Hydropower station dam crack defect detection method, device, equipment and medium

Abstract

The invention relates to the technical field of hydropower stations, and discloses a hydropower station dam crack defect detection method, device, equipment and medium, the maximum value of a ridge variation coefficient is calculated through single-layer discrete two-dimensional wavelet decomposition and ridge wave transformation, and then the real crack edge direction is determined according to the maximum value of the ridge variation coefficient. The edge feature enhancement effect is improved, and the edge boundary is clearer. And further, constructing a crack edge image according to the plurality of pixels corresponding to the absolute values of the plurality of gradients in the crack edge direction, and determining a crack defect detection result of the hydropower station dam to be detected according to the constructed crack edge image. Therefore, by implementing the method, the edge feature is enhanced, so that the edge strength and the recognition rate of image recognition are improved, and the accuracy of a crack defect detection result is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower stations, and in particular to a method, device, equipment and medium for detecting crack defects in a dam of a hydropower station. Background Art

[0002] Crack defect detection on hydropower station dams, especially crack defect detection on the underwater part of the dam, generally adopts sonar detection method combined with computer-aided image recognition technology to timely discover crack defects on the dam.

[0003] To improve the accuracy of sonar detection image recognition, existing technologies are based on transform domains, such as wavelet transform (WT) to achieve accurate recognition of local details in space. However, the wavelet transform method only performs well in representing point singularities, fails to take into account the geometric characteristics of the structure, and does not utilize the regularity of the defect edge. To solve this problem, technicians apply the curve transformation (CVT) method to enhance the contrast of the image recognition process and have good adaptability in terms of sensitivity and anisotropy.

[0004] However, when detecting crack defects in hydropower station dams, the edges of the cracks are mostly sloped. The existing curve transformation denoising method will destroy these sloped edges, resulting in unclear or even distorted edge boundaries detected by sonar images, which in turn leads to low accuracy of crack defect detection results. Summary of the invention

[0005] In view of this, the present invention provides a method, device, equipment and medium for detecting crack defects in a hydropower station dam, so as to solve the problem that the existing curve transformation denoising method will destroy these slope edges, resulting in unclear or even distorted edge boundaries detected by sonar images, and further resulting in low accuracy of crack defect detection results.

[0006] In a first aspect, the present invention provides a method for detecting crack defects in a dam of a hydropower station, the method comprising:

[0007] The original image of the hydropower station dam to be inspected is obtained; based on the original image, the maximum value of the ridge variation coefficient is obtained through single-layer discrete two-dimensional wavelet decomposition and ridgelet transform; the crack edge direction is determined based on the maximum value of the ridge variation coefficient; a crack edge image is constructed based on multiple pixels corresponding to the absolute values ​​of multiple gradients in the crack edge direction; and the crack defect detection result of the hydropower station dam to be inspected is determined based on the crack edge image.

[0008] The method for detecting crack defects in a hydropower station dam provided by the present invention calculates the maximum value of the ridge variation coefficient by single-layer discrete two-dimensional wavelet decomposition and ridgelet transform, and then determines the true crack edge direction according to the maximum value of the ridge variation coefficient, thereby improving the edge feature enhancement effect and making the edge boundary clearer. Furthermore, a crack edge image is constructed based on multiple pixels corresponding to the absolute values ​​of multiple gradients in the crack edge direction, and the crack defect detection result of the hydropower station dam to be detected is determined based on the constructed crack edge image. Therefore, by implementing the present invention, the edge strength and recognition rate of image recognition are improved by enhancing the edge features, thereby improving the accuracy of the crack defect detection result.

[0009] In an optional implementation, based on the original image, a single-layer discrete two-dimensional wavelet decomposition and a ridgelet transform are performed to obtain the maximum value of the ridge transformation coefficient, including:

[0010] The original image is subjected to single-layer discrete two-dimensional wavelet decomposition and equalization processing to obtain the target image; the target image is subjected to ridgelet transform to obtain the maximum value of the ridge transformation coefficient.

[0011] The method for detecting crack defects in a hydropower station dam provided by the present invention obtains a processed target image by performing single-layer discrete two-dimensional wavelet decomposition and equalization processing on the original image, and then performs ridgelet transform on the target image to calculate the corresponding maximum value of the ridge transformation coefficient, thereby providing support for the subsequent determination of the true crack edge direction.

