178 results about "Structure tensor" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention provides a tunnel crack identification method and system based on multi-source image processing, and relates to the technical field of tunnel engineering, and the method comprises the steps: obtaining multi-source data of a horizontal rock stratum tunnel; performing spatial registration on the multi-source data to generate a multi-modal image under the same reference system; extracting multi-modal features according to the multi-modal image, and constructing a multi-source feature map under the same space grid; performing crack initial detection on the multi-source feature map in combination with multi-scale filtering and structure tensor analysis to obtain a crack candidate region mask; accurate crack identification is carried out through the crack candidate area mask, a crack identification result is obtained by combining bedding direction constraint and a microconditional random field, and the crack identification result comprises a crack segmentation map and a crack category. The method solves the problem that existing crack identification does not consider the bedding characteristics of the horizontal rock stratum tunnel.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention relates to the technical field of traditional Chinese medicinal material identification, in particular to a traditional Chinese medicinal material intelligent identification and grading system based on deep learning, which integrates image acquisition, feature extraction, expression optimization, identification evaluation and origin traceability into a whole. Curvature, structure tensor and spectral features are extracted in combination with a differential geometry theory; constructing a Riemannian manifold representation space and performing isometric embedding dimension reduction optimization; identifying the types of the medicinal materials by using a deep convolutional neural network, and comparing with a standard model to evaluate the quality grade; the origin discrimination is realized based on the multi-scale feature comparison of geodesic distance, the category, quality and traceability information of the medicinal materials are comprehensively output, the surface visual features and internal component information of the traditional Chinese medicinal materials are comprehensively utilized through a multi-source data fusion technology, and the feature expression ability and discrimination precision of the recognition system are comprehensively improved.

The invention discloses a urinary calculus CT image automatic segmentation method based on deep learning, particularly relates to the technical field of medical image processing, and is used for solving the problem of low geometric fidelity of a segmentation result caused by hardening artifacts when an existing deep learning segmentation method is used for processing a high-density urinary calculus CT image. The method comprises the following steps: acquiring a urinary calculus CT image, performing initial segmentation by using a deep learning model to generate an initial calculus segmentation region, evaluating texture heterogeneity degree and identifying a hardening artifact risk region by analyzing feature value distribution of a structure tensor field, and positioning an artifact-causing source point based on a CT imaging projection geometric principle by reversely tracing a spatial position relation. According to the method, boundary distortion features are identified by analyzing CT value profile curve form distortion features and local boundary curvature singularity features, geometric correction is performed on corresponding boundaries in an initial stone segmentation region according to the boundary distortion features, a final stone segmentation region is obtained, and the geometric accuracy and reliability of a segmentation result are effectively improved.

The invention belongs to the technical field of image processing, and particularly relates to a flour impurity screening method and system based on image processing, and the method comprises the steps: obtaining a surface grayscale image; obtaining a texture response value according to the gray scale standard deviation in the pixel point neighborhood and the local information entropy; adjusting the basic gradient amplitude by using the texture response value to obtain an enhanced gradient value; obtaining an impurity probability value according to the difference of the structure tensor characteristic values and the enhanced gradient value; constructing a background substrate by using morphological reconstruction, and obtaining a significance index according to a difference value between the impurity probability value and the background substrate; and performing segmentation and connected domain screening according to the significance index. While impurity edge signals are reserved, dust noise and natural fluctuation interference of flour are effectively filtered out, and the accuracy of detecting near-color impurities on the surface of the flour is improved.

