Method and device for detecting concrete surface cracks in high-temperature environment

Abstract

The invention relates to the technical field of concrete crack detection, and discloses a concrete surface crack detection method, device and equipment in a high-temperature environment and a computer program product. The method comprises the following steps: acquiring images of different wavelength information on the concrete surface based on a multispectral sensor; registering the images acquired from multiple angles by using a feature point matching algorithm; calculating to obtain local brightness feature data; fine processing is carried out on the crack edge by combining a Canny edge detection algorithm and multi-scale morphological operation, noise is removed, and boundary breakpoints are repaired; the width and the length of the crack are directly measured by calculating the pixel distance between the crack edges. Compared with the problem of insufficient adaptability to a complex environment in the prior art, the method improves the detection precision and real-time performance of the crack under a high-temperature condition through the combination of multispectral imaging, a local adaptive threshold algorithm and morphology, solves the problem of unstable crack detection under a hot and humid environment, and effectively improves the detection efficiency.

Description

Technical Field

[0001] This invention belongs to the field of concrete crack detection technology, and particularly relates to a method and device for detecting cracks on the surface of concrete under high temperature conditions. Background Technology

[0002] Currently, technologies for detecting cracks on concrete surfaces in high-temperature environments suffer from insufficient accuracy and poor adaptability. For example, traditional crack detection methods mostly rely on single visible light imaging, which is easily affected by changes in concrete surface temperature and uneven illumination, leading to reduced accuracy in crack edge identification and increased noise. Existing technologies cannot fully meet the needs for real-time crack detection and accurate measurement in high-temperature environments, especially when temperature fluctuations are large, as sensor and image quality are significantly interfered with, affecting the accurate identification and measurement of cracks. Therefore, there is an urgent need for a method that can achieve high-precision, real-time crack detection under high-temperature conditions to improve the stability and adaptability of crack detection, thereby ensuring the requirements for safety assessment of concrete structures in high-temperature environments. Summary of the Invention

[0003] To address the aforementioned technical shortcomings, the present invention aims to propose a method for detecting cracks on concrete surfaces under high-temperature conditions. This method addresses the technical problem that existing technologies cannot adequately meet the requirements for real-time detection and accurate measurement of cracks under high-temperature conditions, particularly when temperature fluctuations are significant, as sensor and image quality are severely affected, impacting the accurate identification and measurement of cracks.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for detecting cracks on the surface of concrete under high temperature conditions.

[0005] The method includes:

[0006] Step S10: Based on the multispectral sensor, images of the concrete surface are collected from the front, left, right and top angles to obtain images containing information of four different wavelengths: visible light, near infrared, mid infrared and far infrared.

[0007] Step S20: Use an improved feature point matching algorithm to spatially align images from different angles to generate crack region images with a resolution of 0.1 mm per pixel;

[0008] Step S30: Calculate the mean and standard deviation of pixels in the image based on the local region of the crack area image to obtain local brightness feature data, determine the local threshold based on the local brightness feature data, and segment the crack area to form preliminary crack contour data;

[0009] Step S40: Based on the preliminary crack contour data, the edge data of the crack is obtained by performing edge refinement processing using the improved Canny edge detection algorithm. Noise in the crack edge data is removed by multi-scale morphological operations, and boundary breakpoints are repaired to obtain complete crack contour data.

[0010] Step S50: Measure the width and length of the crack directly by calculating the pixel distance between the crack edges, and generate a crack size report based on these measurements.

[0011] Preferably, the step of removing noise from the crack edge data through multi-scale morphological operations and repairing boundary breakpoints to obtain complete crack contour data includes:

[0012] Morphological dilation of crack edge images is performed using structuring elements of different scales to remove isolated noise points and connect fractured crack boundaries. Combined with changes in morphological structure at different scales, complete crack contour data is extracted and generated.

[0013] Preferably, step S20 further includes using a scale-invariant feature transformation algorithm to identify feature points in the image.

[0014] Preferably, in step S40, the edge refinement process includes using dynamic threshold adjustment during crack edge detection.