[0012] In an optional implementation, performing single-layer discrete two-dimensional wavelet decomposition and equalization processing on the original image to obtain a target image includes:

[0013] The original image is decomposed by a single-layer discrete two-dimensional wavelet to obtain low-frequency subband decomposition coefficients, horizontal high-frequency subband decomposition coefficients, vertical high-frequency subband decomposition coefficients and diagonal high-frequency subband decomposition coefficients; the low-frequency subband decomposition coefficients are equalized to obtain low-frequency subband equalization decomposition coefficients; the original image is reconstructed using the low-frequency subband equalization decomposition coefficients, horizontal high-frequency subband decomposition coefficients, vertical high-frequency subband decomposition coefficients and diagonal high-frequency subband decomposition coefficients to obtain the target image.

[0014] The hydropower station dam crack defect detection method provided by the present invention can realize the reconstruction of the original image through a single-layer discrete two-dimensional wavelet decomposition and equalization processing method, thereby improving the image accuracy.

[0015] In an optional implementation, performing ridgelet transform on the target image to obtain the maximum value of the ridgelet coefficient includes:

[0016] The target image is divided into blocks to obtain multiple sub-images; the two-dimensional image signal of each sub-image is subjected to Randon transformation to obtain multiple Randon transformation data; the multiple Randon transformation data are subjected to wavelet transformation to obtain multiple ridge transformation coefficients; and the maximum value of the ridge transformation coefficient is determined according to the multiple ridge transformation coefficients.

[0017] The hydropower station dam crack defect detection method provided by the present invention can calculate the corresponding maximum value of the ridge variation coefficient through ridgelet transformation, which provides support for the subsequent determination of the true crack edge direction.

[0018] In an optional implementation, determining the crack edge direction based on the maximum value of the ridge variation coefficient includes: calculating the azimuth angle based on the maximum value of the ridge variation coefficient; and determining the crack edge direction based on the azimuth angle.

[0019] The method for detecting crack defects in a hydropower station dam provided by the present invention can determine the true crack edge direction according to the azimuth angle corresponding to the maximum value of the ridge variation coefficient, thereby improving the edge feature enhancement effect and making the edge boundary clearer.

[0020] In a second aspect, the present invention provides a device for detecting crack defects in a dam of a hydropower station, the device comprising:

[0021] The acquisition module is used to acquire the original image of the hydropower station dam to be detected; the processing module is used to obtain the maximum value of the ridge variation coefficient based on the original image through single-layer discrete two-dimensional wavelet decomposition and ridgelet transform; the first determination module is used to determine the crack edge direction based on the maximum value of the ridge variation coefficient; the construction module is used to construct a crack edge image according to multiple pixels corresponding to the absolute values ​​of multiple gradients in the crack edge direction; the second determination module is used to determine the crack defect detection result of the hydropower station dam to be detected based on the crack edge image.

[0022] In an optional embodiment, the processing module includes:

[0023] The processing submodule is used to perform single-layer discrete two-dimensional wavelet decomposition and equalization processing on the original image to obtain the target image; the transformation submodule is used to perform ridgelet transformation on the target image to obtain the maximum value of the ridge transformation coefficient.

[0024] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for detecting crack defects in a hydropower station dam according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for detecting crack defects in a hydropower station dam according to the first aspect or any corresponding embodiment thereof.

[0026] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for detecting crack defects in a hydropower station dam according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 is a schematic flow chart of a method for detecting crack defects in a hydropower station dam according to an embodiment of the present invention;

[0029] Figure 2 is a schematic flow chart of another method for detecting crack defects in a dam of a hydropower station according to an embodiment of the present invention;

[0030] Figure 3 is a schematic flow chart of another method for detecting crack defects in a hydropower station dam according to an embodiment of the present invention;

[0031] Figure 4 is a structural block diagram of a hydropower station dam crack defect detection device according to an embodiment of the present invention;

[0032] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0034] An embodiment of the present invention provides a method for detecting crack defects in a hydropower station dam, which determines the true crack edge direction by the maximum value of the ridge variation coefficient to enhance the edge features, improve the edge strength and recognition rate of image recognition, and thus improve the accuracy of the crack defect detection results.

