Geological structure fracture automatic identification and detection system based on deep learning

Abstract

The invention discloses a geological structure fracture automatic identification and detection system based on deep learning, and relates to the technical field of geological structure fracture automatic identification and detection, and the geological structure fracture automatic identification and detection system comprises a fracture image analysis module, a distortion intelligent evaluation module, a form adaptive correction module, a correction efficiency evaluation module and an identification data management module. The distortion intelligent evaluation module is used for acquiring imaging parameter information of each region in the crack image in real time, judging the form distortion degree of each region in the crack image under a perspective distortion condition, and dividing each region in the crack image into a low distortion region, a medium distortion region and a high distortion region according to a judgment result; and the form self-adaptive correction module is used for constructing a fracture form correction mechanism according to the division result of each region in the fracture image. According to the invention, the problem of fracture shape distortion caused by the change of a shooting view angle is solved, accurate identification and automatic correction of the fracture are realized, and the detection precision and stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic identification and detection of geological structure fissures, and particularly relates to an automatic identification and detection system for geological structure fissures based on deep learning. Background Art

[0002] Geological structure fissures refer to cracks or fractures in the earth's crust or rock formations caused by tectonic forces or other natural factors, and are one of the important rock deformation characteristics in geology. Characteristics such as the morphology, distribution, and width of fissures are of great significance for studying aspects such as groundwater flow, seismic activities, and mineral resource distribution. During geological exploration and engineering construction, accurately identifying and detecting these fissures is the basis for ensuring engineering safety, formulating reasonable mining plans, and conducting disaster prediction. However, traditional fissure identification methods usually rely on manual observation or simple image processing techniques, which are not only inefficient and time-consuming, but also easily affected by human factors, resulting in poor accuracy. Therefore, automatic identification and detection of geological structure fissures based on deep learning has become an effective solution. Deep learning algorithms, especially convolutional neural networks (CNNs), can self-learn through a large amount of geological image data and extract complex fissure features, avoiding the manual intervention and limitations of traditional methods. In addition, deep learning models have significant advantages in large-scale data processing, complex pattern recognition, and adaptive learning, and can automatically optimize in different geological scenarios, improving the accuracy and real-time performance of fissure detection, further enhancing the intelligent level of the overall detection system, and meeting the requirements of modern geological exploration for efficient and accurate detection technologies.

[0003] Existing deep learning-based automatic identification and detection technologies for geological structure fractures usually adopt deep learning algorithms such as convolutional neural networks (CNNs), and automatically extract the characteristic information of fractures by training a large amount of geological image data. Specifically, this technology first obtains a large amount of geological images or three-dimensional data through remote sensing technology, lidar, or geological exploration equipment, which may include information such as different rock surfaces, fracture distributions, and geological layers. Then, the deep learning model is used to process these original data. First, the convolutional layer automatically extracts low-level features in the image, such as edges, textures, and shapes, and then gradually abstracts through deeper network structures to extract high-level features related to fractures, such as the morphology, width, length, and distribution pattern of fractures. The deep learning model is trained with a large amount of labeled data to continuously optimize its parameters, enabling the system to accurately identify fracture characteristics under different geological conditions and adaptively process changes in different scenarios. The trained model can automatically process new geological images and output information such as the location, quantity, and type of fractures in real time, thus achieving efficient and accurate automatic identification and detection of fractures. In addition, to cope with different types of geological data, existing technologies may also combine image enhancement technologies, data preprocessing methods, and multi-modal data fusion technologies to further improve the detection accuracy and system versatility, making the application effect of this technology more excellent in complex geological environments.

[0004] The existing technology has the following deficiencies: During the geological exploration process, handheld cameras or drones and other shooting devices are usually used to obtain fracture images of the rock surface from different angles. In the case of large changes in the shooting angle, the morphology of the fractures will be distorted due to the perspective effect. Since the shooting angle of the shooting device is not always perpendicular to the rock surface, the width, length, and orientation of the fractures in the image are stretched or compressed, resulting in the distortion of the true geometric characteristics of the fractures in the image. The existing deep learning-based automatic identification and detection technologies for geological structure fractures mainly rely on two-dimensional image feature extraction and lack the ability to adapt to the morphological distortion caused by the change of the perspective. It is unable to accurately restore the true morphology of the fractures, resulting in large errors in the identification of the size and direction of the fractures at different shooting angles, which in turn affects the correct assessment of the fracture propagation trend. This error may lead to misjudgments of the true scale and direction of the fractures during the geological disaster assessment or engineering construction planning process, possibly resulting in an underestimation of the fracture scale, thus ignoring potential geological risks and leading to safety hazards; on the contrary, if the fractures are overestimated, it may lead to unnecessary engineering reinforcement, increase construction costs, and affect the economy and feasibility of the project.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, so it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide an automatic recognition and detection system for geological structure fissures based on deep learning to solve the problems in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: An automatic recognition and detection system for geological structure fissures based on deep learning, including a fissure image analysis module, a distortion intelligent evaluation module, a morphology adaptive correction module, a correction efficiency evaluation module, and an identification data management module; The fissure image analysis module scans the rock surface through a photographing device during geological exploration to obtain the image information of the fissures in real time, and detects whether the fissures are distorted due to the perspective effect in the obtained fissure image information. When it is detected that the fissure morphology is distorted due to the perspective effect, the fissure image is evenly divided into several regions; The distortion intelligent evaluation module obtains the imaging parameter information of each region in the fissure image in real time, analyzes it after obtaining, judges the morphological distortion degree of each region in the fissure image under the condition of perspective distortion, and divides each region in the fissure image into a low-distortion region, a medium-distortion region, and a high-distortion region according to the judgment result; The morphology adaptive correction module constructs a fissure morphology correction mechanism according to the division result of each region in the fissure image, and performs corresponding correction measures on the low-distortion region, the medium-distortion region, and the high-distortion region respectively; The correction efficiency evaluation module obtains the correction error information of each region in the fissure image in real time during the process of the fissure morphology correction mechanism correcting each region in the fissure image, analyzes it after obtaining, evaluates whether the correction effect of the fissure morphology correction mechanism on each region in the fissure image can reach the expectation, and optimizes the fissure morphology correction mechanism according to the evaluation result; The identification data management module completes the automatic recognition and detection of the fissures according to the optimized fissure morphology correction mechanism, and records and stores the results of the fissure recognition and detection and the application process of the correction mechanism for subsequent monitoring, analysis, and optimization of the fissure morphology changes.

[0008] Preferably, in the distortion intelligent evaluation module, after obtaining the imaging parameter information of each region in the fissure image in real time, preprocess it; extract the light intensity feature information and contour structure information in the imaging parameter information of each region in the preprocessed fissure image, and analyze them after extraction to generate the fissure perspective distortion coefficient and fissure contour distortion index of each region respectively; construct a morphological distortion model for the generated fissure perspective distortion coefficient and fissure contour distortion index of each region, generate the morphological distortion coefficient of each region, and analyze it after generation to judge the morphological distortion degree of each region in the fissure image under the condition of perspective distortion, and divide each region in the fissure image into a low distortion region, a medium distortion region and a high distortion region according to the judgment result.

[0009] Preferably, the acquisition logic of the fissure perspective distortion coefficient and fissure contour distortion index of each region is as follows: Extract the light intensity feature information in the imaging parameter information of each region in the preprocessed fissure image, specifically including the pixel brightness value, the offset of the centroid coordinate, and the average gradient intensity of the edge pixels of each region in the fissure image at different times within a period of time, and calibrate them respectively as 、 and , represents the pixel brightness value of the th region in the fissure image at the th moment within a period of time, represents the offset of the centroid coordinate of the th region in the fissure image at the th moment within a period of time, represents the average gradient intensity of the edge pixels of the th region in the fissure image at the th moment within a period of time, , , and are all positive integers; Calculate the fissure perspective distortion coefficient of each region. The specific calculation formula is as follows: ; In the formula, is the fissure perspective distortion coefficient of the th region; Extract the contour structure information in the imaging parameter information of each region in the preprocessed fissure image, specifically including the total length of the fissure contour, the difference between the maximum and minimum values of the internal pixels, and the edge curvature change amount of each region in the fissure image at different times within a period of time, and calibrate them respectively as 、 and , Represents the total length of the crack profile at the th moment of the th region in the crack image over a period of time, Represents the difference between the maximum and minimum values of the internal pixels at the th moment of the th region in the crack image over a period of time, Represents the change in edge curvature at the th moment of the th region in the crack image over a period of time; Calculate the crack profile distortion index for each region. The specific calculation formula is as follows: ; In the formula, is the crack profile distortion index of the th region.

[0010] Preferably, construct a morphological distortion model for the crack perspective distortion coefficient and the crack profile distortion index of each generated region, and generate the morphological distortion coefficient of each region through weighted summation. The specific calculation formula is: , where and are the non-zero weight coefficients of the crack perspective distortion coefficient and the crack profile distortion index of each region respectively, and ; Determine the pre-set threshold interval of the morphological distortion coefficient, and compare it with the generated morphological distortion coefficient of each region after determination. According to the comparison result, judge the morphological distortion degree of each region in the crack image under the perspective distortion condition, and divide each region in the crack image into a low distortion region, a medium distortion region and a high distortion region according to the judgment result. The specific comparison analysis and division are as follows: If , the morphological distortion degree of this region in the crack image under the perspective distortion condition is low, and this region is divided into a low distortion region; If , the morphological distortion degree of this region in the crack image under the perspective distortion condition is medium, and this region is divided into a medium distortion region; If , the morphological distortion degree of this region in the crack image under the perspective distortion condition is high, and this region is divided into a high distortion region.