[0015] Preferably, the crack size report generated in step S50 includes the crack width, length, and crack propagation trend analysis results, which are used to assess the safety of the structure.

[0016] Preferably, step S10 further includes using an adaptive light intensity adjustment method during image acquisition to ensure consistent image quality under different lighting conditions.

[0017] Preferably, the noise in the crack edge data in step S40 includes surface texture inhomogeneity, ambient light variation, transmission impurities, and sensor thermal noise.

[0018] The present invention also provides a device for detecting cracks on the surface of concrete under high temperature conditions, comprising:

[0019] The multispectral acquisition module is used to acquire images of the concrete surface from the front, left, right and top angles based on the multispectral sensor, and obtain images containing information of four different wavelengths: visible light, near infrared, mid infrared and far infrared.

[0020] The image registration module is used to spatially align images from different angles using an improved feature point matching algorithm to generate crack region images with a resolution of 0.1 mm per pixel.

[0021] The local feature analysis module is used to calculate the mean and standard deviation of pixels in the image based on the local region in the crack region image to obtain local brightness feature data, determine the local threshold based on the local brightness feature data, and segment the crack region to form preliminary crack contour data.

[0022] The edge processing module is used to refine the edge data of the crack by combining the preliminary crack contour data with the improved Canny edge detection algorithm, remove noise in the crack edge data through multi-scale morphological operations, and repair boundary breakpoints to obtain complete crack contour data.

[0023] The size measurement module is used to directly measure the width and length of the crack by calculating the pixel distance between the crack edges, and generate a crack size report based on these measurement results.

[0024] This invention also provides a device for detecting cracks on concrete surfaces under high-temperature conditions, comprising:

[0025] The system includes a memory, a processor, and a high-temperature environment concrete surface crack detection program stored in the memory and executable on the processor. When the high-temperature environment concrete surface crack detection program is executed by the processor, it implements the high-temperature environment concrete surface crack detection method.

[0026] The present invention also provides a computer program product, including a concrete surface crack detection program under high temperature environment, wherein the concrete surface crack detection program under high temperature environment is executed by a processor to implement the concrete surface crack detection method under high temperature environment.

[0027] The beneficial effects of this invention are as follows: Compared with the inability to fully meet the needs for real-time detection and accurate measurement of cracks under high-temperature environments, especially when the temperature fluctuates greatly, the sensor and image quality will be significantly interfered with, affecting the accurate identification and measurement of cracks, this invention improves the detection accuracy and real-time performance of cracks under high-temperature conditions by combining multispectral imaging, local adaptive threshold algorithm and morphology, and solves the problem of unstable crack detection under hot and humid environments, thereby effectively improving detection efficiency. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1This is a schematic flowchart of the first embodiment of a method for detecting cracks on the surface of concrete under high temperature conditions according to the present invention.

[0030] Figure 2 This is a schematic diagram of the equipment for detecting cracks on the surface of concrete under high temperature conditions according to the present invention.

[0031] Figure 3 This is a schematic diagram of the apparatus for detecting cracks on the surface of concrete under high temperature conditions according to the present invention. Detailed Implementation

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

[0033] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the concrete surface crack detection method under high temperature environment of the present invention, which presents the first embodiment of the concrete surface crack detection method under high temperature environment of the present invention.

[0034] In a first embodiment, the method includes:

[0035] Step S10: Based on the multispectral sensor, images of the concrete surface are collected from the front, left, right and top angles to obtain images containing information of four different wavelengths: visible light, near infrared, mid infrared and far infrared.

[0036] It should be noted that a multispectral sensor is a sensing device capable of simultaneously acquiring electromagnetic wave signals across multiple wavelength ranges. By capturing image information within different spectral ranges, it enhances the ability to detect details on the surface of a target object. This type of sensor typically includes visible light sensors, near-infrared sensors, mid-infrared sensors, and far-infrared sensors, each responsible for acquiring signals in different wavelength bands.

[0037] Understandably, by combining information from different wavelengths, multispectral sensors can effectively identify and separate the differences between cracks and the background on a concrete surface. Especially in high-temperature environments, this multi-wavelength information helps reduce noise interference caused by uneven illumination or thermal radiation. Furthermore, multi-angle acquisition can cover more crack directions and morphologies, further improving the accuracy and completeness of crack detection.

