Concrete stability testing system based on image processing

Abstract

The concrete stability test system based on image processing comprises a light intensity test module, an image acquisition module, a concrete stability test module and a stability result export module, and the output end of the light intensity test module and the output end of the image acquisition module are both connected with the input end of the concrete stability test module. The output end of the concrete stability test module is connected with the input end of the stability result export module, the light intensity test module is used for monitoring concrete stability test environment light intensity information, the image acquisition module is used for acquiring wall image information, and the light intensity information and the wall image information are both transmitted to the concrete stability test module. The concrete stability test module selects a corresponding image processing unit to carry out image processing on the image information according to the received light intensity information, and the concrete stability test module judges the stability of the wall body according to the processed image information.

Description

Technical Field

[0001] This invention relates to the field of testing, and more particularly to a concrete stability testing system based on image processing. Background Technology

[0002] The most significant factor affecting concrete stability is concrete cracking. Understanding crack information in concrete structures is crucial for maintaining damaged structures. Most existing systems require numerous operators to manually inspect for defects, which compromises their safety. Furthermore, the accuracy and efficiency of inspections heavily depend on the skills and experience of the inspectors. For these reasons, an automated system for detecting concrete stability is essential.

[0003] In situations of insufficient light intensity, existing image processing techniques often overlook the complexity of the image acquisition process itself and lack specific consideration for identifying concrete cracks in shadowed conditions. One possible approach to address this problem is shadow detection and removal; however, shadow removal is far from a simple task. Shadow removal in concrete crack images typically severely impacts the quality of the digital image, making further crack detection extremely challenging. Even when light intensity is within expectations, the image acquisition process is severely affected by complex environmental and lighting conditions. The acquired images contain noise caused by irregular lighting and various imperfections. Due to the similarity between cracks and the background, it is difficult to distinguish cracks from the background. Summary of the Invention

[0004] The image processing-based concrete stability testing system provided by this invention includes a light intensity testing module, an image acquisition module, a concrete stability testing module, and a stability result export module. The output terminals of the light intensity testing module and the image acquisition module are both connected to the input terminal of the concrete stability testing module, and the output terminal of the concrete stability testing module is connected to the input terminal of the stability result export module.

[0005] Preferably, the light intensity testing module is used to monitor the ambient light intensity information of the concrete stability test environment, and the image acquisition module is used to acquire wall image information. Both the light intensity information and the wall image information are transmitted to the concrete stability testing module. The concrete stability testing module selects the corresponding image processing unit to process the image information according to the received light intensity information. The concrete stability testing module judges the stability of the wall based on the processed image information. If the length of the crack is greater than a preset threshold, the wall is judged to be in an unstable state. The concrete stability testing module transmits the test results to the stability result export module.

[0006] Preferably, the concrete stability testing module includes a signal receiving unit, an analysis and selection unit, a raw image storage unit, a raw image sending unit, a first image processing unit, a second image processing unit, and a stability analysis unit. The output terminals of the light intensity testing module and the image acquisition module are both connected to the input terminals of the signal receiving unit. The output terminal of the signal receiving unit is connected to the input terminal of the raw image storage unit. The output terminal of the raw image storage unit is connected to the input terminal of the raw image sending unit. The output terminal of the signal receiving unit is connected to the input terminal of the analysis and selection unit. The output terminal of the analysis and selection unit is connected to the input terminal of the first image processing unit. The output terminal of the analysis and selection unit is connected to the input terminal of the second image processing unit. The output terminals of both the first and second image processing units are connected to the input terminal of the stability analysis unit. The output terminal of the stability analysis unit is connected to the input terminal of the stability result export module.

[0007] Preferably, the light intensity testing module and the image acquisition module transmit image information to the original image storage unit and light intensity information to the analysis and selection unit through the signal receiving unit. The analysis and selection unit stores a light intensity threshold. If the received light intensity information is less than the light intensity threshold, the original image storage unit transmits the image information to the first image processing unit. If the received light intensity information is greater than or equal to the light intensity threshold, the original image storage unit transmits the image information to the second image processing unit. The image processed by the first or second image processing unit is transmitted to the stability analysis unit. The stability analysis unit determines the stability of the wall based on the received image information. If the length of the crack is greater than a preset threshold, the wall is determined to be in an unstable state. The stability analysis unit transmits the test results to the stability result export module.