[0011] Preferably, in the morphological adaptive correction module, according to the division results of each region in the crack image, a crack morphology correction mechanism is constructed, specifically: according to the division results of the low-distortion region, medium-distortion region, and high-distortion region, different image transformation, feature correction, and edge optimization parameters are set respectively to form a crack morphology correction mechanism; based on the morphological distortion coefficients of each region and the performance of the image processing algorithm, the crack morphology correction mechanism automatically determines the image transformation method, feature correction strategy, and the adjustment amplitude of edge optimization through pre-set rules. Corresponding correction measures are taken for the low-distortion region, medium-distortion region, and high-distortion region respectively, specifically: in the low-distortion region, the standard image transformation parameters in the crack morphology correction mechanism are used to perform lightweight geometric correction to reduce perspective error; in the medium-distortion region, the standard feature correction parameters in the crack morphology correction mechanism are maintained, combined with geometric adjustment and texture enhancement strategies to ensure the accuracy of the crack boundary; in the high-distortion region, the enhanced edge optimization parameters in the crack morphology correction mechanism are used, combined with deep learning reconstruction algorithms and multi-scale image registration methods, to enhance the recovery accuracy of the crack morphology and reduce the recognition deviation caused by severe distortion.

[0012] Preferably, in the correction efficiency evaluation module, during the process of the crack morphology correction mechanism correcting each region in the crack image, the correction error information of each region in the crack image is obtained in real time and pre-processed after acquisition; the correction offset information and morphological consistency information in the pre-processed correction error information are extracted and analyzed after extraction, and the crack correction offset coefficient and crack correction consistency index of each region are generated respectively; a correction effect evaluation model is constructed for the generated crack correction offset coefficient and crack correction consistency index of each region, and the correction evaluation coefficient of each region is generated and analyzed after generation to evaluate whether the correction effect of the crack morphology correction mechanism on each region in the crack image can meet the expectations, and the crack morphology correction mechanism is optimized according to the evaluation results.

[0013] Preferably, the acquisition logic of the crack correction offset coefficient and crack correction consistency index of each region is as follows: Extract the correction offset information in the pre-processed correction error information, specifically including the pixel density change rate, boundary offset vector length, and gray level gradient variance of each region in the crack image at different times during a period in the process of the crack morphology correction mechanism performing correction, and are respectively calibrated as 、 and , represents the pixel density change rate of the th region in the crack image at the th moment during a period in the process of the crack morphology correction mechanism performing correction, Indicates the length of the boundary offset vector of the th region in the crack image at a certain moment during the calibration process of the crack morphology calibration mechanism. The length of the boundary offset vector of the th region in the crack image at a certain moment during the calibration process of the crack morphology calibration mechanism. Indicates the variance of the gray gradient of the th region in the crack image at a certain moment during the calibration process of the crack morphology calibration mechanism. , , and are all positive integers; Calculate the crack correction offset coefficient of each region. The specific calculation formula is as follows: ; In the formula, is the crack correction offset coefficient of the th region; Extract the morphological consistency information in the preprocessed calibration error information, specifically including the edge pixel coincidence degree, the change rate of the edge gradient, and the change rate of the crack contour length of each region in the crack image at different moments during the calibration process of the crack morphology calibration mechanism, and mark them as , and , Indicates the edge pixel coincidence degree of the th region in the crack image at a certain moment during the calibration process of the crack morphology calibration mechanism. The edge pixel coincidence degree of the th region in the crack image at a certain moment during the calibration process of the crack morphology calibration mechanism. Indicates the change rate of the edge gradient of the th region in the crack image at a certain moment during the calibration process of the crack morphology calibration mechanism. Indicates the change rate of the crack contour length of the th region in the crack image at a certain moment during the calibration process of the crack morphology calibration mechanism. The change rate of the crack contour length of the

[0014] Calculate the crack correction consistency index of each region. The specific calculation formula is as follows: ; In the formula, is the crack correction consistency index of the th region.

[0015] Preferably, for the crack correction offset coefficient and the crack correction consistency index Build a calibration effect evaluation model and generate calibration evaluation coefficients for each region through weighted summation , and the specific calculation formula is: , where and are the fracture calibration offset coefficients of each region and the fracture calibration consistency index non-zero weight coefficients, and ; Determine the calibration evaluation coefficient thresholds for each region preset in advance , and compare with the generated calibration evaluation coefficients of each region after determination . According to the comparison results, evaluate whether the calibration effect of the fracture morphology calibration mechanism on each region in the fracture image can meet the expectations, and optimize the fracture morphology calibration mechanism according to the evaluation results. The specific comparison and analysis are as follows: If , the calibration effect of the fracture morphology calibration mechanism on this region in the fracture image can meet the expectations, and there is no need to optimize the fracture morphology calibration mechanism; If , the calibration effect of the fracture morphology calibration mechanism on this region in the fracture image cannot meet the expectations, and the fracture morphology calibration mechanism needs to be optimized, specifically including: adjusting the image transformation parameters to optimize the perspective distortion correction in the morphology calibration process and reduce the geometric distortion caused by the change of the shooting angle; improving the feature calibration algorithm to improve the accuracy of the fracture boundary by enhancing the feature point matching and texture analysis accuracy and reducing the boundary misalignment and detail loss; enhancing the edge optimization strategy by adopting multi-scale edge detection and adaptive smoothing technology, so as to improve the accuracy and consistency of fracture recognition and ensure that the calibration effect meets the expected standard.

[0016] In the above technical solutions, the technical effects and advantages provided by the present invention are as follows: 1. By introducing modules such as fracture image analysis, distortion evaluation, morphology calibration, calibration efficiency evaluation, and recognition data management, the present invention realizes the accurate recognition and detection of geological structure fractures under perspective distortion conditions. Compared with the traditional fracture recognition method that relies on two-dimensional image features, the present invention can obtain the light intensity characteristics, contour structure, and calibration error information of the fracture region from multiple dimensions, and dynamically evaluate the degree of fracture morphology distortion based on the combination of deep learning and mathematical modeling. By establishing the fracture perspective distortion coefficient and the fracture contour distortion index, the distortion degree of each region can be quantified to ensure that the true geometric shape of the fracture can be accurately restored under different shooting perspectives, thus effectively avoiding the problem of morphological distortion caused by the perspective effect.

[0017] 2. The advantage of the present invention lies in the adoption of an adaptive crack morphology correction mechanism. Different image transformation, feature correction, and edge optimization parameters are set for low, medium, and high distortion regions respectively, and the correction strategy is automatically adjusted, improving the accuracy and robustness of crack detection. By obtaining the imaging parameter information of the crack image in real time and using deep learning reconstruction and multi-scale image registration techniques, the blurring and misalignment of the crack boundary are effectively reduced. At the same time, the correction effect is continuously monitored through an intelligent evaluation module to ensure the stability and consistency of crack correction. In addition, based on the evaluation model of the correction offset coefficient and the correction consistency index, the quantitative evaluation of the correction quality is realized, automatically judging whether the correction meets the expectation, and optimizing according to the evaluation result, greatly reducing the need for human intervention and improving the intelligent level of the system.

[0018] 3. The present invention also has efficient data management and traceability. The identification data management module can record and store the crack detection results and the correction application process, supporting long-term crack morphology monitoring and trend analysis. Through the accumulation of historical data, the system can adaptively adjust the correction parameters and optimize the identification algorithm to adapt to the changes in different geological environments and crack morphologies, improving the accuracy of geological disaster prediction. Generally speaking, this technical solution can not only significantly improve the accuracy of crack identification, reduce the identification error caused by perspective distortion, but also provide scientific and reliable data support for geological exploration and engineering construction, improving the operation efficiency and safety, and having high engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a schematic diagram of the modules of an automatic crack identification and detection system for geological structures based on deep learning according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Now, the exemplary embodiments will be described more comprehensively with reference to the drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more complete and comprehensive, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art.