The invention belongs to the technical field of image processing, and particularly relates to an oil and gas engineering supporting facility intelligent detection method based on machine vision, and the method comprises the steps: constructing a structure tensor matrix based on the gradient of pixel points, calculating a local linear structure response value according to two feature values of the structure tensor matrix, and calculating a local linear structure response value; according to the direction alignment degree of the feature vectors of the pixel points on the local path, the crack continuity of the pixel points is calculated, the morphological significance values of the pixel points are comprehensively obtained and used for adjusting basic contrast parameters, anisotropic diffusion processing is carried out on the to-be-detected image of the oil and gas facility based on the obtained self-adaptive contrast parameters, and the to-be-detected image of the oil and gas facility is obtained. An enhanced image is obtained through multiple times of iterative updating, and a defect detection result graph is obtained through edge detection. According to the method, the problem that false defects are easily misjudged as cracks in a traditional method is solved, and the detection accuracy and reliability are remarkably improved.

The invention belongs to the technical field of image enhancement, and particularly relates to an image enhancement method for VCSEL (Vertical Cavity Surface Emitting Laser) epitaxial wafer detection, which comprises the following steps of: analyzing local and global gray information and structure tensor characteristics of pixel points, and calculating a defect saliency value of each pixel point to quantify the possibility of the pixel point as a defect; interference of a stripe structure in an epitaxial wafer image is weakened; a weighted histogram is constructed based on the defect saliency value, so that the defect pixel occupies a higher weight in the histogram; and determining an optimal segmentation threshold value by searching an energy median point of the weighted histogram, and finally performing BBHE enhancement on the image by using the segmentation threshold value. According to the method, the contrast ratio of the tiny defects can be remarkably improved, a high-quality image is provided for subsequent epitaxial wafer detection, and the detection accuracy is improved.

The invention relates to the field of image processing, in particular to a box-type substation spraying quality detection method based on image processing, and the method comprises the steps: firstly obtaining a to-be-detected image, and calculating a pixel point structure tensor component after the to-be-detected image is preprocessed; extracting a gradient energy field and a gradient main direction angle field based on the component, and further calculating gradient radial consistency representing geometric characteristics of the defect; in combination with the to-be-detected image and the gradient energy field, respectively calculating saturation and flatness indexes representing optical reflection characteristics and intensity-gradient cross-correlation representing defect spatial forms; fusing the three features to generate a final saliency map; and finally, carrying out threshold segmentation and post-processing on the saliency map to obtain a detection result. According to the method, the physical spraying defect and the optical highlight reflection can be effectively distinguished, and the defect detection accuracy and robustness are remarkably improved.

The invention belongs to the technical field of image processing, and particularly relates to an aluminum bar quality detection method and system based on machine vision, and the method comprises the steps: obtaining a gray image of the surface of an aluminum bar; constructing a structure tensor for describing the distribution of the pixel points in the local gradient direction, and obtaining gradient coherence indexes of the pixel points according to feature values of the structure tensor; according to the gradient coherence index, the gray value of the pixel point and the gray value mean value and the gray value standard deviation in the neighborhood, obtaining coherence weighted saliency of the pixel point; according to the coherence weighted saliency and the coherence weighted saliency of all the pixel points in the pixel point neighborhood, acquiring a defect probability index of the pixel points; and performing aluminum bar surface quality discrimination according to the defect probability index. According to the method, through combination of gradient coherence and local brightness statistical characteristics, interference of specular reflection light spots on the surface of the aluminum bar and wiredrawing texture noise is effectively inhibited, and accuracy of detection of weak defects such as scratches is improved.

The invention relates to the technical field of industrial vision, in particular to an aviation part crack detection and repair method which comprises the following steps: acquiring an aviation part optical image at a reference time point and an aviation part optical image at a to-be-detected time point; according to the method, logarithmic polar coordinate transformation is carried out on different time point images, rotation, scaling and translation parameters are extracted, an image registration relation is established, the structural consistency of time sequence images in a local area is enhanced, a structural tensor is constructed for each pixel neighborhood, the change characteristics of the dual-time-phase tensor are compared, a structural change saliency map is generated, and the structural change saliency map is obtained. Sensitive capture of a tiny deformation area is achieved, the responsiveness to an initial crack is improved, then a crack propagation interval is further refined into a main crack path in a self-adaptive threshold segmentation and skeleton extraction mode, the tip acutance of the crack is calculated in combination with tip contour information of a geometric boundary of the crack, and the initial crack is obtained. And quantitative support is provided for the crack danger degree.