[0008] Preferably, the first image processing unit processes the received image as follows:

[0009] S1: Ray tracing steps, described by the following integral equation for light propagation.

[0010] L o (x,ω o ) = L E (x,ω o )+∫ Ω L i (x,ω i )f r (x,ω i ,ω o )(ω i ·n)dω i

[0011] Where x is a spatial variable, ω o It is the direction of the emitted light ray, Lo (x,ω o ) is from point x along direction ω o The total spectral radiance emitted, L E (x,ω o ) is from point x along direction ω o The emitted reference spectral radiance, Ω, is at x,ω i The unit hemisphere along the direction of the normal vector n, x, ω i It is the direction of the incident ray, L i From direction ω i Spectral radiance reaching x inward, f r (x,ω i ,ω o ) is the bidirectional reflection distribution function (at x, from direction ω) i Reflected to ω o (the proportion of light);

[0012] in:

[0013]

[0014] Where f(x) is an arbitrary function, and X is a set of N samples uniformly distributed in Ω;

[0015] S2: Enhance the shadow mask step, using standard image transformation to enhance the shadow mask dataset, generating several unique shadow masks with random rotation parameters between 0 and 360 degrees;

[0016] S3: The shadow blending step enhances concrete crack images by combining images using overlay techniques. This involves blending one image with another to create a single image that incorporates features from both source images.

[0017] Preferably, the second image processing unit processes the received image as follows:

[0018] D1: Preprocessing, converting image information to grayscale using the following formula:

[0019] I(R,G,B)=0.299*R+0.587*G+0.144*B

[0020] R, G, and B represent the values ​​of the red, green, and blue channels, respectively;

[0021] Adjust the average grayscale value of the image to between 180 and 190, and calculate the compensation value for each pixel using the following formula:

[0022]

[0023] The original grayscale image is represented using a two-dimensional matrix: FM×N =[f(x,y)] M×N M×N is the rotation of the image, f(x,y) is the gray value of pixel (x,y), A is a constant value that the preset image average value reaches, and then C is added to each pixel of the image;

[0024] Operate on each pixel in the image using the following formula:

[0025]

[0026] f(x,y) represents the input grayscale value, and g(x,y) is the output grayscale value. [X1,X2] is the preset range, and β and γ are both preset parameters.

[0027] Use the following formula to increase the contrast between the crack and the background:

[0028]

[0029] Where f(x,y) is the original gray value of pixel (x,y);

[0030] D2: Image segmentation, the image segmentation formula is as follows:

[0031]

[0032] Where T is the segmentation threshold;

[0033] D3: Crack detection, expressed by the following formula:

[0034]

[0035] Where A is the number of pixels in the cluster, C is its perimeter, and F ranges from 0 to 1. If the shape of a cluster is close to a circle, then F is closer to 1, and the cluster is noise. Conversely, if the shape of a region is close to a linear shape, F is close to 0, and the region is a crack.

[0036] The concrete stability testing system based on image processing provided by this invention improves image clarity and thus enhances the accuracy of stability testing by monitoring ambient brightness and selecting different image processing units. The first image processing unit proposes an image processing method that differs from existing technologies, using complex shadow shapes to enhance existing crack datasets for accurate crack detection. This first image processing unit helps automate crack detection in real-world environments. The image enhancement technology of the first image processing unit is achieved through three consecutive steps: ray tracing of shadows, shadow dataset enhancement, and shadow fusion, significantly improving the robustness of image processing and model accuracy. The second image processing unit can more accurately acquire minute cracks. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the image processing-based concrete stability testing system of the present invention;

[0038] Figure 2 This is a schematic diagram of shadow image synthesis and enhancement of the first image processing unit of the present invention;