[0022] The present invention provides as Figure 1A geological structure fissure automatic recognition and detection system based on deep learning, including a fissure image analysis module, a distortion intelligent evaluation module, a morphology adaptive correction module, a correction efficiency evaluation module, and an identification data management module; The fissure image analysis module, during geological exploration, scans the rock surface through a photographing device to obtain the image information of the fissures in real time, and detects whether the fissures are distorted due to the perspective effect in the obtained fissure image information. When it is detected that the fissure morphology is distorted due to the perspective effect, the fissure image is evenly divided into several regions; During geological exploration, the rock surface is scanned through a photographing device to obtain the image information of the fissures in real time. Devices such as high-resolution RGB cameras, multispectral cameras, or lidar (LiDAR) can be used to achieve full coverage of the rock surface. Drones or handheld devices carry these sensors and scan the exploration area row by row under a predetermined trajectory or manual control to ensure that fissure images are collected from different angles. To achieve real-time data acquisition, the system software should integrate an image acquisition module, use the device SDK (such as the camera control API or the LiDAR data stream interface) to obtain images, and combine the inertial measurement unit (IMU) and the GPS module to obtain the shooting position, attitude, and altitude information to ensure the geographical accuracy of the data. In addition, to reduce the acquisition deviation caused by device jitter or complex terrain, the system can apply real-time image stitching technology (such as image stitching algorithms based on SIFT or ORB feature points) during the image acquisition stage to generate a complete fissure surface image, and at the same time extract the features of the fissure area through Canny edge detection or deep learning object detection algorithms (such as YOLO or Faster R-CNN) to achieve high-precision fissure information acquisition.

[0023] After obtaining the fissure image information, it is necessary to detect whether the fissures are distorted due to the perspective effect, and in the case of detecting distortion, the fissure image is evenly divided into several regions. The distortion degree of the image can be evaluated by obtaining the imaging parameters of the image (including shooting angle, focal length, flight altitude, etc.) and the geometric features of the fissure edge, using the homography matrix calculation algorithm. The system first performs image registration, matches the feature points of the image through the RANSAC algorithm, obtains the deformation parameters of the fissure area, and combines the perspective projection model to calculate the morphological distortion degree of the fissure. If the distortion degree exceeds the set threshold, grid division or adaptive segmentation algorithms (such as K-means clustering or region growing algorithm) are used to divide the fissure image into multiple regions to ensure the accuracy of subsequent processing. The perspective distortion characteristics of each region are jointly determined by indicators such as edge curvature, width change rate, and surface texture consistency. The system generates a distortion evaluation coefficient based on these indicators and classifies the image regions accordingly to ensure the adoption of adaptive correction strategies under different distortion degrees.

[0024] The reason for this is that in the actual exploration environment, due to the complexity of the terrain, the perspective changes of the imaging equipment, and the irregularity of the rock surface, the fracture images often have significant perspective distortion, resulting in the stretching or compression of the geometric shape of the fractures in the images. If these distortions are not detected and corrected in a timely manner, the width, orientation, and distribution characteristics of the fractures cannot be accurately extracted, which will in turn affect the risk assessment of geological disasters and the rationality of engineering design. By performing real-time distortion detection and regional division in the early stage of image acquisition, potential problems can be identified in advance, and an accurate basis can be provided for subsequent fracture identification and correction. The purpose of regional division is to implement different correction measures under different degrees of distortion, so as to restore the true shape of the fractures to the greatest extent while ensuring the computational efficiency, ensure the accuracy and reliability of the final identification results, improve the adaptability and robustness of the detection system, and reduce misjudgments and omissions caused by distortion.

[0025] The distortion intelligent evaluation module obtains the imaging parameter information of each region in the fracture image in real time, analyzes it after acquisition, judges the degree of morphological distortion of each region in the fracture image under the condition of perspective distortion, and divides each region in the fracture image into a low-distortion region, a medium-distortion region, and a high-distortion region according to the judgment result; In this embodiment, in the distortion intelligent evaluation module, after obtaining the imaging parameter information of each region in the fracture image in real time, preprocess it; extract the light intensity feature information and contour structure information in the imaging parameter information of each region in the preprocessed fracture image, analyze it after extraction, and generate the fracture perspective distortion coefficient and fracture contour distortion index of each region respectively; construct a morphological distortion model for the generated fracture perspective distortion coefficient and fracture contour distortion index of each region, generate the morphological distortion coefficient of each region, analyze it after generation, judge the degree of morphological distortion of each region in the fracture image under the condition of perspective distortion, and divide each region in the fracture image into a low-distortion region, a medium-distortion region, and a high-distortion region according to the judgment result.

[0026] Obtaining the imaging parameter information of each region in the crack image in real time can be achieved by combining image segmentation and feature extraction. First, use common image segmentation techniques (such as K-means clustering, watershed algorithm, or semantic segmentation networks based on deep learning like U-Net) to divide the crack image into multiple regions. Then, on the divided regions, use image processing tools (such as OpenCV, MATLAB, etc.) to extract the imaging parameter information of each region, such as brightness histogram, edge intensity, pixel gradient direction, etc. The specific implementation methods of these parameter information can extract edge features by calling Sobel operator or Laplacian operator, obtain pixel brightness distribution using histogram statistics, and use optical flow method (such as Lucas-Kanade optical flow algorithm) to track the movement trajectory of pixels to record the dynamic changes of each region in real time. In addition, to ensure the real-time nature of the acquired data, a multi-threaded processing method can be adopted and combined with GPU accelerated computing to update the imaging parameters in the region in real time while acquiring the crack image.

[0027] The purpose of preprocessing is to improve the accuracy and stability of subsequent crack recognition and analysis, and reduce the influence of factors such as noise, uneven illumination, and insufficient contrast on the recognition accuracy. When performing preprocessing, operations such as noise removal, image enhancement, contrast adjustment, and edge strengthening are usually required. Noise removal can use spatial domain methods such as Gaussian filtering and median filtering to remove the random noise generated by environmental interference in the crack image; image enhancement can enhance the local contrast of the crack region through adaptive histogram equalization (CLAHE) to make small cracks clearer; contrast adjustment can use Gamma transformation to optimize the image brightness to make the acquisition of imaging parameters more accurate; edge strengthening can use the Laplacian operator or non-maximum suppression (NMS) to highlight the crack edge and improve the robustness of feature extraction. These preprocessing steps can be executed sequentially in the software through a pipeline processing method to ensure that all crack image regions reach the best image quality standard before entering the next step of analysis.

[0028] Extracting the light intensity feature information and contour structure information from the imaging parameter information of each region in the preprocessed crack image can be achieved by combining image feature analysis with computer vision algorithms. First, when extracting light intensity feature information, the pixel statistics and transformation methods can be used to extract the brightness distribution, average brightness, standard deviation and other indicators of the region from the grayscale histogram of the image to reflect the illumination intensity and uniformity of each region. At the same time, the local binary pattern (LBP) method is used to extract the texture features of the region to capture the intensity change pattern of the crack region. In addition, the Fourier transform can be used to analyze the image frequency components to evaluate the illumination change trend of the region and avoid high-frequency noise interference. Secondly, when extracting contour structure information, the crack boundary can be identified by edge detection operators (such as Canny, Sobel or Prewitt operators), and morphological operations (such as corrosion, expansion, opening and closing operations) can be used to optimize the edge integrity, and then the edge length, curvature radius, contour complexity and other indicators can be calculated. In addition, Hough transform can be applied to detect the straight or curved features of the cracks to obtain the directionality and geometric morphology of the cracks, and the minimum bounding rectangle method can be used to extract the aspect ratio, area and other parameters of the region to reflect the morphological characteristics of the cracks. These extraction operations can be implemented through software tools such as OpenCV and MATLAB, and batch processing can be used to parallelize the various regions in the image to ensure the real-time and accuracy of the extraction results.

[0029] In this embodiment, the logic for obtaining the crack perspective distortion coefficient and crack outline distortion index of each region is as follows: The light intensity feature information in the imaging parameter information of each area in the preprocessed crack image is extracted, including the pixel brightness value, the offset of the centroid coordinates and the average gradient intensity of the edge pixels in each area of ​​the crack image at different times within a period of time, and calibrated as , and , Indicates the crack image area within a period of time The pixel brightness value at the moment, Indicates the crack image area within a period of time The offset of the center of mass coordinates at the moment, Indicates the crack image area within a period of time The average gradient strength of edge pixels at the moment, , , and are all positive integers; The pixel brightness values, centroid coordinate offsets, and average gradient intensities of edge pixels at different moments within a period of time for each region in the crack image can be obtained in real time by combining image processing and computer vision techniques, and these operations can be automatically executed in a software environment (such as OpenCV, MATLAB, etc.). First, the pixel brightness values can be obtained by converting the crack image into a grayscale image or extracting single-channel information (such as the Y channel or V channel) in an RGB image, then using region division methods (such as contour detection based on connected regions or image segmentation based on the watershed algorithm) to determine the boundaries of each region, and using pixel statistical methods to calculate the average brightness, maximum brightness, and minimum brightness of each region to ensure that the changes in light intensity within the region can be recorded in real time at different time points. Secondly, the centroid coordinate offset can be obtained through morphological processing and region detection techniques, that is, using binaryzation, morphological opening operation, and contour detection in the image to extract the boundary of the crack region, and calculating the centroid coordinate of this region (such as calculating the geometric center position of the region through image moments), then tracking the centroid coordinates in consecutive time frames (such as using optical flow method or Kalman filter), and calculating the centroid offset at different moments to evaluate the position change of the crack under the change of viewing angle. Finally, the average gradient intensity of edge pixels can be obtained by using edge detection algorithms (such as Sobel operator, Canny operator) to extract edge pixel points within each region, and calculating the gradient amplitude of these pixels, then statistically averaging the gradient values of each frame to evaluate the sharpness change of the crack boundary under distortion conditions. To ensure real-time performance, these acquisition processes can be accelerated by combining multi-threaded parallel processing with GPU, updating the feature data of each region in real time while collecting images, and using time series analysis methods (such as sliding window technique) to ensure the stability and continuity of the data. In this way, the imaging parameter information of each region in the crack image can be accurately obtained at different moments, providing reliable data support for subsequent distortion evaluation and correction.

