrete surface (such as crack propagation, surface spalling, etc.).

[0034] S230, when the difference is greater than a preset difference threshold, control the ultrasonic detection device to emit an ultrasonic signal to the area to be detected and obtain an echo signal; When image differences When the threshold is exceeded, it indicates that a significant change may have occurred on the concrete surface. At this point, the ultrasonic detection device is controlled to emit ultrasonic signals towards the area to be detected and receive the echo signals. This enables intelligent detection triggered on demand, avoiding unnecessary ultrasonic detection and saving equipment resources.

[0035] S240, extract visual roughness features and crack feature regions from the original image at the current time point, and extract ultrasonic roughness features from the echo signal. The specific extraction process is as follows: (1) Visual coarseness features Visual roughness features refer to the assessment of the roughness of a concrete surface from a visual perspective. In the case of water seepage, visual roughness features are minimally affected by avoiding water stains or water flow. The extraction process is as follows: (1-1) Preprocess the original image at the current time point to obtain the first preprocessed image. ; The preprocessing process includes grayscale conversion, filtering, and grayscale compression (compressing the grayscale level from 255 to 16 or 32).

[0036] (1-2) Calculate the first preprocessed image Gray-level co-occurrence matrix ; The gray-level co-occurrence matrix (GLCM) is a core tool for describing the second-order statistical properties of image texture. It statistically analyzes a pair of gray values ​​under specific spatial relationships (distance d, direction θ, such as 0°, 45°, 90°, 135°). The probability of co-occurrence in an image. For rough surfaces, the gray-level changes between pixels are drastic and irregular, which will affect the gray-level co-occurrence matrix. This is reflected in the text.

[0037] (1-3) Based on the gray-level co-occurrence matrix Extract the first preprocessed image Contrast ,energy ,entropy and homogeneity characteristics This yields visually coarse features.

[0038] Contrast It measures the intensity of local grayscale changes in an image. The rougher the concrete surface, the stronger the contrast between shadows and highlights produced by lighting, the greater the grayscale difference between pixels and the higher the probability of it occurring, thus resulting in a higher contrast value.

[0039] energy It reflects the uniformity or regularity of image texture. When When the element distribution in a pixel is concentrated near the main diagonal (i.e., pixels with similar grayscale values), the energy value is high. The smoother and flatter the concrete surface, and the more uniform the texture, the higher the energy value. Conversely, the rougher the surface, the more dispersed the element distribution, and the lower the energy value.

[0040] entropy It measures the complexity or randomness of image texture. The more "chaotic" the probability distribution in a surface, the greater its entropy. The rougher the concrete surface, the more cracks it has, the more severe the spalling, and the more complex and disordered its texture information, the higher its entropy. Smooth surfaces have lower entropy.

[0041] Homogeneity characteristics This measure assesses the consistency of local grayscale values ​​within a texture. It assigns higher weight to elements on the main diagonal (pixel pairs with the same grayscale value). The smoother the surface, the closer the pixel grayscale values, and the higher the homogeneity value. The rougher the surface, the lower the homogeneity value.

[0042] Contrast ,energy ,entropy and homogeneity characteristics The mathematical expressions are as follows:

[0043]

[0044]

[0045]

[0046] In the formula, Indicates the number of gray levels. Indicates the first gray level. Indicates the second gray level. This represents the probability of grayscale coexistence.

[0047] (2) Crack characteristic area Crack feature region extraction is used to describe all suspected cracks in concrete, thereby determining whether crack propagation and enlargement occur. Since cracks are linear structures, and the Hessian matrix has good descriptive properties for linear structures, this application utilizes the Hessian matrix to filter crack pixels, thus forming crack regions. The specific process includes: (2-1) Preprocess the original image at the current time point to obtain the first preprocessed image. ; (2-2) Calculate the first preprocessed image Each pixel Hessian matrix The Hessian matrix The mathematical expression is:

[0048] In the formula, for Second-order partial derivatives in the direction, for Second-order partial derivatives in the direction, The mixed second-order partial derivative; (2-3) Based on the Hessian matrix Calculate pixels First eigenvalue Second eigenvalue , wherein the first feature value and the second eigenvalue The mathematical expressions are as follows:

[0049]

[0050] (2-4) Based on the first feature value of the pixel Second eigenvalue Calculate the degree of anisotropy Wherein, the anisotropy degree The mathematical expression is:

[0051] (2-5) will satisfy: The pixels are used as crack pixels, where, Indicates the heterogeneity threshold; (2-6) Perform density clustering on the crack pixels to obtain multiple pixel clusters, and take the pixel clusters with a number of pixels greater than or equal to a preset number threshold as crack clusters; and construct crack regions based on the crack clusters.