[0039] Figure 3 The image information of the present invention is the image information processed by the first image processing unit;

[0040] Figure 4 This refers to the image information processed by the second image processing unit according to the present invention. Detailed Implementation

[0041] The image processing-based concrete stability testing system provided by the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0042] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in the block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0043] The terms "comprising," "including," and similar terms used should be understood as open-ended, meaning "including / including but not limited to," indicating that other contents may also be included. The same reference numerals used in the embodiments and accompanying drawings indicate the same or similar components or structural features. Furthermore, when describing a component as being disposed on another component, it means that a component is directly disposed on the other component, or a component is indirectly disposed on the other component, i.e., one or more components are disposed between the two components. Conversely, when describing a component as being directly disposed on another component, it means that there are no other components between the two components.

[0044] Concrete structures are often subjected to high levels of stress and strain. The stress in concrete structures is caused by continuous cyclic loading, temperature changes and weathering, which may lead to the generation and propagation of cracks in the concrete structure.

[0045] like Figure 1 As shown, the concrete stability testing system provided by the present invention includes a light intensity testing module, an image acquisition module, a concrete stability testing module, and a stability result export module.

[0046] Specifically, the output terminals of the light intensity testing module and the image acquisition module are both connected to the input terminal of the concrete stability testing module, and the output terminal of the concrete stability testing module is connected to the input terminal of the stability result export module.

[0047] Specifically, the light intensity testing module is used to monitor the ambient light intensity information of the concrete stability test environment, and the image acquisition module is used to acquire wall image information. Both the light intensity information and the wall image information are transmitted to the concrete stability testing module. The concrete stability testing module selects the corresponding image processing unit to process the image information based on the received light intensity information. The concrete stability testing module judges the stability of the wall based on the processed image information. If the length of the crack is greater than a preset threshold, the wall is judged to be in an unstable state. The concrete stability testing module transmits the test results to the stability result export module.

[0048] like Figure 1 As shown, the concrete stability testing module includes a signal receiving unit, an analysis and selection unit, a raw image storage unit, a raw image sending unit, a first image processing unit, a second image processing unit, and a stability analysis unit.

[0049] Specifically, the output terminals of the light intensity testing module and the image acquisition module are both connected to the input terminal of the signal receiving unit. The output terminal of the signal receiving unit is connected to the input terminal of the original image storage unit. The output terminal of the original image storage unit is connected to the input terminal of the original image sending unit. The output terminal of the signal receiving unit is connected to the input terminal of the analysis and selection unit. The output terminal of the analysis and selection unit is connected to the input terminal of the first image processing unit. The output terminal of the analysis and selection unit is connected to the input terminal of the second image processing unit. The output terminals of both the first and second image processing units are connected to the input terminal of the stability analysis unit. The output terminal of the stability analysis unit is connected to the input terminal of the stability result export module.

[0050] Specifically, the light intensity testing module and the image acquisition module transmit image information to the original image storage unit and light intensity information to the analysis and selection unit through the signal receiving unit. The analysis and selection unit stores a light intensity threshold. If the received light intensity information is less than the light intensity threshold, the original image storage unit transmits the image information to the first image processing unit. If the received light intensity information is greater than or equal to the light intensity threshold, the original image storage unit transmits the image information to the second image processing unit. The image processed by the first or second image processing unit is transmitted to the stability analysis unit. The stability analysis unit determines the stability of the wall based on the received image information. If the length of the crack is greater than a preset threshold, the wall is determined to be in an unstable state. The stability analysis unit transmits the test results to the stability result export module.

[0051] Specifically, the concrete stability testing system provided by this invention improves the clarity of image processing and thus enhances the accuracy of stability testing by monitoring ambient brightness and selecting different image processing units.

[0052] Specifically, the first image processing unit proposes an image processing method that differs from existing technologies. It enhances existing crack datasets with complex shadow shapes to achieve accurate crack detection. The first image processing unit helps to automate crack detection in real-world environments.