[0030] Calculate the perspective distortion coefficients of the cracks in each region. The specific calculation formula is as follows: ; In the formula, is the perspective distortion coefficient of the crack in the th region; The reason for designing this calculation formula is to fully consider the comprehensive influence of factors such as light intensity change, position offset, and edge gradient in each region of the crack image under perspective distortion conditions on the distortion degree. First, the exponential operation is used in the formula to process the average gradient intensity , because the edge gradient intensity can reflect the clarity and sharpness of the fracture boundary, and the exponential operation can amplify the change of the edge intensity to highlight the greater impact that the high-gradient area may receive under perspective distortion. Secondly, the logarithmic operation is designed to balance the relationship between the pixel brightness value and the centroid coordinate offset. This is to avoid the impact of overly large or small values on the overall calculation. The logarithmic function has the characteristic of smooth growth, which can suppress the outliers caused by extreme illumination or offset. At the same time, adding "1" avoids the problem of the denominator being zero. The in the denominator represents the influence of the centroid offset. The larger the offset, the more significant the perspective distortion of the area, thereby reducing the influence weight of the light intensity on the distortion coefficient. Finally, by taking the average over the time dimension , the calculation result becomes more stable over time, which can eliminate the random fluctuations within a short period and ensure that the fracture perspective distortion coefficient can more accurately reflect the distortion degree of the area over the entire time period. In summary, the calculation method of this formula can comprehensively evaluate the perspective distortion degree of each area by exponentially amplifying the edge distortion characteristics, logarithmically balancing the weights of illumination and position offset, and combining the method of time averaging.

[0031] The size of the fracture perspective distortion coefficient of the th area directly reflects the degree of morphological distortion of the area under perspective distortion conditions. The larger the value of the coefficient, the more serious the deformation impact on the area under the perspective effect, and the geometric consistency of the width, length, and edge characteristics of the fracture has been distorted to a large extent, resulting in an increase in the recognition error; conversely, if the value is small, it indicates that the perspective distortion degree of the area is low, and the fracture morphology is relatively close to its true geometric characteristics and can be recognized and evaluated more accurately. Specifically, the fracture perspective distortion coefficient combines the light intensity characteristic information and the spatial displacement information. When the edge gradient intensity is high and the light intensity fluctuates greatly, and the centroid offset also increases significantly, the coefficient value will increase, indicating that significant morphological distortion has occurred in the area and further correction is required; while when the gradient, illumination, and position offset remain relatively stable, the coefficient value is low, indicating that the area morphology is relatively stable and belongs to the low-distortion area.

[0032] Extract the contour structure information in the imaging parameter information of each area in the preprocessed fracture image, specifically including the total length of the fracture contour at different times within a period of time in each area of the fracture image, the difference between the maximum and minimum values of the internal pixels, and the amount of change in the edge curvature, and calibrate them as , and , represents the The total length of the crack profile at a certain moment within a period of time for a certain area represents the difference between the maximum and minimum values of the internal pixels at a certain moment within a period of time for the th area in the crack image The difference between the maximum and minimum values of the internal pixels at a certain moment within a period of time for a certain area represents the change in edge curvature at a certain moment within a period of time for the th area in the crack image The change in edge curvature at a certain moment within a period of time for a certain area represents the change in edge curvature at a certain moment within a period of time for the The total length of the crack profile, the difference between the maximum and minimum values of the internal pixels, and the change in edge curvature at different moments within a period of time for each area in the crack image can be obtained in real time through image processing and computer vision techniques and implemented in a software environment (such as OpenCV, MATLAB). First, the total length of the crack profile can be obtained by extracting the crack boundary through edge detection (such as the Canny operator), identifying the region contour using a contour detection algorithm (such as findContours), and combining a perimeter calculation function (such as arcLength) to obtain the real-time contour length. Second, the difference between the maximum and minimum values of the internal pixels can be obtained through grayscale histogram analysis. First, convert the crack region into a grayscale image, and then use pixel statistical methods to calculate the difference between the maximum and minimum pixel values to reflect the change in regional contrast. Finally, the change in edge curvature can be obtained by combining edge detection with a curvature calculation method (such as second-order difference or polynomial fitting). Calculate the local curvature of the boundary pixels and analyze its change in consecutive frames. Use the optical flow method to track key points to monitor the dynamic change of the edge shape. These methods can be executed in real time through multi-threaded processing and GPU acceleration technology, providing accurate data for subsequent crack distortion analysis.

[0033] Calculate the crack profile distortion index for each area. The specific calculation formula is as follows: ; In the formula, is the crack profile distortion index for the th area

[0034] This calculation formula is in this form to comprehensively evaluate the degree of change in the contour shape of each area in the crack image under perspective distortion conditions and ensure the scientificity and accuracy of the evaluation process. First, the purpose of the exponential operation is to amplify the influence of the total length of the crack profile on distortion, and at the same time consider the difference between the maximum and minimum values of the internal pixels As a normalization factor, it prevents extreme values from affecting the calculation results. Adding "1" to the denominator can avoid division-by-zero errors and ensure the stability of the calculation. This operation can reflect the change in the length of the crack profile due to perspective distortion. At the same time, through the amplification effect of the exponential operation, it can more sensitively capture the subtle changes in the contour deformation. Secondly, the logarithmic operation is used to balance the change in edge curvature and its impact on the overall distortion degree. Since there may be large fluctuations in the local curvature of the crack edge, the logarithmic operation can smooth its influence, ensure the stable growth of the value, and avoid the distortion effect being ignored due to too small a value. In addition, adding "1" can prevent the problem of inability to calculate the logarithm when it is zero, ensuring the robustness of the mathematical calculation. Finally, the method of taking the time average is to balance the distortion fluctuations at different time points, reduce the interference of abnormal data at a single moment, make the calculation results more globally representative, and ensure that the crack profile distortion index can accurately reflect the distortion situation of the region over the entire time range. Therefore, this calculation method can comprehensively and accurately evaluate the morphological distortion degree of the crack region while ensuring the stability of data calculation, providing a reliable data basis for subsequent distortion correction.

[0035] The size of the crack profile distortion index of the nth region directly reflects the morphological distortion degree of the region under perspective distortion conditions. The larger the value, the more significant the change in the total length of the crack profile at different time points, and at the same time, the contrast difference of the internal pixels is large, and the edge curvature changes violently, indicating that the contour shape of the crack has undergone large deformation and distortion due to perspective distortion, resulting in an increase in the irregularity of the crack edge and an increase in the recognition difficulty. Therefore, a high value of indicates that there is a relatively serious morphological distortion in this region, and corresponding correction measures need to be taken, such as morphological restoration or geometric correction; on the contrary, if the value is small, it means that the crack profile length is stable, the pixel contrast change is small, and the edge shape is relatively smooth, indicating that this region is less affected by perspective distortion and the morphology remains basically stable, and the true geometric features of the crack can be recognized more accurately.

[0036] In this embodiment, for the crack perspective distortion coefficients and the crack profile distortion indices of each generated region, a morphological distortion model is constructed, and the morphological distortion coefficient of each region is generated through weighted summation. The specific calculation formula is: , where and are the crack perspective distortion coefficients and the crack profile distortion indices of each region respectively non-zero weight coefficients, and ; The purpose of constructing the morphological distortion model by weighted summation is to comprehensively evaluate the overall impact of each region of the fracture image on perspective distortion and contour distortion, so as to ensure that the evaluation of morphological distortion is both comprehensive and accurate. The fracture perspective distortion coefficient mainly measures the geometric distortion caused by the change of shooting angle, such as morphological stretching or compression, while the fracture contour distortion index is used to evaluate morphological characteristics such as the curvature change and structural integrity of the fracture boundary. Therefore, reasonable weight allocation needs to be carried out for the two to balance the influence of different distortion factors. By introducing non-zero weight coefficients and , and satisfying , the system can flexibly adjust the contribution ratio of the two according to different geological conditions. For example, when the perspective distortion is significant, increase the weight of , and when the morphological change of the fracture edge is large, increase the weight of . In the specific implementation process, first, through image processing techniques (such as edge detection, feature extraction, etc.), the and of each region are obtained in real time, and then a linear weighted operation is performed at the software level to finally generate the morphological distortion coefficient , and compare it with the preset threshold interval. According to the comparison result, the fracture region is divided into low-distortion, medium-distortion, and high-distortion regions, providing a scientific basis for subsequent automatic correction.