[0035] According to an embodiment of the present invention, an embodiment of a method for detecting crack defects in a dam of a hydropower station is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] In this embodiment, a method for detecting crack defects in a hydropower station dam is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 1 FIG. 1 is a flow chart of a method for detecting crack defects in a hydropower station dam according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0037] Step S101, obtaining an original image of a hydropower station dam to be inspected.

[0038] Specifically, the original image of the hydropower station dam to be inspected can be collected through high-definition image sonar technologies such as cameras and sonar detection.

[0039] Step S102: Based on the original image, single-layer discrete two-dimensional wavelet decomposition and ridgelet transform are performed to obtain the maximum value of the ridge transformation coefficient.

[0040] Among them, single-layer discrete two-dimensional wavelet decomposition is a signal processing method that decomposes a two-dimensional signal (such as an image) into multiple frequency bands, each of which consists of different frequencies and amplitudes.

[0041] Furthermore, the Ridgelet Transform is a transformation method used for image denoising, which is based on the extension of the wavelet transform and can better capture the local features in the image.

[0042] Specifically, single-layer discrete two-dimensional wavelet decomposition can effectively capture features such as edges and textures in the image. Furthermore, the ridgelet transform can decompose the image into ridge coefficients of different scales and directions and obtain the maximum value of the ridge coefficient.

[0043] Step S103, determining the crack edge direction based on the maximum value of the ridge variation coefficient.

[0044] Specifically, the direction of the crack edge can be inferred by using the information corresponding to the direction of the crack edge determined based on the maximum value of the ridge variation coefficient. Generally speaking, if the maximum value of the ridge variation coefficient appears in a certain direction, it is likely that the crack has more obvious characteristics in this direction.

[0045] For example, when processing the original image of the road surface, if the maximum value of the ridge coefficient appears in the horizontal direction, it may mean that the crack mainly extends in the horizontal direction. In the detection image of the bridge structure, if the maximum value appears in the oblique direction, it implies that the edge direction of the crack is inclined.

[0046] Furthermore, the crack edge direction can be determined more accurately by combining relevant algorithms.

[0047] Step S104 , constructing a crack edge image according to a plurality of pixels corresponding to the absolute values ​​of a plurality of gradients in the crack edge direction.

[0048] Specifically, when constructing the crack edge image, the absolute value of the gradient of each pixel is accurately calculated and screened. Pixels with larger absolute values ​​of gradient are more likely to be located at the edge of the crack. By effectively connecting and combining these pixels, the general shape of the crack can be preliminarily outlined.

[0049] For example, when processing an image of a dam surface, the absolute values ​​of the gradients of a series of pixels are calculated along a specific crack edge direction. Among them, the absolute values ​​of the gradients of some pixels are significantly higher than those of the surrounding pixels. By connecting these prominent pixels, a clear crack edge line can be initially depicted.

[0050] In this embodiment, the corresponding crack edge image can be constructed by comparing pixels near the absolute values ​​of multiple gradients in the crack edge direction.

[0051] Step S105, determining the crack defect detection result of the hydropower station dam to be detected according to the crack edge image.

[0052] Specifically, the crack edge image can represent the direction of the crack edge. The inside of the edge is the crack, and the outside of the edge is the good area. Therefore, further evaluation and judgment of the crack morphology inside the edge can obtain the crack defect detection result of the hydropower station dam to be inspected.

[0053] The method for detecting crack defects in a hydropower station dam provided in this embodiment calculates the maximum value of the ridge variation coefficient by single-layer discrete two-dimensional wavelet decomposition and ridgelet transform, and then determines the true crack edge direction according to the maximum value of the ridge variation coefficient, thereby improving the edge feature enhancement effect and making the edge boundary clearer. Furthermore, a crack edge image is constructed based on multiple pixels corresponding to the absolute values ​​of multiple gradients in the crack edge direction, and the crack defect detection result of the hydropower station dam to be detected is determined based on the constructed crack edge image. Therefore, by implementing the present invention, the edge strength and recognition rate of image recognition are improved by enhancing the edge features, thereby improving the accuracy of the crack defect detection result.