[0052] (3) Ultrasonic roughness characteristics When ultrasound propagates to the surface or internal defects of concrete, the amplitude, frequency components, and energy attenuation of its echo signal will change significantly due to surface roughness (caused by scattering) and changes in internal structure.

[0053] Ultrasonic roughness features can reflect actual roughness. When water seeps into a concrete surface, the flowing water covers surface cracks and uneven areas, causing a significant decrease in actual roughness. At this time, the scattering of ultrasonic waves on the concrete surface decreases, and the echo peak amplitude increases, thus significantly increasing ultrasonic smoothness. Visual roughness, however, is less affected by the reflective properties of water and changes little. Using this principle to accurately identify water seepage avoids interference from various noises on the concrete surface. The extraction process of ultrasonic roughness features includes: (3-1) The echo signal is preprocessed to obtain a preprocessed echo signal, wherein the preprocessing includes filtering, amplification and gain compensation; (3-2) Extract the time window of the surface reflected wave from the preprocessed echo signal, and extract the absolute maximum value of the signal from the time window to obtain the echo peak amplitude. ; When an ultrasonic wave is incident perpendicularly on a concrete surface, some of its energy is reflected, forming an echo. The rougher the surface, the more the sound waves are scattered at the microscopic protrusions, resulting in less energy being reflected back to the transducer, and thus a decrease in the peak amplitude of the echo.

[0054] (3-3) Extract the spectral center frequency from the preprocessed echo signal ; Center frequency of the spectrum The mathematical expression is:

[0055] In the formula, This indicates the number of points in the spectrum obtained by the Fast Fourier Transform. Indicates the frequency component index. Indicates the first One frequency component, This represents the discrete spectrum obtained by performing a Fast Fourier Transform on the echo signal.

[0056] (3-4) Extract the echo envelope of the preprocessed echo signal. Construct a decay model within a selected time window, wherein the mathematical expression of the decay model is:

[0057] In the formula, Indicates the initial amplitude. Indicates the attenuation coefficient. Indicates the time of transmission; Sampling is performed within the selected time window to obtain multiple sampling time points and their envelope amplitude values. The attenuation model is then linearly fitted based on these multiple sampling time points and their envelope amplitude values ​​to obtain the attenuation coefficient. Attenuation coefficient The larger the value (the more negative), the faster the decay, and the more severe the roughness or internal damage may be.

[0058] S250, calculate visual roughness based on a pre-built first fitting model and the visual roughness feature, and calculate ultrasonic smoothness based on a pre-built second fitting model and the ultrasonic roughness feature, wherein the first fitting model characterizes the relationship between visual roughness feature and roughness, and the second fitting model characterizes the relationship between ultrasonic roughness feature and roughness. This application employs a multivariate linear relationship to fit the relationship between roughness and visual roughness features, and to fit the relationship between roughness and ultrasonic roughness features, specifically including: (1) Acquire multiple concrete surface image samples and ultrasonic echo signal samples of the concrete surface; Multiple concrete surface image samples and ultrasonic echo signal samples of concrete surfaces were manually labeled with their roughness, i.e., visual roughness. and ultrasonic smoothness During annotation, for the same concrete wall surface image sample and the ultrasonic echo signal of the concrete surface, its visual roughness is... and ultrasonic smoothness They should be equal to ensure that the evaluation labels of the subsequently obtained fitted models are consistent; (2) Extract visual roughness feature samples from the concrete surface image samples and extract ultrasonic roughness feature samples from the ultrasonic echo signal samples, wherein the visual roughness feature samples include contrast samples. Energy sample Entropy samples and homogeneous feature samples The ultrasonic roughness feature sample includes echo peak amplitude samples. Spectrum center frequency sample and attenuation coefficient samples ,in, For concrete surface image sample index, For the ultrasonic echo signal sample index; Please refer to the previous text for the feature extraction process, which will not be repeated here.