[0038] It should be understood that, for better crack detection and condition assessment of concrete surfaces, the process of acquiring images of the concrete surface from the front, left, right, and top angles using multispectral sensors involves first configuring and calibrating a multispectral device including visible light, near-infrared, mid-infrared, and far-infrared sensors to ensure that each sensor can capture image data to the maximum extent at its corresponding wavelength. Subsequently, the multispectral sensors are mounted on a multi-angle adjustable mechanical device. Initially, the sensors are aimed at the front of the concrete surface for the first round of image acquisition. Then, the device is adjusted sequentially to capture images from the left, right, and top angles. This multi-angle acquisition helps capture different crack features that may appear due to changes in angle. At each angle, all sensors are activated simultaneously to ensure temporal consistency of the images acquired from each angle, facilitating subsequent image comparison and analysis.

[0039] Step S20: Use an improved feature point matching algorithm to spatially align images from different angles to generate crack region images with a resolution of 0.1 mm per pixel;

[0040] It's important to note that feature point matching algorithms are techniques that detect and match salient local feature points in an image to align images from different viewpoints or wavelengths. These algorithms typically include four main steps: feature point detection, feature description, feature matching, and transform estimation. Common feature point detection algorithms include SIFT (Scale Invariant Feature Transform), SURF (Speeded Robust Feature Transform), and ORB (Rotation Invariant Binary Descriptor). These algorithms effectively extract key points from images, ensuring accurate matching of corresponding feature points even at different angles and viewpoints.

[0041] Understandably, feature point matching algorithms can identify and match common features in images from different angles, thereby enabling spatial registration of these images. This algorithm not only handles differences caused by variations in angle and lighting but also corrects images from different perspectives through geometric transformations, ensuring accuracy during multi-angle information fusion. Feature point matching improves image alignment accuracy, especially when generating high-resolution (e.g., 0.1 mm per pixel) images of crack regions, ensuring the precision of crack edges and avoiding image distortion.

[0042] It should be understood that, in order to better integrate concrete surface images acquired from multiple angles and improve the accuracy and efficiency of crack detection, improvements to the feature point matching algorithm may specifically include:

[0043] (1) Traditional formula methods and their limitations

[0044] Traditional feature point matching algorithms (such as SIFT) typically perform matching based on the Euclidean distance between the feature point's descriptor and its nearest neighbor. The formula is as follows:

[0045]

[0046] Where D(p1,p2) is the Euclidean distance between feature points p1 and p2; Let p1 and p2 be the feature descriptors of feature points in the i-th dimension, respectively, and n represent the dimension of the feature point descriptor, which can be 128 here.

[0047] While this method can achieve feature point matching in most cases, in high-precision crack detection scenarios, the use of this formula can lead to error accumulation due to the small edge features of cracks, especially for small feature areas (such as crack edges or corners), which may result in mismatched matching.

[0048] (2) To meet the requirements for high precision and fine feature points in crack detection, this invention introduces a weighted feature point matching formula and local region curvature analysis, which gives higher priority to feature points at the crack edge during the matching process. The improved matching formula is as follows:

[0049]

[0050] Among them, D w (p1, p2) is the weighted Euclidean distance; w i It is a weighting factor related to crack features, used to amplify the influence of feature points near the crack edge; the remaining parameters are consistent with traditional methods.

[0051] The improvement lies in the fact that, unlike traditional formulas, the weights are dynamically adjusted based on the structural characteristics of the crack, giving higher matching priority to feature points at the crack edges. This can be achieved by setting weights using geometric properties such as curvature analysis, ensuring more precise alignment of feature points within the crack region.

[0052] It should be understood that, in order to further improve the matching accuracy of crack edges, we analyze the curvature of the region surrounding the feature points. The curvature K is set as:

[0053]

[0054] Where y′ represents the slope of the tangent direction at the crack edge, and y″ represents the second derivative of the crack edge. By calculating the curvature of the crack region, inflection points or high-curvature areas at the crack edge are identified, giving higher weight to feature points at these locations during matching and reducing matching bias caused by angle differences.