[0054] In this embodiment, a method for detecting crack defects in a hydropower station dam is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 2 FIG. 1 is a flow chart of a method for detecting crack defects in a hydropower station dam according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0055] Step S201, obtain the original image of the hydropower station dam to be detected. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0056] Step S202: Based on the original image, single-layer discrete two-dimensional wavelet decomposition and ridgelet transform are performed to obtain the maximum value of the ridge transformation coefficient.

[0057] Specifically, the above step S202 includes:

[0058] Step S2021, performing single-layer discrete two-dimensional wavelet decomposition and equalization processing on the original image to obtain a target image.

[0059] Specifically, the original image can be decomposed into sub-bands of different frequencies and directions through single-layer discrete two-dimensional wavelet decomposition. It is like breaking a complex image into multiple simple parts, which can more clearly see the characteristics of the image at different scales and directions.

[0060] The equalization process focuses on adjusting the pixel distribution of the image so that the contrast of the image is enhanced and the originally darker or brighter areas can show more details.

[0061] For example, for an original image of a landscape with dark lighting, after the above processing, the outlines and textures of trees that were not clear in the shadows may become more obvious in the target image.

[0062] Therefore, the original image can be processed by combining single-layer discrete two-dimensional wavelet decomposition and equalization to obtain the processed target image.

[0063] In some optional implementations, the above step S2021 includes:

[0064] Step a1, performing a single-layer discrete two-dimensional wavelet decomposition on the original image to obtain low-frequency sub-band decomposition coefficients, horizontal high-frequency sub-band decomposition coefficients, vertical high-frequency sub-band decomposition coefficients and diagonal high-frequency sub-band decomposition coefficients.

[0065] Step a2: performing equalization processing on the low-frequency sub-band decomposition coefficients to obtain low-frequency sub-band equalization decomposition coefficients.

[0066] Step a3, reconstructing the original image using the low-frequency sub-band equalization decomposition coefficients, the horizontal high-frequency sub-band decomposition coefficients, the vertical high-frequency sub-band decomposition coefficients and the diagonal high-frequency sub-band decomposition coefficients to obtain the target image.

[0067] Among them, the low-frequency subband decomposition coefficients usually contain the general outline and main information of the image; the horizontal high-frequency subband decomposition coefficients reflect the detail changes and edge information of the image in the horizontal direction; the vertical high-frequency subband decomposition coefficients reflect the details and edges in the vertical direction; the diagonal high-frequency subband decomposition coefficients describe the high-frequency information of the image in the diagonal direction. For example, in an image of a mountain range, the low-frequency subband may show the general outline of the mountain range, the horizontal high-frequency subband may highlight the horizontal edge of the mountain range and the sky, the vertical high-frequency subband may highlight the vertical texture of the mountain range, and the diagonal high-frequency subband may show some oblique details.

[0068] Specifically, the original image is decomposed by a single-layer discrete two-dimensional wavelet to obtain the decomposition coefficients of the low-frequency subband c A, corresponding to the three high-frequency sub-band decomposition coefficients in the horizontal, vertical and diagonal directions respectively c H, cV, cD.

[0069] Furthermore, the low-frequency subband decomposition coefficients c A is equalized to obtain a new equalization coefficient. The equalization process can enhance the contrast and brightness distribution of the low-frequency part, making the overall brightness of the image more uniform, thereby highlighting some originally less obvious features.

[0070] Furthermore, using the new equalization coefficients and high frequency subband decomposition coefficients c H, c V, c D reconstructs the original image and obtains the reconstructed target image. Through reconstruction, the information of each sub-band can be integrated, so that the final target image retains the main features of the original image and improves the image quality and visibility through the equalization processing of the low-frequency sub-band.

[0071] Step S2022, performing ridgelet transform on the target image to obtain the maximum value of the ridgelet coefficient.

[0072] In some optional implementations, the above step SS2022 includes:

[0073] Step b1, dividing the target image into blocks to obtain multiple sub-images.

[0074] Step b2: performing Randon transform on the two-dimensional image signal of each sub-image to obtain a plurality of Randon transform data.

[0075] Step b3, performing wavelet transform on multiple Randon transform data to obtain multiple ridge transformation coefficients.

[0076] Step b4, determining the maximum value of the ridge variation coefficient based on multiple ridge variation coefficients.

[0077] First, the entire target image is divided into blocks so that the singular boundaries in each small image block can be approximated by a straight line.