[0037] Determine the preset threshold interval of the morphological distortion coefficient , and after determination, compare it with the morphological distortion coefficients of each generated region. According to the comparison result, judge the morphological distortion degree of each region in the fracture image under the condition of perspective distortion, and divide each region in the fracture image into low-distortion regions, medium-distortion regions, and high-distortion regions according to the judgment result. The specific comparison analysis and division are as follows: If , the morphological distortion degree of this region in the fracture image under the condition of perspective distortion is low, and this region is divided into a low-distortion region; This situation means that the perspective distortion of this region has little impact on the fracture morphology, and the width, length, and edge features of the fracture basically remain in their original state without obvious deformation or distortion. At this time, the extraction and recognition accuracy of fracture features is high, which can accurately reflect the actual geometric features of the fracture, thus providing reliable data support for subsequent geological analysis, disaster assessment, and engineering design, reducing the possibility of misjudgment, not requiring additional correction processing, and saving computing resources and time.

[0038] If , the degree of morphological distortion of this area in the fracture image under perspective distortion conditions is medium, and this area is classified as a medium distortion area; This situation means that the degree of morphological distortion of this area under perspective distortion conditions is at a medium level, and certain degrees of stretching, compression, or edge curvature changes begin to appear in the geometric features of the fracture morphology, which may have a certain impact on the fracture recognition accuracy. At this time, the system needs to perform moderate morphological correction on this area to reduce the influence of distortion on the fracture geometric information, thereby improving the reliability of recognition. If the correction is not carried out in a timely manner, it may lead to evaluation deviations in the subsequent fracture propagation trend, affecting the accuracy of geological disaster risk prediction.

[0039] If , the degree of morphological distortion of this area in the fracture image under perspective distortion conditions is high, and this area is classified as a high distortion area.

[0040] This situation means that this area is severely affected by perspective distortion, and the morphological features of the fracture show a large degree of distortion, which may lead to problems such as fracture contour breakage, splicing errors, and morphological misjudgment. Such areas require more complex geometric correction methods, such as multi-view image fusion, deep learning reconstruction, or model matching methods based on known fracture morphologies, to restore the true geometric features of the fracture. If this area is not effectively corrected, it may lead to serious engineering safety hazards, such as misestimation of the fracture scale, affecting the decision-making of key projects such as mining and tunnel construction.

[0041] The pre-set threshold interval of the morphological distortion coefficient can be determined through statistical analysis, machine learning modeling, and experimental calibration, etc., to ensure that the threshold can accurately reflect the degree of morphological distortion of the fracture image under different perspective distortion conditions. First, statistical analysis methods can be used to process a large amount of fracture image data, extract the fracture perspective distortion coefficient and fracture contour distortion index of each area, and calculate statistical indicators such as their mean and variance. The critical values for dividing low, medium, and high distortion areas are set by setting quantiles or standard deviation ranges. Secondly, machine learning modeling can be adopted. A classification model (such as support vector machine SVM, random forest, or neural network) is trained using a large amount of labeled data. The and data of each area are input, and the distribution pattern of the morphological distortion coefficient under different distortion degrees is learned, and the optimal threshold interval is automatically generated. In specific implementation, the classification boundary of the model can be optimized through cross-validation to ensure the stability and accuracy of the threshold interval. Finally, with the help of the experimental calibration method, on the reference image with known true fracture morphology, low, medium, and high distortion areas are manually selected, and the corresponding Values are used to form an empirical threshold library, and a reasonable threshold range is determined through similarity matching methods in the new image. These methods can be implemented through automated data analysis, model training, and verification processes in software environments (such as Python, MATLAB, etc.), thereby ensuring the scientificity, objectivity, and adaptability of the morphological distortion coefficient threshold.

[0042] Morphological adaptive correction module. According to the division results of each region in the fissure image, a fissure morphology correction mechanism is constructed, and corresponding correction measures are taken for the low-distortion region, medium-distortion region, and high-distortion region respectively. In this embodiment, in the morphological adaptive correction module, according to the division results of each region in the fissure image, a fissure morphology correction mechanism is constructed as follows: Different image transformation, feature correction, and edge optimization parameters are set respectively according to the division results of the low-distortion region, medium-distortion region, and high-distortion region to form a fissure morphology correction mechanism; based on the morphological distortion coefficients of each region and the performance of the image processing algorithm, the image transformation method, feature correction strategy, and adjustment amplitude of edge optimization are automatically determined through preset rules. In the morphological adaptive correction module, according to the division results of each region in the fissure image, a fissure morphology correction mechanism can be implemented by combining adaptive adjustment based on image processing algorithms, optimization of machine learning models, and rule-driven parameter adjustment to ensure the correction accuracy and adaptability of the fissure morphology under different distortion degrees. In the specific implementation process, first, lightweight correction is performed on the low-distortion region through image processing techniques (such as affine transformation, perspective transformation, and geometric transformation), and the fissure image is adjusted in scale, rotation, and perspective using preset morphological transformation parameters to correct slight morphological deviations. Secondly, in the medium-distortion region, combined with feature correction techniques, the geometric features of the fissure are extracted and compared through edge detection (such as Canny operator) and feature point matching (such as SIFT, ORB, etc.), and the morphological correction is performed using a deformation model (such as Thin Plate Spline, TPS) to ensure the accuracy and consistency of the fissure boundary features. In addition, in the high-distortion region, deep learning algorithms (such as convolutional neural network CNN or autoencoder network Autoencoder) are used to learn and reconstruct the fissure morphology, and combined with multi-scale image registration techniques (such as pyramid optical flow method), refined correction is performed on the severely distorted region to enhance the integrity and recognizability of the fissure morphology. The implementation of this method can minimize the impact of perspective distortion on fissure recognition, ensure that the system can obtain accurate fissure geometric information under different geological conditions and shooting angles, improve the correction efficiency at the same time, and provide high-quality data support for subsequent fissure analysis and prediction.

[0043] Corresponding correction measures are taken for the low-distortion region, medium-distortion region, and high-distortion region respectively. Specifically: In the low-distortion region, lightweight geometric correction is performed using the standard image transformation parameters in the fissure morphology correction mechanism to reduce perspective errors; in the medium-distortion region, the standard feature correction parameters in the fissure morphology correction mechanism are maintained, combined with geometric adjustment and texture enhancement strategies to ensure the accuracy of the fissure boundary; in the high-distortion region, the enhanced edge optimization parameters in the fissure morphology correction mechanism are used, combined with deep learning reconstruction algorithms and multi-scale image registration methods to enhance the recovery accuracy of the fissure morphology and reduce recognition deviations caused by severe distortion.

[0044] The corresponding correction measures for the low-distortion region, medium-distortion region, and high-distortion region can be achieved through a combination of adaptive adjustment based on image processing, feature extraction and matching techniques, and deep learning models to ensure precise correction of the fissure morphology under different distortion degrees, reduce recognition errors, and improve detection accuracy. Specifically, in the low-distortion region, lightweight geometric correction methods such as affine transformation and perspective transformation are adopted, and correction is performed based on standard image transformation parameters to fine-tune the scale, rotation, and perspective relationship of the image, reduce geometric distortion caused by perspective deviation, ensure the basic consistency of the fissure morphology, and at the same time use smoothing techniques such as bilinear interpolation to prevent information loss during the correction process; in the medium-distortion region, optimization is carried out through feature correction combined with geometric adjustment and texture enhancement. Techniques such as edge detection (e.g., Canny operator), Harris corner detection, or SIFT feature extraction are used to identify the boundary features of the fissure and perform feature point matching. Subsequently, a deformation correction model (such as Thin Plate Spline, TPS) is used for non-rigid adjustment, and at the same time, image texture information is enhanced through histogram equalization, gamma correction, etc. to improve the clarity of the fissure boundary and ensure the integrity of its geometric morphology; in the high-distortion region, it is necessary to combine deep learning reconstruction algorithms and multi-scale image registration methods. The deep learning of the fissure morphology features is carried out through a pre-trained deep neural network (such as U-Net, CNN) to reconstruct the original fissure morphology. At the same time, the optical flow method or pyramid Kanade matching algorithm is used for multi-scale image registration to extract the global morphology of the fissure from images of different scales, further reducing structural errors caused by severe distortion. The purpose of adopting these methods is to provide hierarchical and precise correction strategies for different degrees of morphological distortion, ensure the authenticity and reliability of the fissure morphology, and then improve the accuracy of subsequent fissure recognition and analysis, providing more accurate data support for geological disaster prediction and engineering decision-making.

[0045] Calibration efficiency evaluation module, during the process of the crack shape calibration mechanism calibrating each region in the crack image, it obtains the calibration error information of each region in the crack image in real time, analyzes it after obtaining, evaluates whether the calibration effect of the crack shape calibration mechanism on each region in the crack image can meet the expectation, and optimizes the crack shape calibration mechanism according to the evaluation result; In this embodiment, in the calibration efficiency evaluation module, during the process of the crack shape calibration mechanism calibrating each region in the crack image, it obtains the calibration error information of each region in the crack image in real time and performs preprocessing after obtaining; extracts the calibration offset information and shape consistency information from the preprocessed calibration error information, analyzes them after extraction, and respectively generates the crack calibration offset coefficient and crack calibration consistency index for each region; constructs a calibration effect evaluation model for the generated crack calibration offset coefficient and crack calibration consistency index of each region, generates the calibration evaluation coefficient for each region, analyzes it after generating, evaluates whether the calibration effect of the crack shape calibration mechanism on each region in the crack image can meet the expectation, and optimizes the crack shape calibration mechanism according to the evaluation result.