The invention relates to the field of image processing, in particular to an industrial feeding anomaly recognition method based on visual flow detection, and the method comprises the steps: obtaining an image sequence for optical flow calculation through the collection and preprocessing of an image sequence of an industrial feeding port; performing nonlinear fusion on the pixel brightness value and the time brightness change rate to obtain a highlight suppression factor; carrying out distribution analysis on the local gradient structure tensor characteristic value to obtain a gradient structure confidence factor; performing joint weighting on the highlight suppression factor and the gradient structure confidence factor to obtain a dynamic space-time confidence weight; weighted optical flow calculation and variance threshold judgment are carried out on the dynamic space-time confidence coefficient weight, a feeding abnormal state recognition result is obtained, and therefore the problems of optical flow calculation distortion and abnormal misinformation caused by mirror surface highlight in the SMT micro component feeding process are solved.

The invention relates to the technical field of geophysical exploration data processing, and discloses a multi-element prospecting data cleaning and feature extraction system based on machine learning, and the system comprises a structure field generation unit which is used for constructing a multi-scale structure tensor field according to three-dimensional seismic data and extracting a structure vector representing a geological structure; the self-adaptive cleaning unit is used for carrying out abnormal value judgment and interpolation on non-seismic data by using the structural vector as prior guide information; the coupling feature generation unit is used for generating single-scale and cross-scale coupling features capable of deeply quantizing the relationship between the attribute change and the structure by calculating the projection of the non-seismic data gradient on different-scale structure vectors; and the feature output unit is used for combining the cleaned data, coupling the features and the original seismic attributes and constructing a high-dimensional final feature vector. According to the method, input with more geological significance is provided for machine learning, so that the precision and reliability of prospecting prediction are remarkably improved.

The invention relates to the technical field of image segmentation, in particular to an anorectal focus automatic segmentation method based on deep learning, which comprises the following steps: acquiring an anorectal image pixel map, extracting contrast and direction offset to mark candidate focus points, screening overlapped marks to generate a focus activation mark map, and establishing a response map to generate a boundary response distribution map. And training the network to output a classification graph, and extracting a truncation path to complete image segmentation. According to the invention, through extracting the contrast value and the gradient amplitude of the local gray level co-occurrence matrix, accurate capturing of the spatial difference of the lesion area under a complex background is realized, through constructing a response map and direction consistency comparison mechanism and combining multi-dimensional features such as a direction gradient histogram and a structure tensor, the area discrimination capability and the edge classification precision are improved, and the accuracy of edge classification is improved. The texture stability is judged by means of anisotropic standard deviation, a fuzzy edge mask is set, truncation paths are screened in combination with a main direction vector included angle deviation trend, and continuity and stability of a boundary convergence position are ensured.

The invention relates to the technical field of image recognition, in particular to a carotid artery image analysis method and system based on deep learning and a medium. The method comprises the following steps: obtaining a heterogeneous carotid artery image set, and carrying out heterogeneous federal integrated processing to obtain a federal precoding image set; recognizing an image feature semantic co-occurrence mode based on the federal precoding image set so as to construct a high-order image semantic structure; performing multi-scale blood vessel semantic embedding on the federal precoding image set to obtain a multi-scale structure tensor; performing privacy enhancement contrast representation migration on the multi-scale structure tensor to obtain an encryption migration embedding set; and reversely mapping the encryption migration embedding set to a federal precoding image set, and carrying out image reconstruction scoring by taking a high-order image semantic structure as a reconstruction constraint, thereby obtaining a carotid artery image feature scoring matrix. The carotid artery image feature recognition accuracy and processing efficiency can be improved.