[0078] Secondly, the two-dimensional image signal of each sub-image block is subjected to Randon transform, as shown in the following equation (1):

[0079] R{f(x,y)}=∫∫f(x,y)δ(tx cosθ-y sinθ)dxdy=pθ(t)(1)

[0080] Where: f(x, y) represents the two-dimensional image signal; t represents the distance along the straight line; θ represents the rotation angle; δ represents the Dirac function; the projection pθ(t) represents the integral along a series of parallel lines (projection lines), and the Randon transform is the set of all projections {pθ(t), θ∈(0,π]}.

[0081] Furthermore, based on the above relationship (1), the original image that has not been rotated is first subjected to Randon transformation (angle is 0 degrees) to obtain a reference vector, which is used as the original reference vector R0, in preparation for the next step of comparison;

[0082] Furthermore, a set of 360 Randon transforms are performed on the image to be detected, and the projection angle is transformed from 0° to 359°, with an increment step of 1° each time. Executing 360 Randon transforms can obtain 360 Randon transform vectors R(θ), θ∈[0,359].

[0083] Furthermore, the obtained 360 detection vectors R(θ) are compared with the reference vector R0, and the correlation coefficient NC(i) between each vector to be detected and R0 is calculated using the following relationship (2):

[0084]

[0085] Furthermore, 360 correlation coefficients can be calculated through the above relationship (3), where the projection angle corresponding to the maximum value of the correlation coefficient is the rotation attack angle that the image to be detected may have been subjected to, represented by θ. Then the image is rotated in the opposite direction of θ, that is, the image subjected to the rotation attack can be rotationally corrected.

[0086] Secondly, the data of Randon transformation is subjected to wavelet transformation to obtain the ridge coefficient of the two-dimensional image, as shown in the following equation (3):

[0087]

[0088] Then: a represents the scale parameter of a ridge wave; b represents the position parameter; CRTf(a, b, θ) represents the ridge variation coefficient; It represents the ridgelet generated by the admissible condition and is a basic function generator of separable variables, which can generate a set of target-oriented ridgelet families.

[0089] Finally, the maximum value α is calculated by the ridge coefficient on each angle line max (a, b, θ), as shown in the following relation (4):

[0090] α max (a, b, θ) = argmax θ α Q，y (a,b,θ) (4)

[0091] Among them, α Q，y (a, b, θ) is the ridge transformation coefficient CRTf(a, b, θ) calculated by the above relationship (3).

[0092] Step S203, determine the crack edge direction based on the maximum value of the ridge variation coefficient. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0093] Step S204: construct a crack edge image based on multiple pixels corresponding to the absolute values ​​of multiple gradients in the crack edge direction. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0094] Step S205, determining the crack defect detection result of the hydropower station dam to be detected according to the crack edge image. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0095] The method for detecting crack defects in a hydropower station dam provided in this embodiment can realize the reconstruction of the original image through a single-layer discrete two-dimensional wavelet decomposition and equalization processing method, thereby improving the image accuracy. Then, the corresponding maximum value of the ridge transformation coefficient can be calculated by performing a ridgelet transform on the reconstructed target image, and then the true crack edge direction is determined according to the maximum value of the ridge transformation coefficient, thereby improving the edge feature enhancement effect and making the edge boundary clearer. Furthermore, a crack edge image is constructed according to multiple pixels corresponding to the absolute values ​​of multiple gradients in the crack edge direction, and the crack defect detection result of the hydropower station dam to be detected is determined according to the constructed crack edge image. Therefore, by implementing the present invention, the edge strength and recognition rate of image recognition are improved by enhancing the edge features, thereby improving the accuracy of the crack defect detection result.

[0096] In this embodiment, a method for detecting crack defects in a hydropower station dam is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 3 FIG. 1 is a flow chart of a method for detecting crack defects in a hydropower station dam according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0097] Step S301, obtain the original image of the hydropower station dam to be detected. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0098] Step S302: Based on the original image, single-layer discrete two-dimensional wavelet decomposition and ridgelet transform are performed to obtain the maximum value of the ridge transformation coefficient. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0099] Step S303, determining the crack edge direction based on the maximum value of the ridge variation coefficient.