[0046] In the calibration efficiency evaluation module, the real-time acquisition of the calibration error information of each region in the crack image can be achieved through image registration, feature matching, and statistical analysis techniques. The specific methods include using the phase correlation method or the optical flow method to align the crack images before and after calibration to ensure the consistency of the positions of each region, and detecting the changes in the crack contour, texture, and edge features through feature matching algorithms such as SIFT and ORB to calculate the calibration error, such as centroid offset, contour overlap, and gradient change. The necessity of preprocessing is that the calibration error information may contain noise and outliers, and direct analysis may affect the accuracy. Therefore, normalization, denoising, and smoothing processing are required to improve the data quality. The specific preprocessing includes: (1) using Gaussian filtering to reduce random noise and smooth the crack boundary; (2) adopting min-max normalization to unify the data range and enhance comparability; (3) using the box plot method to remove outliers to prevent interference with the calibration effect evaluation. Through these preprocessing measures, the stability and accuracy of the data are ensured, providing a reliable basis for the subsequent optimization of the calibration mechanism.

[0047] The extraction of the calibration offset information and morphological consistency information from the preprocessed calibration error information can be achieved through techniques such as image feature analysis, morphological processing, and statistical modeling. Specifically, the extraction of the calibration offset information can calculate the key point displacement of the crack image before and after calibration through the optical flow method or feature point matching method (such as SIFT, ORB), and combine the centroid calculation technology to obtain the overall offset of the pixel positions within the region, and measure the overall displacement trend of the crack edge through vector analysis. At the same time, the extraction of the morphological consistency information can extract the crack boundary based on contour detection (such as Canny edge detection), and use the Hausdorff distance or Dice similarity coefficient to quantify the overlap degree of the contours before and after calibration to evaluate the consistency of calibration. In addition, texture analysis techniques (such as gray-level co-occurrence matrix GLCM) can be used to calculate features such as contrast and energy of the crack region to further evaluate the internal consistency of the crack region after calibration. During the extraction process, the sliding window method needs to be used to analyze the crack image region by region, extract local feature changes, and screen out regions with significant offsets and consistency deviations by setting thresholds, so as to ensure the accurate evaluation of the calibration effect. These methods can be automatically executed in image processing software (such as OpenCV, MATLAB) to achieve efficient and accurate extraction of calibration errors.

[0048] In this embodiment, the acquisition logic of the crack calibration offset coefficient and the crack calibration consistency index for each region is as follows: Extract the calibration offset information from the preprocessed calibration error information, specifically including the pixel density change rate, boundary offset vector length, and gray-level gradient variance of each region in the crack images at different times during a period of time in the process of crack morphology calibration, and calibrate them respectively as , and , represents the pixel density change rate of the th region in the crack image at the th moment during a period of time in the process of crack morphology calibration, represents the boundary offset vector length of the th region in the crack image at the th moment during a period of time in the process of crack morphology calibration, represents the gray-level gradient variance of the th region in the crack image at the th moment during a period of time in the process of crack morphology calibration, , , and are all positive integers; During the calibration process of the crack morphology calibration mechanism, the pixel density change rate, the length of the boundary offset vector, and the gray gradient variance of each region in the crack images at different times within a certain period can be achieved through image processing and feature analysis techniques. The pixel density change rate can extract the crack region through image binarization (such as Otsu threshold method), and count the change of the number of pixels per unit area over time to evaluate the expansion or contraction trend of the crack morphology. The length of the boundary offset vector can extract the crack boundary using edge detection (such as Canny operator), and track the boundary changes at different times through feature point matching (such as SIFT, ORB algorithms), calculate the offset distance of the centroid, so as to quantify the adjustment amplitude of the crack boundary. The acquisition of the gray gradient variance depends on image gradient operation (such as Sobel operator), calculate the gray gradient of the crack region at different time points, and count its variance to measure the texture consistency inside the calibrated region. To improve the accuracy of the data, the sliding window block processing of the image can be used, combined with the time series smoothing algorithm to remove noise, ensuring the stability and reliability of the obtained data. These methods can be automatically implemented through software tools (such as OpenCV, MATLAB), providing accurate data support for crack calibration evaluation.

[0049] Calculate the crack calibration offset coefficient of each region. The specific calculation formula is as follows: ; In the formula, is the crack calibration offset coefficient of the th region; The reason for adopting this way in the calculation formula is mainly to comprehensively and accurately evaluate the offset degree of each region during the crack morphology calibration process, so as to quantify the accuracy of the calibration effect. First, in the numerator part of the formula, uses the product of the pixel density change rate and the length of the boundary offset vector. Such a processing method can comprehensively consider the expansion or contraction of the crack region morphology before and after calibration, and at the same time combine the overall change of the boundary position, making the calculation of the offset degree more comprehensive and avoiding local deviation that may be caused by a single index. In addition, the square operation of the product term is introduced to amplify larger offset errors, making the regions with significant deviations more prominent in the calculation process, which helps to quickly identify high-error regions and ensure the sensitivity of the evaluation. Second, in the denominator part, The variance of the gray - level gradient is used as the calibration normalization factor, where adding "1" prevents the problem of zero denominator and ensures the computational stability is maintained even when the gradient change is small. The variance of the gray - level gradient measures the texture consistency of the fracture area before and after calibration. The larger the gradient change, the greater the morphological change in the calibrated fracture area. Therefore, the weight of the impact on the overall calibration offset should be reduced to avoid misjudgment caused by over - correction. Finally, the overall calculation result is smoothed by taking the square - root operation to reduce the impact of extreme values on the calculation result, while improving the robustness and stability of the calculation. Finally, by taking the average value in the time dimension, it is ensured that the evaluation result has temporal consistency and representativeness, avoiding the deviation at a single time point from affecting the overall judgment and ensuring the scientificity and reliability of the calibration effect.

[0050] The fracture calibration offset coefficient of the th region directly reflects the quality of the calibration effect of the fracture morphology calibration mechanism on this region. The smaller the value, the higher the consistency of the pixel density distribution, boundary position, and gray - level gradient in this region before and after calibration, indicating that the distortion of the fracture morphology has been effectively corrected, the calibration effect is good, and it approaches the expected target; on the contrary, if

[0051] the value is large, it indicates that there is still a significant offset in the fracture morphology of this region after calibration, manifested as excessive pixel density change, significant deviation of the boundary position, or inconsistent texture features, indicating that the calibration mechanism fails to fully eliminate perspective distortion or morphological distortion, the calibration effect is not ideal, and it may be necessary to further optimize the calibration parameters or adopt a more refined calibration strategy.

[0051] Extract the morphological consistency information in the pre - processed calibration error information, specifically including the edge - pixel coincidence degree, the change rate of the edge gradient, and the change rate of the fracture contour length in each region of the fracture images at different times during a period when the fracture morphology calibration mechanism is performing calibration, and they are respectively labeled as 、 and , represents the edge - pixel coincidence degree of the th region in the fracture image at time during a period when the fracture morphology calibration mechanism is performing calibration, represents the change rate of the edge gradient of the th region in the fracture image at time during a period when the fracture morphology calibration mechanism is performing calibration, represents the change rate of the fracture contour length of the th region in the fracture image at time during a period when the fracture morphology calibration mechanism is performing calibration; During the calibration process of the crack morphology calibration mechanism, the edge pixel coincidence degree, the change rate of the edge gradient, and the change rate of the crack contour length in different regions of the crack images at different moments within a period of time can be achieved through image processing techniques. The edge pixel coincidence degree can extract the crack boundary through edge detection algorithms (such as Canny or Sobel operators), and use feature matching methods (such as ORB, SIFT) to register the images before and after calibration, calculate the proportion of matching pixels, and evaluate the boundary consistency. The change rate of the edge gradient can calculate the gray gradient of the crack boundary at different time points through gradient operations (such as Sobel operators), and measure the sharpness change through normalized difference to identify whether the calibration causes edge blurring or oversharpening. The change rate of the crack contour length can calculate the perimeter change of the crack contour before and after calibration through contour detection (such as contour extraction based on morphological closing operation) to evaluate the degree of morphological stretching or compression. These methods can be automatically executed with the help of image processing libraries such as OpenCV to ensure real-time and accurate acquisition of calibration evaluation data.

[0052] Calculate the crack calibration consistency index for each region. The specific calculation formula is as follows: ; In the formula, is the crack calibration consistency index of the

[0053] This calculation formula is adopted in this way to comprehensively and accurately evaluate the impact of the crack morphology calibration mechanism on the morphological consistency of each region, and to ensure that different degrees of distortion are reasonably measured. First of all, the edge pixel coincidence degree in the numerator part measures the matching degree of the crack edge pixels before and after calibration, directly reflecting the consistency of the crack morphology. The higher the value, the better the boundary calibration effect. However, the change in boundary sharpness will also affect the consistency. Therefore, the edge gradient change rate is introduced in the denominator for normalization to prevent excessive influence on the matching degree when the gradient change amplitude is large, and to avoid misjudgment caused by too high or too low edge sharpness. Secondly, taking the logarithmic operation on the crack contour length change rate is aimed at smoothing the influence of the contour length change before and after calibration, avoiding sudden deviations in the overall consistency evaluation caused by excessive morphological stretching or compression, and ensuring that even small changes can be effectively identified. Finally, the calculation results at all moments are smoothed through time averaging operation to reduce the accidental error of single measurement and improve the stability and reliability of the evaluation results. In summary, this formula combines the boundary coincidence degree, the gradient change rate, and the contour change rate to ensure a comprehensive evaluation of the calibration consistency from three aspects: edge, texture, and overall morphology, and to ensure the comparability and accuracy of the crack morphology at different calibration stages.