[0053] In actual monitoring, if the light intensity is weak, the images of the concrete surface containing cracks captured by the image acquisition module will be affected by complex lighting conditions, including shadows and changes in brightness.

[0054] like Figure 2 As shown, the first image processing unit processes the received image as follows:

[0055] S1: Ray tracing step, using path tracing to accurately represent optical phenomena. Path tracing is a ray tracing technique used to simulate the physical behavior of light. The algorithm is based on the solution to the rendering equation, which describes the propagation of light in the scene through the following integral equation.

[0056] L o (x,ω o ) = L E (x,ω o )+∫ Ω L i (x,ω i )f r (x,ω i ,ω o )(ω i ·n)dω i

[0057] where x is a spatial variable, ω o is the direction of the outgoing ray, L o (x, ω o ) is the total spectral radiance emitted from point x in the direction ω o , L E (x, ω o ) is the reference spectral radiance emitted from point x in the direction ω o , Ω is the unit hemisphere in the direction of the normal vector n at x, ω i , x, ω i is the direction of the incident ray, L i is the spectral radiance reaching x from the direction ω i inward, f r (x, ω i , ω o ) is the bidirectional reflectance distribution function (the proportion of light reflected from the direction ω i at x to ω o ).

[0058] Where:

[0059]

[0060] where f(x) is an arbitrary function and X is a set consisting of N samples uniformly distributed in Ω.

[0061] A brief overview of the ray tracing algorithm is as follows: Multiple rays are projected from each pixel of the image acquisition module in random directions into the scene. The generated rays are reflected, refracted, or absorbed by the objects in the scene until they reach the light source or the user-defined bounce limit, forming a set of paths from the camera to the light source. Then, the amount of light for each pixel is calculated for each ray, and this value is averaged and assigned to that specific pixel.

[0062] S2: Enhancement of the shadow mask step. Standard image transformation techniques are used to enhance the shadow mask dataset (in the specific monitoring process, this dataset contains several unique shadow masks), as shown in the second part of Figure 4 . Transformations such as random rotation (0 < θ < 360), random scaling (0.2), height (0.2), width (0.2), shear (0.2), and opacity (0.6 < x < 0.8) are applied to generate several unique shadow masks. The parameter for random rotation is between 0 and 360 degrees because shadows can be projected onto the concrete image at any angle, and the other parameters are selected based on the actual situation to prevent deviation from the original shadow form.

[0063] S3: The shadow blending step enhances concrete crack images by combining images using overlay techniques. This involves blending one image (the shadow panel) with image information (the concrete surface) to create the illusion of a single image containing features from both source images. This effect is achieved using a multiplicative blending operation, which takes the value of each pixel in the image from 0 to 1 and multiplies them by the value of the corresponding pixel in the other image. As long as any layer is brighter than black, the composite image will be darker because each value is less than 1, and the product will be less than each initial value greater than or equal to zero. This operation is performed on the entire dataset of several images.

[0064] Figure 3 The image information after processing by the first image processing unit is displayed. Figure 3 -a and Figure 3 -b represents crack-free image information. Figure 3 -c and Figure 3 -d specifies image information with cracks.

[0065] Specifically, the image enhancement technique of the first image processing unit is achieved through three consecutive steps: ray tracing of shadows, shadow dataset enhancement, and shadow fusion. The robustness of image processing and model accuracy are significantly improved (actually from 0.9045 to 0.9941). This enhanced classification capability is beneficial for structural control and stability monitoring of concrete structures using an image acquisition module, where the method must be robust enough to handle real-world images affected by various environmental conditions, such as shadows, occlusion, defects, and concrete spalling.

[0066] Specifically, if the light intensity information is greater than or equal to the light intensity threshold, it indicates that the light intensity at the monitoring site meets expectations. The second image processing unit then processes the received image as follows:

[0067] D1: Preprocessing. Image information inevitably contains some noise, which can cause serious problems in automatic crack detection. In order to remove noise, the first step is to perform some preprocessing.