[0055] For example, suppose in a crack detection project, two high-resolution crack images (0.1 mm / pixel resolution) need to be spatially aligned. The crack width is approximately 1 mm, and images A and B were taken from different angles, with slight deformation and angular differences at the crack edges.

[0056] Suppose the matching feature point descriptors are: feature point p1 = [0.5, 0.7, 0.6] in image A, and feature point p2 = [0.52, 0.68, 0.58] in image B.

[0057] Calculated using traditional Euclidean distance:

[0058]

[0059] The improved method introduces weights, w1 = 0.6, w2 = 0.2, w3 = 0.2, and then performs weighted distance calculation:

[0060]

[0061] By using a weighted distance of 0.02449, the matching accuracy was improved, especially the feature points at the crack edge were more accurately aligned.

[0062] Step S30: Calculate the mean and standard deviation of pixels in the image based on the local region of the crack area image to obtain local brightness feature data, determine the local threshold based on the local brightness feature data, and segment the crack area to form preliminary crack contour data;

[0063] It should be noted that local brightness feature data refers to the brightness information of each pixel within a small area of ​​the crack region image, used to describe the brightness distribution of that area, including the pixel mean, pixel standard deviation, and brightness variation trend; the local threshold is a threshold dynamically determined based on the local brightness feature data, used to distinguish between crack regions and non-crack regions. Compared to the global threshold, the local threshold can adapt to images with uneven brightness, improving the accuracy of crack detection; the crack region refers to a region in the image that exhibits significant brightness differences, typically a region with low brightness and a linear or irregular shape; preliminary crack contour data refers to the contour information of the crack edge formed after brightness segmentation of the crack region. It is the basis for subsequent refined crack detection, including the crack boundary contour and morphological features such as crack length, width, and curvature.

[0064] It should be understood that the local threshold is set based on the mean and standard deviation of the brightness of a local region. The threshold automatically adjusts according to the brightness variations in different local regions to adapt to uneven brightness at the crack edges, thus more accurately segmenting the crack area. The calculation formula is T = μ + k * σ; where T represents the local threshold, μ is the mean brightness of pixels within the local region, σ is the standard deviation of pixel brightness within the local region, and k is an adjustment coefficient used to control the sensitivity of the threshold. For example, assuming a crack image with a resolution of 0.1 mm has a mean brightness of μ = 120 and a standard deviation of σ = 15 in a certain region, the local threshold can be calculated using the formula: T = 127.5. This threshold is used to segment the crack within that local region. Pixels in the crack area with a brightness below the threshold of 127.5 will be identified as crack parts, while those with a brightness above the threshold will be considered background. By segmenting these regions, preliminary crack contour data can be formed, including the crack's boundary lines and shape.

[0065] Step S40: Based on the preliminary crack contour data, the edge data of the crack is obtained by performing edge refinement processing using the improved Canny edge detection algorithm. Noise in the crack edge data is removed by multi-scale morphological operations, and boundary breakpoints are repaired to obtain complete crack contour data.

[0066] It should be noted that the Canny edge detection algorithm is a commonly used edge detection method, comprising five steps: denoising, gradient calculation, non-maximum suppression, double threshold detection, and edge connection. The improved Canny algorithm optimizes these steps to adapt to the complexity of crack edges. Multi-scale morphological operations refer to performing morphological operations on crack edge data at different scale structuring elements to remove edge noise and enhance crack integrity. Commonly used morphological operations include erosion, dilation, opening, and closing operations. Boundary breakpoints are interruptions in the crack contour caused by noise or image resolution issues during crack edge detection. These breakpoints need to be repaired to form complete crack contour data.

[0067] It should be understood that the improved Canny algorithm adaptively adjusts the high and low thresholds based on the brightness characteristics of the crack region, enabling it to better detect the fine edges of the crack. The formula is as follows:

[0068] T gigh =μ+k1·σ,T low =μ+k2·σ

[0069] Among them, T high and T low These represent the high and low threshold values, respectively; μ is the average brightness of the local area; σ is the standard deviation; and k1 and k2 are adjustment coefficients. By dynamically adjusting the thresholds, the system can adapt to the brightness differences in different areas of the crack, ensuring precise detection of the crack edges.