[0059] (3) Normalize the ultrasonic coarse feature sample and the visual coarse feature sample respectively to obtain normalized ultrasonic coarse feature sample and normalized visual coarse feature sample. Construct a first multivariate linear relationship and a second multivariate linear relationship, wherein the mathematical expression of the first multivariate linear relationship is:

[0060] The mathematical expression for the second multivariate linear relationship is:

[0061] In the formula, Indicates the visual roughness of the annotation. This indicates the labeled ultrasonic smoothness. and Both represent intercepts. Represents the contrast coefficient. Indicates normalized contrast. Represents the coefficient of the energy term. Represents normalized energy. Represents the coefficient of the entropy term. Represents the normalized entropy. Represents the coefficient of the homogeneity characteristic term. Indicates normalized homogeneity characteristics. This represents the coefficient of the peak echo amplitude term. Indicates the normalized echo peak amplitude. Represents the coefficient of the center frequency term in the spectrum. Indicates the center frequency of the normalized spectrum. This represents the coefficient of the attenuation coefficient term. Represents the normalized attenuation coefficient. and All are error terms; (4) Substitute the normalized visual roughness feature samples into the first multivariate linear relationship and fit them using the least squares method to obtain the first fitting model; and substitute the normalized ultrasonic roughness feature samples into the second multivariate linear relationship and fit them using the least squares method to obtain the second fitting model.

[0062] The least squares fitting of the multivariate linear model is an existing technique and will not be elaborated here. The aforementioned model transforms complex feature parameters into quantifiable roughness indices, improving the accuracy and robustness of roughness assessment through multi-feature fusion. The fitted model is based on actual sample data, ensuring the objectivity and reliability of the results. Furthermore, the coefficients of each feature obtained using the multivariate linear model can explain the most significant contribution of each parameter to roughness, exhibiting good interpretability and facilitating subsequent algorithm debugging.

[0063] After obtaining the first and second fitting models mentioned above, the calculation process for visual roughness and ultrasonic smoothness includes: S251, normalize the visual roughness feature to obtain normalized visual roughness feature; and normalize the ultrasonic roughness feature to obtain normalized ultrasonic roughness feature. Normalization can be achieved using max-min normalization.

[0064] S252, Substitute the normalized visual roughness features into the first fitting model to obtain the visual roughness. The normalized ultrasonic roughness features are then substituted into the second fitting model to obtain the ultrasonic smoothness. .

[0065] S260, compare the visual roughness with the ultrasonic smoothness to obtain a roughness comparison result, and determine the water seepage situation based on the roughness comparison result; and compare the crack feature area with the crack feature reference area of ​​the previous detection cycle to obtain a crack comparison result; Specifically, it includes: Calculate the visual roughness With the aforementioned ultrasonic smoothness Difference rate , wherein the difference rate The mathematical expression is:

[0066] The difference rate If the difference exceeds the preset threshold, it is determined that there is water seepage on the concrete surface.

[0067] If the visual roughness With the aforementioned ultrasonic smoothness Coming from the same wall surface, therefore theoretically the visual roughness... With the aforementioned ultrasonic smoothness If the two are equal, and the difference is significant, after ruling out rainfall (as determined by staff), it can be determined that the wall is leaking.

[0068] S270, Real-time monitoring of concrete quality in water conservancy projects based on the comparison results of water seepage and cracks.

[0069] When water seeps into the concrete surface, and the area of ​​the cracked area in the current inspection cycle is larger than the area of ​​the cracked area in the previous inspection cycle, an alarm message will be sent to the target object.

[0070] If water seepage is observed on the concrete surface, and the crack area in the current inspection cycle is larger than the crack area in the previous inspection cycle, it indicates a quality problem with the concrete that requires repair. Therefore, an alarm message will be sent to the staff.

[0071] This invention presents a method for inspecting the quality of concrete in hydraulic engineering projects based on image recognition technology. By integrating image sequence analysis and ultrasonic detection technology, it achieves real-time and continuous quality monitoring of concrete in hydraulic engineering projects, effectively overcoming the inherent defects of traditional periodic inspection methods. By comparing the differences between current and historical images, ultrasonic detection is automatically triggered to accurately extract visual and ultrasonic roughness features. Combined with a preset fitting model, visual roughness and actual roughness are calculated, enabling early warning of seepage risks. Simultaneously, a dynamic comparison mechanism of crack feature areas can track crack expansion in real time, avoiding the missed detection of sudden hidden dangers (such as dangerous cracks and seepage). This technology significantly shortens the inspection cycle, reducing the response time to potential hazards from several weeks to real-time, providing a scientific basis for preventing engineering accidents and optimizing maintenance decisions. It not only ensures the structural safety and durability of hydraulic engineering projects but also reduces the cost of periodic inspections, demonstrating significant engineering practical value and promotion potential.