The invention discloses a fan blade defect detection method based on direction constraint edge extraction and texture discrimination, and the method comprises the steps: image enhancement processing: carrying out the brightness and contrast enhancement processing of an input original fan blade image, and obtaining an enhanced image; fan blade positioning: analyzing the main direction of the image by using a structure tensor, and positioning a fan blade area in combination with a direction constraint edge extraction method; and defect detection and extraction: performing morphological operation, connected domain analysis and texture fusion discrimination on the suspected region in the positioning result, and extracting a final defect region. The method is suitable for automatic identification of surface defects such as cracks and corrosion in an offshore wind power inspection image; the method has the advantages of high robustness, low false detection rate and adaptability to complex sea and sky backgrounds.

The invention relates to the technical field of computer vision, and discloses a precise medicine delivery method and system, and the method effectively strips illumination artifacts through the construction of a local gradient structure tensor field and an anisotropic screening mechanism, remarkably improves the robustness of a visual front end in a light and dark alternating environment, and improves the accuracy of medicine delivery. Rigid body motion constraint and Lie algebra continuous modeling are utilized, high-precision space-time alignment between heterogeneous sensors is realized on the premise that scene depth does not need to be recovered, hardware delay errors are eliminated, a physical-driven probability weight dynamic allocation mechanism is constructed by introducing a multipath scattering index and a structural information entropy flux, and a dynamic space-time alignment algorithm is established. Environment degradation is sensed in real time, the observation weight is adjusted in a self-adaptive mode, the influence of the non-line-of-sight multipath effect and visual texture missing is effectively restrained, high-precision inertial dead reckoning can still be maintained in a blind area where a sensor is in full failure in combination with momentum prior information generated through Schel complement marginalization operation, and the method has the advantages of being high in precision and high in precision. And the navigation continuity and reliability of the distribution robot in a complex scene are ensured.

The invention relates to the technical field of image processing, and discloses a warping process monitoring method and system based on image recognition, and the method comprises the steps: obtaining a yarn image in the operation process of a warping machine, and inputting the image into a monitoring model; generating a direction gradient prior graph, and calculating a structure tensor representing yarn texture complexity; discrete wavelet transform is carried out on the preorder layer feature map of each dense layer, and a high-frequency detail component and a low-frequency contour component are obtained through separation; performing weighted fusion on the high-frequency detail component and the low-frequency contour component to generate a fusion feature map; performing channel splicing on the directional gradient prior image and the fusion feature image, and inputting a splicing result into a sub-pixel convolution layer for up-sampling to obtain a high-resolution monitoring image; calculating a local anisotropy index of each pixel; and the local anisotropy index and the pixel gray value are combined to identify the yarn broken end or hairiness defect, and a monitoring result is output. According to the method, the reliability of detecting tiny defects such as broken ends and hairiness under a complex background is improved.

The invention relates to the technical field of image data processing, in particular to a flange forge piece surface defect detection method and system based on image processing, and the method comprises the steps: obtaining an original image of a flange forge piece, and carrying out the differential Gaussian band-pass filtering to obtain a preprocessed image; performing multi-scale structure tensor analysis on the preprocessed image, and determining a texture abnormal value according to a gradient magnitude and an included angle between a gradient direction and a local texture direction; extracting a background image through morphological reconstruction, and calculating a geometric suppression weight based on brightness difference; and fusing the texture abnormal value and the geometric suppression weight to obtain a defect response value, and identifying the surface defect through threshold segmentation. According to the method, through combination of multi-scale texture analysis and a geometric suppression mechanism, weak defects can be accurately extracted under a strong texture background, artifacts generated by a flange geometric structure are effectively suppressed, and the accuracy and robustness of defect detection are improved.