[0070] For example, suppose there is a crack image where the brightness mean of a certain local area is μ = 100 and the standard deviation is σ = 2. Set k1 = 1.5 and k2 = 0.5 to calculate the high and low thresholds respectively.

[0071] T high =μ+k1·σ=130,T low =μ+k2·σ=110

[0072] In this example, the choice between thresholds 130 and 110 distinguishes between prominent and subtle edges in a crack image: when the brightness change at the crack edge is very pronounced (e.g., a transition from a black crack to a white background), the gradient values ​​of these pixels may be greater than 130, and they will be directly identified as strong edge points. For areas with less defined crack edges or smaller brightness changes (e.g., small cracks within the crack), the gradient values ​​of these pixels may fall between 110 and 130. The Canny algorithm ensures that these pixels form continuous edge lines by connecting weak edges. Ultimately, the high threshold of 130 ensures that the main outline of the crack is accurately identified, while the low threshold of 110 helps connect the fine crack edges, thus forming a complete crack outline while avoiding noise interference.

[0073] Step S50: Measure the width and length of the crack directly by calculating the pixel distance between the crack edges, and generate a crack size report based on these measurements.

[0074] It should be noted that the width and length of the crack are calculated by analyzing crack edge data. Specific steps include calculating the pixel distance between crack edges to directly measure the actual size of the crack. Crack width refers to the shortest distance between the two edges perpendicular to the crack direction. By extracting the two edges of the crack and calculating the perpendicular distance between these two edges pixel by pixel, the width value at different locations of the crack can be obtained. Crack length is the total length along the crack direction. By tracking the continuity of the crack edges and gradually accumulating the pixel distance along the crack path, the total length of the crack can be obtained. The crack size report includes important indicators such as the average width, maximum width, minimum width, and total length of the crack. This information will be used to assess the severity of the crack and potential structural risks. The report will include width variations at different locations, showing the width distribution along the crack's length, including the maximum, minimum, and average width values.

[0075] Understandably, the conversion from pixel distance to actual distance is necessary: ​​since images are represented in pixels, in actual measurements, pixel distance must be converted to physical distance using resolution (e.g., 0.1 mm per pixel). For example, if the image resolution is 0.1 mm / pixel, then multiplying the pixel distance by 0.1 mm gives the actual physical size of the crack.

[0076] It should be understood that during crack width measurement, the distance between each pair of opposite edge points needs to be calculated along the crack direction. This allows us to obtain the crack width at different locations and generate a crack width distribution map. The crack length is calculated by accumulating pixel distances point by point along the edge path of the crack's main direction. By tracing the crack path, the total crack length can be obtained.

[0077] For example, assuming the image resolution is 0.1 mm / pixel, and the measured pixel distance at a certain position on both sides of the crack is 10 pixels, then the actual crack width at that position is: W = 10 × 0.1 = 1.0 mm. By calculating pixel by pixel along the crack edge, assuming the total crack length is 500 pixels, then the actual crack length is: L = 500 × 0.1 = 50.0 mm. The final crack size report will contain this information and further analyze the structural characteristics of the crack based on the measurement results.

[0078] In the second embodiment, the step of removing noise from the crack edge data through multi-scale morphological operations and repairing boundary breakpoints to obtain complete crack contour data includes:

[0079] Morphological dilation of crack edge images is performed using structuring elements of different scales to remove isolated noise points and connect fractured crack boundaries. Combined with changes in morphological structure at different scales, complete crack contour data is extracted and generated.

[0080] It should be noted that multi-scale morphological operations refer to using structural elements of different sizes and shapes (such as circles, squares, or rhombuses) to perform morphological operations on crack edge images at different scales. This method can repair and optimize crack edges at multiple levels of detail, remove noise points, and connect broken crack boundaries, thereby generating a more complete and continuous crack contour.