[0072] like Figure 4 As shown, this application also provides a concrete quality inspection system for hydraulic engineering based on image recognition technology, including: The acquisition module is used to acquire an image sequence of the concrete area to be inspected in a hydraulic engineering project, wherein the image sequence is acquired based on a pre-set image acquisition device; The difference calculation module is used to extract the original image at the current time point and the original image at the previous time point from the image sequence, and to calculate the difference between the original image at the current time point and the original image at the previous time point; An ultrasonic detection module is used to control an ultrasonic detection device to emit ultrasonic signals toward the area to be detected and obtain echo signals when the difference is greater than a preset difference threshold. The feature extraction module is used to extract visual roughness features and crack feature regions from the original image at the current time point, and to extract ultrasonic roughness features from the echo signal; The roughness calculation module is used to calculate visual roughness based on a pre-built first fitting model and the visual roughness feature, and to calculate ultrasonic smoothness based on a pre-built second fitting model and the ultrasonic roughness feature, wherein the first fitting model characterizes the relationship between visual roughness feature and roughness, and the second fitting model characterizes the relationship between ultrasonic roughness feature and roughness. The comparison module is used to compare the visual roughness with the ultrasonic smoothness to obtain a roughness comparison result, and to determine the water seepage situation based on the roughness comparison result; and to compare the crack feature area with the crack feature reference area of ​​the previous detection cycle to obtain a crack comparison result. The detection module is used to perform real-time detection of the concrete quality of water conservancy projects based on the comparison results of the water seepage and the cracks.

[0073] This invention presents a concrete quality inspection system for hydraulic engineering based on image recognition technology. By integrating image sequence analysis and ultrasonic detection technology, it achieves real-time and continuous quality monitoring of concrete in hydraulic engineering projects, effectively overcoming the inherent shortcomings of traditional periodic inspection methods. By comparing the differences between current and historical images, ultrasonic detection is automatically triggered to accurately extract visual and ultrasonic roughness features. Combined with a preset fitting model, visual roughness and actual roughness are calculated, enabling early warning of seepage risks. Simultaneously, a dynamic comparison mechanism for crack feature areas can track crack expansion in real time, avoiding the missed detection of sudden hidden dangers (such as dangerous cracks and seepage). This technology significantly shortens the inspection cycle, reducing the response time from several weeks to real-time, providing a scientific basis for preventing engineering accidents and optimizing maintenance decisions. It not only ensures the structural safety and durability of hydraulic engineering projects but also reduces the cost of periodic inspections, demonstrating significant engineering practical value and promotion potential.

[0074] Figure 5 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 5 The computer system of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0075] like Figure 5As shown, the computer system includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on a program stored in Read-Only Memory (ROM) 502 or a program loaded from storage portion 508 into Random Access Memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0076] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0077] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this application.

[0078] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0080] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0081] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0082] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0083] The above embodiments are merely preferred embodiments provided to fully illustrate this application, and the scope of protection of this application is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on this application are all within the scope of protection of this application.

Claims

1. A method for detecting the quality of concrete in water conservancy projects based on image recognition technology, characterized in that, Including the following steps: An image sequence of the concrete area to be inspected in a hydraulic engineering project is acquired, wherein the image sequence is acquired based on a pre-set image acquisition device; Extract the original image at the current time point and the original image at the previous time point from the image sequence, and calculate the difference between the original image at the current time point and the original image at the previous time point; When the difference is greater than a preset difference threshold, the ultrasonic detection device is controlled to emit an ultrasonic signal toward the area to be detected and an echo signal is obtained. Visual roughness features and crack feature regions are extracted from the original image at the current time point, and ultrasonic roughness features are extracted from the echo signal; Visual roughness is calculated based on a pre-built first fitting model and the visual roughness feature, and ultrasonic smoothness is calculated based on a pre-built second fitting model and the ultrasonic roughness feature, wherein the first fitting model characterizes the relationship between visual roughness feature and roughness, and the second fitting model characterizes the relationship between ultrasonic roughness feature and roughness. The visual roughness is compared with the ultrasonic smoothness to obtain a roughness comparison result, and the water seepage situation is determined based on the roughness comparison result; and the crack feature area is compared with the crack feature reference area of ​​the previous detection cycle to obtain a crack comparison result. The concrete quality of water conservancy projects is monitored in real time based on the comparison results of the water seepage and cracks.