The invention discloses a holographic generation method and system based on generative artificial intelligence, and the method comprises the following steps: S1, obtaining image information and depth information of a target scene at different visual angles, and generating a structured tensor based on the image information and the depth information; s2, performing space-direction joint coding on the structured tensor generated in the step S1, and extracting a structural latent variable; s3, using the structure latent variable extracted in the step S2 as a control signal to generate a complex amplitude diagram of the target scene, the complex amplitude diagram including amplitude information and phase information; s4, based on a physical propagation model, reconstructing the complex amplitude graph generated in the step S3 into a multi-view image sequence; and S5, mapping and adapting the multi-view image sequence reconstructed in the step S4 to a terminal display device for output display. According to the invention, while the physical consistency is ensured, the high-quality complex amplitude image is generated by directly utilizing the actual light field data, and the multi-view real restoration is realized.

The invention discloses a spinning defect identification method and system based on machine vision, and relates to the technical field of spinning defect identification, and the method comprises the steps: obtaining a yarn backlight image through telecentric imaging, and determining the point diffusion pixel width through combining imaging parameters; extracting a yarn boundary, a yarn axis direction and a normal direction based on the gradient component and the structure tensor; constructing a normal sampling band, and forming a scattering imbalance amount along the yarn in a normalization mode; calculating direction energy in the hairiness analysis area and obtaining a hairiness main direction angle, and comparing the hairiness main direction angle with a yarn axis direction to obtain a spiral orientation angle sequence; obtaining a response coefficient for explaining the conventional scattering change through local arc length neighborhood fitting, and forming a residual sequence; and extracting abnormal points based on a statistical rule of residual amplitude and combining the abnormal points into a defect section. According to the invention, the accuracy and stability of yarn defect identification can be improved.

The invention relates to the technical field of image data processing, in particular to an MBR water production turbidity detection method and system based on image processing, and the method comprises the steps: obtaining an original image of an MBR water production pipeline, and extracting a water body region by using a mask matrix; calculating the local contrast of the pixel points relative to the neighborhood, constructing a suppression weight based on the noise standard deviation of the image sensor, and weighting the local contrast to obtain scattering response intensity; obtaining the texture disorder degree based on the ratio of the geometric mean value to the arithmetic mean value of the feature values of the structure tensor; the particle confidence is obtained based on the scattering response intensity and the texture disorder degree, the weighted particle confidence is obtained through weighting of a Sigmoid function, and the average value of the particle confidence is calculated to serve as the comprehensive turbidity so as to evaluate the water production state. The scattering response intensity and the texture disorder degree are fused, a weight suppression and soft threshold mechanism is introduced, pipe wall scratches and thermal noise interference are reduced, and the detection accuracy is improved.

The present application relates to the field of infrared image processing, and discloses an infrared small target detection method and device based on image information entropy and multi-scale local contrast measure, comprising: (1) constructing a local weighted information entropy operator to obtain a local weighted information entropy image of an original infrared image; (2) using the weighted information entropy image and the structure tensor theory to construct a background prior and a target prior to obtain a local saliency prior; (3) using the original thermal infrared image to design a local contrast coefficient to obtain a multi-scale local contrast image; (4) using the multi-scale local contrast image and the corresponding structure tensor to design an information filter; and (5) fusing the local saliency prior and the information filter to realize the detection of the infrared small target. The present application comprehensively utilizes the weighted image entropy operator, the multi-scale local contrast measure and the structure tensor theory, and can effectively suppress the background, enhance the target and improve the detection performance of the infrared small target.

The invention belongs to the technical field of condition judgment, and particularly relates to an electrode foil corrosion condition judgment method and system, and the method comprises the following steps: S1, obtaining a grayscale image of a to-be-analyzed electrode foil; constructing a structure tensor based on the composite gradient reflecting the local brightness and texture information of the pixel points, determining an anisotropic diffusion coefficient according to the structure tensor, and performing iterative anisotropic diffusion filtering processing on the grayscale image by using the anisotropic diffusion coefficient to obtain a filtered image; and S2, fusing the anisotropic diffusion coefficient determined for each pixel point with the pixel intensity of the filtered image to obtain a high-contrast corrosion significance map. According to the method, the contour edge information of the corrosion area can be kept while the complex texture and noise of the background area of the electrode foil image are smoothed, the contradiction between denoising and edge protection of a traditional filtering method is solved, the result of the corrosion condition is more reliable, and the automation level of electrode foil product quality detection is improved.