[0081] It should be understood that multi-scale morphological operations gradually eliminate noise and repair edge breaks by dilating the crack edges at different scales. The process of morphological dilation can be understood as expanding each pixel at the crack edge to the range of its surrounding pixels, thus removing isolated noise points and connecting crack segments at edge breaks. At small scales, morphological dilation can handle minute noise within cracks more precisely; at large scales, dilation can repair discontinuities at the boundaries of large cracks. In crack detection images, noise points may be small, isolated pixels, often insignificant. Using small-scale structuring elements for morphological dilation can effectively remove these isolated noise points, making crack edges smoother; furthermore, when there are broken or discontinuous parts at the crack boundary, large-scale structuring elements can connect these boundaries through dilation operations, thus forming a complete crack outline.

[0082] For example, assuming a crack image contains numerous noise points and boundary breaks, dilation using small-scale structuring elements (e.g., 3×3 square structuring elements) can remove isolated noise pixels, resulting in a cleaner image. Further dilation using larger-scale structuring elements (e.g., 5×5 or 7×7) can repair broken sections at the crack boundaries, connecting them to form a complete crack outline. This approach, by combining morphological changes at different scales, produces a crack outline that is not only complete and continuous but also preserves crack detail information, ensuring accurate crack edge detection.

[0083] In addition, the present invention also provides a real-time detection device for concrete surface cracks under high temperature conditions, please refer to... Figure 3 The real-time detection device for concrete surface cracks under high temperature conditions includes:

[0084] The positioning module is used to receive BeiDou satellite positioning data of the target area based on the BeiDou satellite receiver, and to determine the precise location of the target area by parsing the BeiDou satellite positioning data;

[0085] The data acquisition and processing module is used to acquire current meteorological data sources and historical meteorological data sources based on the precise location of the target area, and obtain initial meteorological data and historical meteorological data through standardization processing;

[0086] The model building and training module is used to build an extreme weather event prediction model based on the initial meteorological data and historical meteorological data, train the model using convolutional neural network and recurrent neural network algorithms, and evaluate and optimize the model using cross-validation methods.

[0087] The feature extraction and early warning release module is used to process the initial meteorological data through time series analysis to obtain key meteorological features and input them into the trained extreme weather event prediction model. Based on the extreme weather event prediction results, it releases extreme weather event early warning information to the terminal.

[0088] This invention provides a real-time detection device for concrete surface cracks under high-temperature conditions, employing a real-time detection method for concrete surface cracks under high-temperature conditions as described in the above embodiments, thus solving the technical problem of real-time detection of concrete surface cracks under high-temperature conditions. Compared with the prior art, the beneficial effects of the real-time detection device for concrete surface cracks under high-temperature conditions provided by this invention are the same as those of the real-time detection method for concrete surface cracks under high-temperature conditions provided in the above embodiments, and other technical features of the real-time detection device for concrete surface cracks under high-temperature conditions are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0089] This invention provides a real-time detection device for concrete surface cracks under high-temperature conditions. Please refer to [the relevant documentation]. Figure 2A real-time concrete surface crack detection device under high-temperature conditions includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the real-time concrete surface crack detection method under high-temperature conditions described in Embodiment 1 above. The real-time concrete surface crack detection device under high-temperature conditions in this embodiment of the invention may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This real-time concrete surface crack detection device under high-temperature conditions is merely an example and should not impose any limitations on the functionality or scope of use of the embodiments of the invention. A real-time detection device for concrete surface cracks under high-temperature conditions may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the real-time detection device for concrete surface cracks under high-temperature conditions. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows a real-time concrete surface crack detection device for high-temperature environments to exchange data with other devices wirelessly or via wired communication. Although a real-time concrete surface crack detection device for high-temperature environments with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0090] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for real-time detection of concrete surface cracks under high-temperature conditions. The computer program product provided by this invention can solve the technical problem of real-time detection of concrete surface cracks under high-temperature conditions. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the method for real-time detection of concrete surface cracks under high-temperature conditions provided in the above embodiments, and will not be repeated here.