[0068] Use the following formula to convert image information to a grayscale image:

[0069] I(R,G,B)=0.299*R+0.587*G+0.144*B

[0070] R, G, and B represent the values ​​of the red, green, and blue channels, respectively. One is the grayscale value of the pixel after conversion. This conversion can improve image processing efficiency without losing crack information.

[0071] As mentioned earlier, the average grayscale value of a single image is adjusted to between 180 and 190, and the compensation value for each pixel is calculated using the following formula:

[0072]

[0073] The original grayscale image is represented using a two-dimensional matrix: F M×N =[f(x,y)] M×N M×N is the rotation of the image, f(x,y) is the gray value of pixel (x,y), A is a constant value that the preset image average value reaches, and then C is added to each pixel of the image. This adjustment makes the image and image segmentation more comparable.

[0074] Separating cracks from the background using thresholding is difficult because the threshold is not easy to choose, which reduces the efficiency of the method under different conditions.

[0075] To enhance the difference between the cracks and the background, grayscale correction can be applied to the concrete image, using the following formula to operate on each pixel in the image:

[0076]

[0077] f(x,y) represents the input grayscale value, and g(x,y) is the output grayscale value. [X1,X2] is the preset range, and β and γ are both preset parameters.

[0078] Noise is removed using a Gaussian filter, and the contrast between the crack and the background is improved using the following formula:

[0079]

[0080] Where f(x,y) is the original gray value of pixel (x,y). The smaller f(x,y) is, the better (255-f(x,y)) and (255-f(x,y)) are. The larger the value, the more this non-linear function can further improve the image contrast.

[0081] D2: Image segmentation, applying morphological operations such as dilation and thinning to ensure connectivity between crack segments. Due to the high contrast of the image, the above method makes thresholding segmentation easier.

[0082]

[0083] Where T is the segmentation threshold.

[0084] D3: Crack detection. A binary image is obtained through threshold segmentation. This image contains two regions: one representing cracks, and the other representing residual noise caused by irregular lighting and surface imperfections in the concrete. Therefore, cracks must be distinguished and noise removed. By characterizing the regions, we can assess whether each region constitutes a crack, expressed by the following formula:

[0085]

[0086] Where A is the number of pixels in the cluster, C is its perimeter, and F ranges from 0 to 1. If the shape of a cluster is close to a circle, then F is closer to 1, and the cluster is noise. Conversely, if the shape of a region is close to a linear shape, F is close to 0, and the region is a crack.

[0087] like Figure 4 As shown, Figure 4 -a represents the original image. Figure 4 -b represents the image information processed by the second image processing unit.

[0088] Finally, it should be reiterated that the constituent elements / components described in the foregoing embodiments of the present invention are merely illustrative examples and are not intended to limit the scope of the present invention. Substitutions or variations of other equivalent components should be within the scope of protection of the present invention.

Claims

1. A concrete stability testing system based on image processing, characterized in that, The concrete stability testing system includes a light intensity testing module, an image acquisition module, a concrete stability testing module, and a stability result export module. The output terminals of the light intensity testing module and the image acquisition module are connected to the input terminal of the concrete stability testing module, and the output terminal of the concrete stability testing module is connected to the input terminal of the stability result export module. The light intensity testing module monitors the ambient light intensity of the concrete stability test environment, while the image acquisition module acquires wall image information. Both the light intensity information and the wall image information are transmitted to the concrete stability testing module. The concrete stability testing module selects the corresponding image processing unit to process the image information based on the received light intensity information. The concrete stability testing module determines the stability of the wall based on the processed image information. If the length of the crack is greater than a preset threshold, the wall is determined to be in an unstable state. The concrete stability testing module then transmits the test results to the stability result export module.