2. The method for detecting the quality of concrete in water conservancy projects based on image recognition technology according to claim 1, characterized in that, Calculating the difference between the original image at the current time point and the original image at the previous time point includes: The original image at the current time point The original image from the previous time point Preprocessing is performed separately to obtain the first preprocessed image. Second preprocessed image The preprocessing includes contrast enhancement, grayscale conversion, and filtering. Calculate the first preprocessed image Second preprocessed image Differences , wherein the difference The mathematical expression is: In the formula, Indicates the image width. Indicates the image height. This indicates that the coordinates in the first preprocessed image are... The pixel value of the pixel, This indicates that the coordinates in the second preprocessed image are... The pixel value of the pixel.

3. The method for detecting the quality of concrete in water conservancy projects based on image recognition technology according to claim 1, characterized in that, Extracting visual coarse features from the original image at the current time point includes: The original image at the current time point is preprocessed to obtain a first preprocessed image. ; Calculate the first preprocessed image Gray co-occurrence matrix ; Based on the gray-level co-occurrence matrix Extract the first preprocessed image Contrast ,energy ,entropy and homogeneity characteristics This yields visually coarse features.

4. The method for detecting the quality of concrete in water conservancy projects based on image recognition technology according to claim 1, characterized in that, Extracting crack feature regions from the original image at the current time point includes: The original image at the current time point is preprocessed to obtain a first preprocessed image. ; Calculate the first preprocessed image Each pixel Hessian matrix ; According to the Hessian matrix Calculate pixels First eigenvalue Second eigenvalue ; Based on the first feature value of the pixel Second eigenvalue Calculate the degree of anisotropy Wherein, the anisotropy degree The mathematical expression is: Will satisfy: The pixels are used as crack pixels, where, Indicates the heterogeneity threshold; Density clustering is performed on the crack pixels to obtain multiple pixel clusters, and pixel clusters with a number of pixels greater than or equal to a preset threshold are taken as crack clusters; and crack regions are constructed based on the crack clusters.

5. The method for detecting the quality of concrete in water conservancy projects based on image recognition technology according to claim 1, characterized in that, The ultrasonic roughness features include echo peak amplitude, spectral center frequency, and attenuation coefficient, wherein extracting the ultrasonic roughness features from the echo signal includes: The echo signal is preprocessed to obtain a preprocessed echo signal, wherein the preprocessing includes filtering, amplification and gain compensation; The time window for surface reflected waves is extracted from the preprocessed echo signal, and the absolute maximum value of the signal is extracted from the time window to obtain the echo peak amplitude. ; Extract the spectral center frequency from the preprocessed echo signal ; Extract the echo envelope of the preprocessed echo signal. Construct a decay model within a selected time window, wherein the mathematical expression of the decay model is: In the formula, Indicates the initial amplitude. Indicates the attenuation coefficient. Indicates the time of transmission; Sampling is performed within the selected time window to obtain multiple sampling time points and their envelope amplitude values. The attenuation model is then linearly fitted based on these multiple sampling time points and their envelope amplitude values ​​to obtain the attenuation coefficient. .