[0091] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0092] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0093] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for detecting concrete surface cracks in a high temperature environment, characterized in that: The method comprises: Step S10: Based on the multispectral sensor, images of the concrete surface are collected from the front, left, right and top view angles to obtain images containing information of four different wavelengths: visible light, near infrared, mid-infrared and far infrared; Step S20: using an improved feature point matching algorithm to spatially align images at different angles to generate a crack area image with a resolution of 0.1 mm per pixel; Step S30: calculating the mean and standard deviation of pixels in the image according to the local area in the crack area image to obtain local brightness feature data, determining a local threshold according to the local brightness feature data and segmenting the crack area to form preliminary crack contour data; Step S40: performing edge refinement processing based on the preliminary crack contour data in combination with an improved Canny edge detection algorithm to obtain crack edge data, removing noise in the crack edge data through multi-scale morphological operations, and repairing boundary breakpoints to obtain complete crack contour data; Step S50: directly measuring the width and length of the crack by calculating the pixel distance between the crack edges, and generating a crack size report based on these measurement results.

2. The method for detecting concrete surface cracks in a high temperature environment according to claim 1, characterized in that: In step S40, the step of removing noise from the edge data of the crack by multi-scale morphological operation and repairing the boundary breakpoints to obtain complete crack contour data includes: The crack edge images are morphologically expanded using structural elements of different scales to remove isolated noise points and connect broken crack boundaries. The complete crack contour data is extracted and generated by combining the changes in morphological structures at different scales.

3. The method for detecting concrete surface cracks in a high temperature environment as claimed in claim 1, characterized in that: Before step S20, the method further includes: using a scale-invariant feature transformation algorithm to identify feature points on images collected at different angles.

4. The method for detecting concrete surface cracks in a high temperature environment as claimed in claim 1, characterized in that: In step S40, the edge refinement processing includes dynamically adjusting the crack edge determination threshold in the Canny edge detection algorithm according to the local brightness characteristics of the crack area during the crack edge detection process.

5. The method for detecting concrete surface cracks in a high temperature environment as claimed in claim 1, characterized in that: The crack size report generated in step S50 includes crack width, length and crack expansion trend analysis results, which are used to evaluate the safety of the structure.

6. The method for detecting concrete surface cracks in a high temperature environment as claimed in claim 1, characterized in that: In step S10, a light intensity adaptive adjustment method is used to perform image acquisition.

7. The method for detecting concrete surface cracks in a high temperature environment as claimed in claim 1, characterized in that: The noise in the edge data of the crack in step S40 includes sensor thermal noise.

8. A concrete surface crack detection device in a high temperature environment, characterized in that: The concrete surface crack detection device under high temperature environment comprises: The multispectral acquisition module is used to collect information on the concrete surface from the front, left, right and top view angles based on a multispectral sensor to obtain images containing information of four different wavelengths: visible light, near infrared, mid-infrared and far infrared; An image registration module is used to spatially align images at different angles using an improved feature point matching algorithm to generate a crack area image with a resolution of 0.1 mm per pixel; A local feature analysis module, used to calculate the mean and standard deviation of pixels in the image according to the local area in the crack area image to obtain local brightness feature data, determine the local threshold according to the local brightness feature data and segment the crack area to form preliminary crack contour data; An edge processing module is used to perform edge refinement processing based on the preliminary crack contour data in combination with an improved Canny edge detection algorithm to obtain edge data of the crack, remove noise in the edge data of the crack through multi-scale morphological operations, and repair boundary breakpoints to obtain complete crack contour data; The size measurement module is used to directly measure the width and length of cracks by calculating the pixel distance between the crack edges and generate a crack size report based on these measurement results.

9. A concrete surface crack detection device in a high temperature environment, characterized in that: The concrete surface crack detection equipment under high temperature environment comprises: A memory, a processor, and a concrete surface crack detection program in a high temperature environment stored in the memory and executable on the processor. When the concrete surface crack detection program in a high temperature environment is executed by the processor, the concrete surface crack detection method in a high temperature environment is implemented.

10. A computer program product, characterized in that The computer program product includes a concrete surface crack detection program under a high temperature environment, and when the concrete surface crack detection program under a high temperature environment is executed by a processor, the concrete surface crack detection method under a high temperature environment is implemented.

Images