2. The image processing-based concrete stability testing system according to claim 1, characterized in that, The concrete stability testing module includes a signal receiving unit, an analysis and selection unit, a raw image storage unit, a raw image sending unit, a first image processing unit, a second image processing unit, and a stability analysis unit. The output terminals of the light intensity testing module and the image acquisition module are both connected to the input terminals of the signal receiving unit. The output terminal of the signal receiving unit is connected to the input terminal of the raw image storage unit. The output terminal of the raw image storage unit is connected to the input terminal of the raw image sending unit. The output terminal of the signal receiving unit is connected to the input terminal of the analysis and selection unit. The output terminal of the analysis and selection unit is connected to the input terminal of the first image processing unit. The output terminal of the analysis and selection unit is connected to the input terminal of the second image processing unit. The output terminals of both the first and second image processing units are connected to the input terminal of the stability analysis unit. The output terminal of the stability analysis unit is connected to the input terminal of the stability result export module. The light intensity testing module and the image acquisition module transmit image information to the original image storage unit and light intensity information to the analysis and selection unit through the signal receiving unit. The analysis and selection unit stores a light intensity threshold. If the received light intensity information is less than the light intensity threshold, the original image storage unit transmits the image information to the first image processing unit. If the received light intensity information is greater than or equal to the light intensity threshold, the original image storage unit transmits the image information to the second image processing unit. The image processed by the first or second image processing unit is transmitted to the stability analysis unit. The stability analysis unit determines the stability of the wall based on the received image information. If the length of the crack is greater than a preset threshold, the wall is determined to be in an unstable state. The stability analysis unit transmits the test results to the stability result export module.

3. The image processing-based concrete stability testing system according to claim 2, characterized in that, The first image processing unit processes the received image as follows: S1: Ray tracing steps, described by the following integral equation for light propagation. L o (x,ω o )=L E (x,ω o )+∫ Ω L i (x,ω i )f r (x,ω i ,oh o )(oh i ·n)dω i Where x is a spatial variable, ω o It is the direction of the emitted light ray, L o (x,ω o ) is from point x along direction ω o The total spectral radiance emitted, L E (x,ω o ) is from point x along direction ω o The emitted reference spectral radiance, Ω, is at x,ω i The unit hemisphere along the direction of the normal vector n, x, ω i It is the direction of the incident ray, L i From direction ω i Spectral radiance reaching x inward, f r (x,ω i ,ω o ) is the bidirectional reflection distribution function (at x, from direction ω) i Reflected to ω o (the proportion of light); in: Where f(x) is an arbitrary function, and X is a set of N samples uniformly distributed in Ω; S2: Enhance the shadow mask step, using standard image transformation to enhance the shadow mask dataset, generating several unique shadow masks with random rotation parameters between 0 and 360 degrees; S3: The shadow blending step enhances concrete crack images by combining images using overlay techniques. This involves blending one image with another to create a single image that incorporates features from both source images.

4. The image processing-based concrete stability testing system according to claim 3, characterized in that, The second image processing unit processes the received image as follows: D1: Preprocessing, converting image information to grayscale using the following formula: I(R,G,B)=0.299*R+0.587*G+0.144*B R, G, and B represent the values ​​of the red, green, and blue channels, respectively; Adjust the average grayscale value of the image to between 180 and 190, and calculate the compensation value for each pixel using the following formula: The original grayscale image is represented using a two-dimensional matrix: F M×N =[f(x,y)] M×N M×N is the rotation of the image, f(x,y) is the gray value of pixel (x,y), A is a constant value that the preset image average value reaches, and then C is added to each pixel of the image; Operate on each pixel in the image using the following formula: f(x,y) represents the input grayscale value, and g(x,y) is the output grayscale value. [X1,X2] is the preset range, and β and γ are both preset parameters. Use the following formula to increase the contrast between the crack and the background: Where f(x,y) is the original gray value of pixel (x,y); D2: Image segmentation, the image segmentation formula is as follows: Where T is the segmentation threshold; D3: Crack detection, expressed by the following formula: Where A is the number of pixels in the cluster, C is its perimeter, and F ranges from 0 to 1. If the shape of a cluster is close to a circle, then F is closer to 1, and the cluster is noise. Conversely, if the shape of a region is close to a linear shape, F is close to 0, and the region is a crack.

Images