[0054] The Fissure correction consistency index of a region The size directly reflects the morphological consistency restoration effect of the fissure morphology correction mechanism on this region. The smaller the index, the higher the consistency of the fissure morphology before and after correction, the higher the matching degree of edge pixels, the smaller the change in edge gradient, and the reasonable adjustment range of contour length, indicating a better correction effect, accurate retention of fissure features, and meeting the expected goal; conversely, if the value is large, it means that there are still significant inconsistencies after fissure correction. For example, the edge matching degree is low, the gradient changes violently, and the contour length changes greatly, indicating that there may be a large deviation in the corrected fissure morphology, the true features of the fissure are not accurately restored, and the correction effect is not ideal.

[0055] In this embodiment, for the fissure correction offset coefficients of each generated region and the fissure correction consistency index a correction effect evaluation model is constructed, and the correction evaluation coefficient of each region is generated by weighted summation , and the specific calculation formula is: , where and are the non-zero weight coefficients of the fissure correction offset coefficient and the fissure correction consistency index of each region respectively and the fissure correction consistency index , and ; In the correction effect evaluation model, the weight coefficients and are used to measure the importance of the fissure correction offset coefficient and the fissure correction consistency index in the overall correction evaluation respectively. reflects the impact of the offset degree of the fissure region on the correction effect. If the correction process causes a large boundary displacement, a higher weight needs to be given to highlight its impact; then reflects the contribution of morphological consistency to the evaluation. If the fissure morphology needs to be highly maintained after correction, this weight should be increased. The sum of the two is fixed at 1 to ensure the balance of the evaluation coefficient. The specific values of the weights can be adjusted according to experimental data or experience, usually optimized through historical evaluation results to achieve adaptability to different fissure conditions.

[0056] Determine the correction evaluation coefficient thresholds of each region set in advance , and after determination, compare with the correction evaluation coefficients of each generated region to evaluate whether the correction effect of the fissure morphology correction mechanism on each region in the fissure image can meet the expectation according to the comparison result, and optimize the fissure morphology correction mechanism according to the evaluation result. The specific comparison and analysis are as follows: If , the correction effect of the crack morphology correction mechanism on the region in the crack image can achieve the expected effect, and there is no need to optimize the crack morphology correction mechanism; This means that the fracture morphology correction mechanism has achieved the expected correction effect in this area, and the fracture morphology, boundary and texture characteristics have reached a state that is highly consistent with the actual situation. The correction error is within an acceptable range and no further adjustment of the correction strategy is required. Its impact is that it can ensure the accurate identification and evaluation of fracture structures in geological exploration or engineering applications, reduce additional computing overhead and unnecessary adjustment work, and thus improve the operating efficiency and reliability of the system.

[0057] like The correction effect of the crack morphology correction mechanism on this area in the crack image cannot achieve the expected effect, and the crack morphology correction mechanism needs to be optimized, including: adjusting the image transformation parameters to optimize the perspective distortion correction in the morphology correction process and reduce the geometric distortion caused by changes in shooting angles; improving the feature correction algorithm, by enhancing the accuracy of feature point matching and texture analysis, improving the accuracy of the crack boundary, reducing boundary dislocation and detail loss; enhancing the edge optimization strategy, using multi-scale edge detection and adaptive smoothing technology, so as to improve the accuracy and consistency of crack identification and ensure that the correction effect meets the expected standards.

[0058] This situation means that the fracture morphology correction mechanism has failed to achieve the expected correction effect in this area, and there may be problems such as blurred fracture boundaries, distorted shapes or dimensional distortions, resulting in reduced recognition accuracy. The impact is that if the correction mechanism is not optimized in time, it may lead to incorrect identification of fracture features, which in turn affects the accuracy of geological hazard assessment or engineering construction planning, and increases potential safety hazards or construction costs. Therefore, the correction mechanism needs to be adjusted to ensure that the corrected fracture morphology is consistent with the actual situation.

[0059] The optimization of the crack morphology correction mechanism can be achieved by combining image processing, deep learning, and optimization algorithms to reduce geometric distortion caused by changes in the shooting angle and improve the accuracy and consistency of crack recognition. First, in terms of adjusting image transformation parameters, a method based on homography matrix estimation (such as the RANSAC algorithm) can be used. By detecting feature point pairs in the images before and after correction, the optimal perspective transformation matrix is calculated to geometrically correct the crack images, thereby compensating for the morphological distortion caused by the viewing angle deviation. Second, in terms of improving the feature correction algorithm, deep learning techniques (such as CNN convolutional neural networks) can be introduced to perform high-precision learning and matching of the texture features in the crack region. Feature point detection algorithms such as SIFT and ORB are used to enhance the accuracy of key point localization. Combining with the optical flow method to evaluate the boundary changes after correction, ensuring the accuracy of the crack boundary, and reducing boundary misalignment and detail loss. Finally, in terms of enhancing the edge optimization strategy, multi-scale edge detection techniques (such as the combination of the Canny operator and the LoG operator) can be used to detect the crack edges at different scales to ensure the integrity of the boundary details. Combining with adaptive smoothing algorithms (such as bilateral filtering or guided filtering) to balance the sharpness and smoothness of the edges, reduce noise interference, and improve the consistency of the crack morphology. Through the above methods, the correction parameters can be automatically adjusted at the software level to make the correction of the crack morphology more in line with the actual situation, avoiding overcorrection or undercorrection, and ultimately ensuring the accuracy and reliability of the recognition results.

[0060] The presetting of the correction evaluation coefficient thresholds for each region can be achieved by combining statistical analysis, machine learning, and optimization algorithms. First, based on a large amount of historical crack image data, mean-variance analysis or confidence interval estimation is used to calculate the reasonable ranges of the crack correction offset coefficient and the consistency index, and the empirical thresholds are initially set. Second, through K-means clustering or Gaussian mixture model (GMM), the corrected crack regions are classified to automatically identify the threshold ranges of different distortion degrees. Finally, genetic algorithms or particle swarm optimization algorithms are applied, combined with the feedback of the correction effect, to dynamically adjust the thresholds to ensure accuracy and robustness. These methods can be automatically executed through software tools (such as OpenCV, Scikit-learn) to achieve the intelligent and adaptive adjustment of threshold setting.

[0061] The recognition data management module completes the automatic recognition and detection of cracks according to the optimized crack morphology correction mechanism, and records and stores the results of crack recognition and detection and the application process of the correction mechanism for subsequent monitoring, analysis, and optimization of crack morphology changes.

[0062] The implementation of the identification data management module can combine database management, automated data processing, and deep learning techniques to ensure the accurate storage and efficient management of fracture identification and detection results. First, a database management system (such as MySQL, MongoDB) is used to establish a structured storage scheme to classify and store fracture detection results, calibration parameters, and evaluation data, and a timestamp index is used for subsequent query and retrospective analysis. Second, data processing tools (such as Pandas, NumPy) are used to preprocess the stored data, including data cleaning, normalization, and trend analysis, to support long-term monitoring and change analysis of fracture morphology. Finally, in combination with deep learning frameworks (such as TensorFlow, PyTorch), the stored fracture images and detection results are trained and optimized to continuously improve the identification accuracy, and predictive analysis is performed based on historical data to identify potential fracture propagation trends. This approach can not only achieve the efficient management of fracture identification results but also optimize the subsequent identification process in a data-driven manner, ensuring the self-adaptability and long-term monitoring ability of the system and improving the reliability of geological safety assessment.

[0063] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0064] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be solid-state drives.

[0065] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not imply the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0066] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0067] In several embodiments provided by the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the above-described embodiments are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0068] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0069] In addition, the functional units in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0070] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A geological structure crack automatic identification and detection system based on deep learning, characterized in that: It includes crack image analysis module, distortion intelligent evaluation module, morphology adaptive correction module, correction efficiency evaluation module and recognition data management module; The crack image analysis module, during the geological exploration process, scans the rock surface through the camera to obtain the image information of the cracks in real time, and detects whether the cracks are distorted due to the perspective effect in the obtained crack image information. When it is detected that the crack morphology is distorted due to the perspective effect, the crack image is evenly divided into several areas; The distortion intelligent evaluation module acquires the imaging parameter information of each area in the crack image in real time, analyzes it after acquisition, determines the degree of morphological distortion of each area in the crack image under the condition of perspective distortion, and divides each area in the crack image into low distortion area, medium distortion area and high distortion area according to the judgment result; The morphology adaptive correction module constructs a crack morphology correction mechanism according to the division results of each area in the crack image, and performs corresponding correction measures on the low distortion area, medium distortion area and high distortion area respectively; The correction performance evaluation module acquires the correction error information of each area in the crack image in real time during the process of the crack morphology correction mechanism correcting each area in the crack image, and analyzes it after acquisition to evaluate whether the correction effect of the crack morphology correction mechanism on each area in the crack image can meet the expectations, and optimizes the crack morphology correction mechanism according to the evaluation results; The identification data management module completes the automatic identification and detection of cracks according to the optimized crack morphology correction mechanism, and records and stores the results of crack identification and detection and the application process of the correction mechanism for subsequent monitoring, analysis and optimization of crack morphology changes.