6. The method for detecting the quality of concrete in hydraulic engineering based on image recognition technology according to claim 1, characterized in that, The method for constructing the first fitting model or the second fitting model includes: Acquire multiple concrete surface image samples and ultrasonic echo signal samples of the concrete surface; Visual roughness feature samples are extracted from the concrete surface image samples, and ultrasonic roughness feature samples are extracted from the ultrasonic echo signal samples, wherein the visual roughness feature samples include contrast samples. Energy sample Entropy samples and homogeneous feature samples The ultrasonic roughness feature sample includes echo peak amplitude samples. Spectrum center frequency sample and attenuation coefficient samples ,in, For concrete surface image sample index, Index of ultrasonic echo signal samples; The ultrasonic coarse feature samples and the visual coarse feature samples are normalized respectively to obtain normalized ultrasonic coarse feature samples and normalized visual coarse feature samples. Construct a first multivariate linear relationship and a second multivariate linear relationship, wherein the mathematical expression of the first multivariate linear relationship is: The mathematical expression for the second multivariate linear relationship is: In the formula, Indicates the visual roughness of the annotation. This indicates the labeled ultrasonic smoothness. and Both represent intercepts. Represents the contrast coefficient. Indicates normalized contrast. Represents the coefficient of the energy term. Represents normalized energy. Represents the coefficient of the entropy term. Represents the normalized entropy. Represents the coefficient of the homogeneity characteristic term. Indicates normalized homogeneity characteristics. This represents the coefficient of the peak echo amplitude term. Indicates the normalized echo peak amplitude. Represents the coefficient of the center frequency term in the spectrum. Indicates the center frequency of the normalized spectrum. This represents the coefficient of the attenuation coefficient term. Represents the normalized attenuation coefficient. and All are error terms; Substituting the normalized visual roughness feature samples into the first multivariate linear relationship and combining it with the least squares method for fitting, a first fitting model is obtained; and substituting the normalized ultrasonic roughness feature samples into the second multivariate linear relationship and combining it with the least squares method for fitting, a second fitting model is obtained.

7. The method for detecting the quality of concrete in water conservancy projects based on image recognition technology according to claim 6, characterized in that, Visual roughness is calculated based on a pre-built first fitting model and the visual roughness features, and ultrasonic smoothness is calculated based on a pre-built second fitting model and the ultrasonic roughness features, including: The visual roughness features are normalized to obtain normalized visual roughness features; and the ultrasonic roughness features are normalized to obtain normalized ultrasonic roughness features. Substituting the normalized visual roughness features into the first fitting model, we obtain the visual roughness. The normalized ultrasonic roughness features are then substituted into the second fitting model to obtain the ultrasonic smoothness. .

8. The method for detecting the quality of concrete in water conservancy projects based on image recognition technology according to claim 1, characterized in that, The visual roughness is compared with the ultrasonic smoothness to obtain a roughness comparison result, and the water seepage situation is determined based on the roughness comparison result, including: Calculate the visual roughness With the aforementioned ultrasonic smoothness Difference rate , wherein the difference rate The mathematical expression is: The difference rate If the difference exceeds the preset threshold, it is determined that there is water seepage on the concrete surface.

9. The method for detecting the quality of concrete in water conservancy projects based on image recognition technology according to claim 1, characterized in that, Real-time monitoring of concrete quality in hydraulic engineering projects is conducted based on the comparison results of the water seepage and cracks, including: When water seeps into the concrete surface, and the area of ​​the cracked area in the current inspection cycle is larger than the area of ​​the cracked area in the previous inspection cycle, an alarm message will be sent to the target object.

10. A concrete quality inspection system for hydraulic engineering based on image recognition technology, characterized in that, include: The acquisition module is used to acquire an image sequence of the concrete area to be inspected in a hydraulic engineering project, wherein the image sequence is acquired based on a pre-set image acquisition device; The difference calculation module is used to extract the original image at the current time point and the original image at the previous time point from the image sequence, and to calculate the difference between the original image at the current time point and the original image at the previous time point; An ultrasonic detection module is used to control an ultrasonic detection device to emit ultrasonic signals toward the area to be detected and obtain echo signals when the difference is greater than a preset difference threshold. The feature extraction module is used to extract visual roughness features and crack feature regions from the original image at the current time point, and to extract ultrasonic roughness features from the echo signal; The roughness calculation module is used to calculate visual roughness based on a pre-built first fitting model and the visual roughness feature, and to calculate ultrasonic smoothness based on a pre-built second fitting model and the ultrasonic roughness feature, wherein the first fitting model characterizes the relationship between visual roughness feature and roughness, and the second fitting model characterizes the relationship between ultrasonic roughness feature and roughness. The comparison module is used to compare the visual roughness with the ultrasonic smoothness to obtain a roughness comparison result, and to determine the water seepage situation based on the roughness comparison result; and to compare the crack feature area with the crack feature reference area of ​​the previous detection cycle to obtain a crack comparison result. The detection module is used to perform real-time detection of the concrete quality of water conservancy projects based on the comparison results of the water seepage and the cracks.

Images