[0100] Specifically, the above step S303 includes:

[0101] Step S3031, calculating the azimuth angle based on the maximum value of the ridge variation coefficient.

[0102] Specifically, the azimuth angle θ can be calculated by applying the gradient of the maximum value A , as shown in the following relation (5):

[0103]

[0104] Where: Represents the gradient of any point on the ridge line in two-dimensional coordinates;

[0105] Step S3032, determining the crack edge direction based on the azimuth angle.

[0106] Specifically, according to the azimuth angle θA Determine the crack edge direction As shown in the following relation (6):

[0107]

[0108] Where: Q max represents the edge azimuth θ A The maximum point.

[0109] Step S304: construct a crack edge image based on multiple pixels corresponding to the absolute values ​​of multiple gradients in the crack edge direction. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0110] Step S305, determine the crack defect detection result of the hydropower station dam to be detected according to the crack edge image. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0111] The method for detecting crack defects in a hydropower station dam provided in this embodiment calculates the maximum value of the ridge variation coefficient through single-layer discrete two-dimensional wavelet decomposition and ridgelet transform, and then determines the true crack edge direction according to the azimuth corresponding to the maximum value of the ridge variation coefficient, thereby improving the edge feature enhancement effect and making the edge boundary clearer. Further, a crack edge image is constructed based on multiple pixels corresponding to the absolute values ​​of multiple gradients in the crack edge direction, and the crack defect detection result of the hydropower station dam to be detected is determined based on the constructed crack edge image. Therefore, by implementing the present invention, the edge strength and recognition rate of image recognition are improved by enhancing the edge features, thereby improving the accuracy of the crack defect detection result.

[0112] In this embodiment, a hydropower station dam crack defect detection device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0113] This embodiment provides a hydropower station dam crack defect detection device, such as Figure 4 As shown, including:

[0114] The acquisition module 401 is used to acquire the original image of the hydropower station dam to be detected.

[0115] The processing module 402 is used to obtain the maximum value of the ridge transformation coefficient based on the original image through single-layer discrete two-dimensional wavelet decomposition and ridgelet transformation.

[0116] The first determination module 403 is used to determine the crack edge direction based on the maximum value of the ridge variation coefficient.

[0117] The construction module 404 is used to construct a crack edge image according to a plurality of pixels corresponding to the absolute values ​​of a plurality of gradients in the crack edge direction.

[0118] The second determination module 405 is used to determine the crack defect detection result of the hydropower station dam to be detected according to the crack edge image.

[0119] In some optional implementations, the processing module 402 includes:

[0120] The processing submodule is used to perform single-layer discrete two-dimensional wavelet decomposition and equalization processing on the original image to obtain the target image.

[0121] The transformation submodule is used to perform ridgelet transformation on the target image to obtain the maximum value of the ridge transformation coefficient.

[0122] In some optional implementations, the processing submodule includes:

[0123] The decomposition unit is used to perform a single-layer discrete two-dimensional wavelet decomposition on the original image to obtain low-frequency sub-band decomposition coefficients, horizontal high-frequency sub-band decomposition coefficients, vertical high-frequency sub-band decomposition coefficients and diagonal high-frequency sub-band decomposition coefficients.

[0124] The processing unit is used to perform equalization processing on the low-frequency sub-band decomposition coefficients to obtain low-frequency sub-band equalization decomposition coefficients.

[0125] The reconstruction unit is used to reconstruct the original image by using the low-frequency sub-band equalization decomposition coefficient, the horizontal high-frequency sub-band decomposition coefficient, the vertical high-frequency sub-band decomposition coefficient and the diagonal high-frequency sub-band decomposition coefficient to obtain the target image.

[0126] In some optional implementations, the transformation submodule includes:

[0127] The block division unit is used to divide the target image into blocks to obtain multiple sub-images.

[0128] The first transform unit is used to perform Randon transform on the two-dimensional image signal of each sub-image to obtain a plurality of Randon transform data.

[0129] The second transform unit is used to perform wavelet transform on the multiple Randon transform data to obtain multiple ridge transform coefficients.

[0130] The determination unit is used to determine the maximum value of the ridge variation coefficient according to multiple ridge variation coefficients.

[0131] In some optional implementations, the first determining module 403 includes:

[0132] The calculation submodule is used to calculate the azimuth angle based on the maximum value of the ridge variation coefficient.