2. According to claim 1, a geological structure crack automatic identification and detection system based on deep learning is characterized in that: In the distortion intelligent assessment module, after the imaging parameter information of each area in the crack image is acquired in real time, it is preprocessed; the light intensity feature information and contour structure information in the imaging parameter information of each area in the preprocessed crack image are extracted, and analyzed after extraction to generate the crack perspective distortion coefficient and crack outline distortion index of each area respectively; a morphological distortion model is constructed for the generated crack perspective distortion coefficient and crack outline distortion index of each area, and the morphological distortion coefficient of each area is generated, and analyzed after generation to judge the morphological distortion degree of each area in the crack image under the perspective distortion condition, and according to the judgment result, each area in the crack image is divided into a low distortion area, a medium distortion area and a high distortion area.

3. The deep learning-based automatic identification and detection system for geological structure cracks according to claim 2 is characterized in that: The logic for obtaining the crack perspective distortion coefficient and crack outline distortion index of each region is as follows: The light intensity feature information in the imaging parameter information of each area in the preprocessed crack image is extracted, including the pixel brightness value, the offset of the centroid coordinates and the average gradient intensity of the edge pixels in each area of ​​the crack image at different times within a period of time, and calibrated as , and , Indicates the crack image area within a period of time The pixel brightness value at the moment, Indicates the crack image area within a period of time The offset of the center of mass coordinates at the moment, Indicates the crack image area within a period of time The average gradient strength of edge pixels at the moment, , , and All are positive integers; Calculate the crack perspective distortion coefficient of each area. The specific calculation formula is as follows: In the formula, For the The slit perspective distortion coefficient of each region; The contour structure information in the imaging parameter information of each area in the preprocessed crack image is extracted, including the total length of the crack contour of each area in the crack image at different times within a period of time, the difference between the maximum and minimum values ​​of the internal pixels, and the change in edge curvature, and they are calibrated as , and , Indicates the crack image area within a period of time The total length of the crack profile at time, Indicates the crack image area within a period of time The difference between the maximum and minimum values ​​of the internal pixels at the moment, Indicates the crack image area within a period of time The change of edge curvature at the moment; Calculate the crack profile distortion index of each area. The specific calculation formula is as follows: In the formula, For the The fracture contour distortion index of a region.

4. The deep learning-based automatic identification and detection system for geological structure cracks according to claim 3 is characterized in that: The crack perspective distortion coefficients for each generated area and crack profile distortion index Construct a morphological distortion model and generate the morphological distortion coefficients of each region by weighted summation , the specific calculation formula is: ,in and are the crack perspective distortion coefficients of each region and crack profile distortion index The non-zero weight coefficient of ; Determine the preset morphological distortion coefficient threshold interval , and after determination, the morphological distortion coefficients of each region generated The comparison is performed, and the degree of morphological distortion of each area in the crack image under the perspective distortion condition is judged according to the comparison results, and each area in the crack image is divided into low distortion area, medium distortion area and high distortion area according to the judgment results. The specific comparison analysis and division are as follows: like , the morphological distortion degree of this area in the crack image under the perspective distortion condition is low, and this area is divided into a low distortion area; like , the morphological distortion degree of this area in the crack image under the perspective distortion condition is medium, and this area is divided into a medium distortion area; like , the degree of morphological distortion of this area in the crack image under the perspective distortion condition is high, and this area is divided into a high distortion area.

5. The deep learning-based automatic identification and detection system for geological structure cracks according to claim 4 is characterized in that: In the morphological adaptive correction module, a crack morphological correction mechanism is constructed according to the division results of each area in the crack image. Specifically, different image transformation, feature correction and edge optimization parameters are set according to the division results of low-distortion area, medium-distortion area and high-distortion area to form a crack morphological correction mechanism. The crack morphological correction mechanism is based on the morphological distortion coefficient of each area and the performance of the image processing algorithm, and automatically determines the image transformation method, feature correction strategy and edge optimization adjustment range through pre-set rules. Corresponding correction measures are taken for low-distortion areas, medium-distortion areas, and high-distortion areas respectively. Specifically, in low-distortion areas, the standard image transformation parameters in the crack morphology correction mechanism are used to perform lightweight geometric correction to reduce perspective errors; in medium-distortion areas, the standard feature correction parameters in the crack morphology correction mechanism are maintained, combined with geometric adjustment and texture enhancement strategies to ensure the accuracy of crack boundaries; in high-distortion areas, the enhanced edge optimization parameters in the crack morphology correction mechanism are used, combined with deep learning reconstruction algorithms and multi-scale image registration methods to enhance the restoration accuracy of crack morphology and reduce recognition bias caused by severe distortion.

6. The deep learning-based automatic identification and detection system for geological structure cracks according to claim 5 is characterized in that: In the correction performance evaluation module, in the process of the crack morphology correction mechanism correcting each area in the crack image, the correction error information of each area in the crack image is obtained in real time, and preprocessing is performed after acquisition; the correction offset information and morphological consistency information in the preprocessed correction error information are extracted, and analyzed after extraction to generate the crack correction offset coefficient and the crack correction consistency index of each area respectively; a correction effect evaluation model is constructed for the generated crack correction offset coefficient and the crack correction consistency index of each area, and the correction evaluation coefficient of each area is generated, and analyzed after generation to evaluate whether the correction effect of the crack morphology correction mechanism on each area in the crack image can meet expectations, and the crack morphology correction mechanism is optimized according to the evaluation results.

7. The deep learning-based automatic identification and detection system for geological structure cracks according to claim 6 is characterized in that: The logic for obtaining the crack correction offset coefficient and crack correction consistency index of each region is as follows: The correction offset information in the preprocessed correction error information is extracted, specifically including the pixel density change rate, boundary offset vector length and grayscale gradient variance of each area in the crack image at different times over a period of time during the correction process of the crack morphology correction mechanism, and calibrated as , and , Indicates the period of time during which the crack morphology correction mechanism is correcting The moment crack image The pixel density change rate of the area, Indicates the period of time during which the crack morphology correction mechanism is correcting The moment crack image The length of the boundary offset vector of the region, Indicates the period of time during which the crack morphology correction mechanism is correcting The moment crack image The gray gradient variance of the region, , , and All are positive integers; Calculate the crack correction offset coefficient of each area. The specific calculation formula is as follows: In the formula, For the The crack correction offset coefficient for each region; The morphological consistency information in the preprocessed correction error information is extracted, including the edge pixel coincidence, the change rate of the edge gradient and the change rate of the crack contour length in each area of ​​the crack image at different times during a period of time during the correction process of the crack morphology correction mechanism, and calibrated as , and , Indicates the period of time during which the crack morphology correction mechanism is correcting The moment crack image The edge pixel overlap of the region, Indicates the period of time during which the crack morphology correction mechanism is correcting The moment crack image The rate of change of the edge gradient of a region is Indicates the period of time during which the crack morphology correction mechanism is correcting The moment crack image The rate of change of the fracture contour length in each region; Calculate the crack correction consistency index of each area. The specific calculation formula is as follows: In the formula, For the The crack-corrected consistency index for each region.

8. The deep learning-based automatic identification and detection system for geological structure cracks according to claim 7 is characterized in that: Correction offset coefficients for cracks in each generated region and gap-corrected consistency index Construct a correction effect evaluation model and generate correction evaluation coefficients for each area through weighted summation , the specific calculation formula is: ,in and The crack correction offset coefficients for each region are and gap-corrected consistency index The non-zero weight coefficient of ; Determine the pre-set correction evaluation coefficient thresholds for each area , and after determination, the correction evaluation coefficients for each region generated A comparison is performed, and based on the comparison results, it is evaluated whether the correction effect of the crack morphology correction mechanism on each area in the crack image can meet the expectations, and the crack morphology correction mechanism is optimized based on the evaluation results. The specific comparison and analysis is as follows: like , the correction effect of the crack morphology correction mechanism on the region in the crack image can achieve the expected effect, and there is no need to optimize the crack morphology correction mechanism; like The correction effect of the crack morphology correction mechanism on this area in the crack image cannot achieve the expected effect, and the crack morphology correction mechanism needs to be optimized, including: adjusting the image transformation parameters to optimize the perspective distortion correction in the morphology correction process and reduce the geometric distortion caused by changes in shooting angles; improving the feature correction algorithm, by enhancing the accuracy of feature point matching and texture analysis, improving the accuracy of the crack boundary, reducing boundary dislocation and detail loss; enhancing the edge optimization strategy, using multi-scale edge detection and adaptive smoothing technology, so as to improve the accuracy and consistency of crack identification and ensure that the correction effect meets the expected standards.

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