The invention relates to the technical field of image processing, in particular to a titanium frame container welding seam quality detection method and system. The method comprises the following steps: acquiring a surface image of a to-be-detected welding seam of the titanium frame container, determining a global gray variance of the surface image, and determining a differential scale parameter based on the global gray variance; constructing a smooth structure tensor of a target pixel point in the surface image by using the differential scale parameter, and performing characteristic decomposition on the smooth structure tensor to determine a coherence index and a main direction angle of the target pixel point so as to determine that the target pixel point is a linear path pixel or a dotted path pixel; utilizing the linear path pixels and the dotted path pixels to obtain a pore intensity distribution diagram composed of pore intensity values; and obtaining a weld quality detection result by using the crack enhancement diagram and the pore intensity distribution diagram. According to the technical scheme, the quality of the welding seam of the titanium frame container can be detected.

The invention provides a seismic fault identification method and system based on multi-attribute collaboration and spectral domain graph enhancement, and relates to the technical field of seismic exploration, and the method comprises the steps: carrying out the all-directional dip angle and azimuth angle scanning of a seismic data volume, extracting multi-scale guide field information through combining structure tensor decomposition, and executing anisotropic diffusion filtering; calculating characteristic value distribution through a characteristic value coherence algorithm based on the filtering data volume, and determining the structural consistency difference of adjacent seismic traces to obtain a fracture coherence attribute data volume; carrying out azimuth gather sorting and pre-stack time migration processing on the seismic data volume, extracting seismic wave dynamic response characteristics, carrying out Fourier series expansion on the azimuth change rate to obtain a crack indication information data volume, splicing the data volume and executing multi-scale three-dimensional convolution solution, and constructing a spatial dependency graph through a spectral clustering algorithm; spectral domain enhancement features are obtained through spectral domain graph transformation and frequency selective filtering reconstruction, and morphological connectivity analysis is executed to obtain a crack prediction result.

The invention relates to the technical field of image processing, and discloses a road-air cooperative pavement disease digital management and control system based on a three-dimensional model, and the system comprises a data collection module which carries out the graying and smoothing of an unmanned plane image, so as to achieve the multiplicative detrending, and obtains a detrending brightness graph; a window generation module calculates a structure tensor based on the de-trending brightness graph, determines a wheel track main direction, and generates a window set according to a preset window size and a step length in a vertical direction; a priori graph generation module calculates normalized double coherence in each window and performs aggregation to obtain window feature values, and a priori graph is formed through interpolation; an intermediate feature map generation module generates a spatial gain map by using the prior map, and performs point-by-point gating on the intermediate feature map; the training sample construction module determines a sampling probability according to the window characteristic value and selects a sample; and the network training module carries out weighted segmentation on the loss training network through the pixel weight, and outputs a pavement disease detection result in a reasoning stage.

The present invention discloses a method and system for blur removal and jitter correction in visual monitoring of super high-rise buildings, comprising the following steps: a visual sensor acquires a video stream at a fixed frame rate, and locates an initial region of interest (ROI) by mapping prior information; the spatial gradient field is calculated for the ROI image of the current frame, and a Gaussian-weighted two-dimensional gradient structure tensor matrix is ​​constructed; for the clear image sequence that passes blur detection, variational mode decomposition is then used in the time domain to decouple the displacement time series into eigenmode functions of different frequencies, and the low-frequency components are reconstructed to preserve the true deformation of the building; the reconstructed low-frequency displacement signal is output as the final monitoring result. The present invention employs a lightweight algorithm design throughout, significantly reducing the matrix operation dimension of subsequent processing through a dynamic ROI clipping mechanism; the structural tensor eigenvalues ​​are solved using a direct algebraic analytical method, avoiding complex matrix iterative decomposition.