[0133] The determination submodule is used to determine the crack edge direction based on the azimuth angle.

[0134] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0135] The hydropower station dam crack defect detection device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0136] The embodiment of the present invention also provides a computer device having the above Figure 4 The hydropower station dam crack defect detection device shown.

[0137] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0138] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0139] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0140] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0141] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0142] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0143] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0144] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0145] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for detecting crack defects in a hydropower station dam, characterized in that: The method comprises: Obtain the original image of the hydropower station dam to be inspected; Based on the original image, a single-layer discrete two-dimensional wavelet decomposition and a ridgelet transform are performed to obtain a maximum value of a ridge transformation coefficient; Determining the crack edge direction based on the maximum value of the ridge variation coefficient; constructing a crack edge image according to a plurality of pixels corresponding to the absolute values ​​of a plurality of gradients in the crack edge direction; The crack defect detection result of the hydropower station dam to be detected is determined according to the crack edge image.

2. The method according to claim 1, characterized in that Based on the original image, a single-layer discrete two-dimensional wavelet decomposition and ridgelet transform are performed to obtain the maximum value of the ridge transformation coefficient, including: Performing single-layer discrete two-dimensional wavelet decomposition and equalization processing on the original image to obtain a target image; Performing ridgelet transform on the target image to obtain the maximum value of the ridgelet coefficient.

3. The method according to claim 2, characterized in that The original image is subjected to single-layer discrete two-dimensional wavelet decomposition and equalization processing to obtain a target image, including: Performing single-layer discrete two-dimensional wavelet decomposition on the original image to obtain low-frequency sub-band decomposition coefficients, horizontal high-frequency sub-band decomposition coefficients, vertical high-frequency sub-band decomposition coefficients, and diagonal high-frequency sub-band decomposition coefficients; Performing equalization processing on the low-frequency sub-band decomposition coefficients to obtain low-frequency sub-band equalization decomposition coefficients; The original image is reconstructed using the low-frequency sub-band equalization decomposition coefficients, the horizontal high-frequency sub-band decomposition coefficients, the vertical high-frequency sub-band decomposition coefficients and the diagonal high-frequency sub-band decomposition coefficients to obtain the target image.

4. The method according to claim 2, characterized in that: Performing ridgelet transform on the target image to obtain the maximum value of the ridgelet coefficient includes: Dividing the target image into blocks to obtain multiple sub-images; Performing Randon transformation on the two-dimensional image signal of each sub-image to obtain a plurality of Randon transformation data; Performing wavelet transform on the plurality of Randon transform data to obtain a plurality of ridge transformation coefficients; The maximum value of the ridge variation coefficient is determined according to the multiple ridge variation coefficients.

5. The method according to claim 1, characterized in that Determining the crack edge direction based on the maximum value of the ridge variation coefficient includes: Calculating the azimuth angle based on the maximum value of the ridge variation coefficient; The crack edge direction is determined based on the azimuth angle.

6. A hydropower station dam crack defect detection device, characterized in that: The device comprises: An acquisition module, used for acquiring the original image of the hydropower station dam to be inspected; A processing module, used for obtaining the maximum value of the ridge transformation coefficient based on the original image through single-layer discrete two-dimensional wavelet decomposition and ridgelet transformation; A first determination module, used to determine the crack edge direction based on the maximum value of the ridge variation coefficient; A construction module, used to construct a crack edge image according to a plurality of pixels corresponding to the absolute values ​​of a plurality of gradients in the crack edge direction; The second determination module is used to determine the crack defect detection result of the hydropower station dam to be detected according to the crack edge image.

7. The device according to claim 6, characterized in that The processing module comprises: A processing submodule, used for performing single-layer discrete two-dimensional wavelet decomposition and equalization processing on the original image to obtain a target image; The transformation submodule is used to perform ridgelet transformation on the target image to obtain the maximum value of the ridgelet coefficient.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for detecting crack defects in a hydropower station dam according to any one of claims 1 to 5 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for detecting crack defects in a hydropower station dam according to any one of claims 1 to 5.

10. A computer program product, characterized in that It comprises computer instructions, and the computer instructions are used to enable a computer to execute the hydropower station dam crack defect detection method according to any one of claims 1 to 5.

Images