572 results about "Geometric transformation" patented technology

A Convolutional Neural network (CNNs) based system and method based on the same for image analysis including for computer aided detection / diagnosis is provided, enabling an efficient and accurate delineation of diseased volumes in images / medical images. More particularly, the system of the present invention is adapted to incorporate deformable convolution (DC) module based processor means, along the down- and up-sampling pathways of the underlying U-Net architectural framework of said system for effective understanding and attending to deformable geometry of unknown transformations, considering deformable convolution (DC), unlike the basic convolution is advantageously not constrained by predefined geometrical structures of the kernels. Said system of the present invention advantageously also permits weighted combination of image feature components along the encoder and decoder arms of the deformable convolution (DC) processor module based system architectural network due to introduction of Weight Generation (WG) modules in said system, such that dynamic assignment of importance to relevant spatial locations of the corresponding image feature maps could be given so as to also boost overall accuracy while ensembling to enable reduction in module analytic error by simultaneously maintaining the generalization in performance in respect of robustness to noise to attain accurate and faster demarcation of the ROI (region of interest). Added to the aforesaid, preferred integration of Focal Asymmetric Similarity (F AS) loss function analytic based processor module / means in said system, allowed effective handling of class imbalance for further improved performance.

The invention relates to the technical field of camera motion compensation, and discloses an IMU-based camera motion compensation method and device and a storage medium, and the method comprises the steps: processing original data collected by a plurality of IMU sensors, and obtaining six-degree-of-freedom IMU fusion data; processing through a linear convolution aliasing model to obtain a high-dimensional motion feature vector; performing fusion to obtain a camera motion parameter prediction value; constructing an advanced network compensation model, carrying out jitter intensity evaluation, and dynamically adjusting a compensation model parameter matrix; performing geometric transformation to obtain an initial stable image sequence and a foreground mask; according to the method, IMU data and image information can be effectively fused, camera motion parameters can be accurately estimated, and a compensation strategy can be dynamically adjusted according to scene features to realize camera motion compensation.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention provides a multi-modal data urban land utilization scene classification method based on street scene image matching remote sensing image, designs a cross-view image matching method based on geometric transformation, and realizes spatial position matching of a multi-view street scene image and a remote sensing image; the method comprises the following steps: constructing an urban land utilization scene classification model based on a high-resolution remote sensing image and a streetscape image by utilizing a pre-trained large model with relatively strong generalization ability, and extracting depth features of different scales and different visual angles; designing a global-local feature fusion module, and aggregating features of different levels through self-attention; a gate control cycle fusion module is designed to realize interaction of streetscape images at different shooting time and shooting view angles; through a channel attention mechanism, remote sensing image features and streetscape image features are distributed according to different weights, contribution proportions of the features in different classification tasks are optimized, a dynamic gradient modulation strategy is designed, and different modal information is adaptively fused, so that accurate classification of urban land utilization remote sensing images is realized.

The invention provides a geometric correction method and device for a satellite remote sensing image and a storage medium, and relates to the technical field of image correction, and the method comprises the steps: obtaining remote sensing image data of at least two different spectral bands, synchronously recording satellite attitude parameters and orbit position parameters, and carrying out the verification processing; based on the satellite attitude parameters and the orbit position, geometric coordinate conversion is carried out on each remote sensing image data, and target remote sensing image data is generated; dividing each piece of target remote sensing image data into a plurality of continuous grid units, and constructing a local geometric transformation model at the vertex of each grid unit; and resampling the original pixels in each grid unit according to the local geometric transformation model, and outputting a plurality of pieces of target remote sensing image data subjected to geometric correction. According to the invention, the geometric correction precision of the high-resolution satellite remote sensing image can be improved.

The invention discloses a pavement crack detection method based on a Yolov8 model, and belongs to the technical field of crack detection. Comprising the following steps: constructing a pavement crack segmentation data set: acquiring a road crack image through an unmanned aerial vehicle, performing data enhancement processing of geometric transformation and color transformation on the image in combination with a public data set, performing Gaussian filtering denoising on a noise image, and performing crack labeling by using Label; based on a YOLOv8-Seg model, improvement is carried out by introducing a PKIblock multi-scale convolution kernel, generalizing an efficient layer aggregation network GELAN module, an EMA attention mechanism and replacing a spatial pyramid pooling layer SPPF into a SimSPPF, and a road crack recognition model YOLOv8-RCI is constructed; and performing crack detection and instance segmentation on the unmanned aerial vehicle image by using the trained YOLOv8-RCI model, and outputting a crack position and mask information. Through the improvement in the four aspects, the detection segmentation performance of the model is improved, and the model meets the requirement of real-time detection.

The invention provides a textile pattern dislocation identification method, which comprises the following steps of: performing geometric transformation analysis on extracted pattern contour information, calculating a relative position relationship of each part of a pattern, establishing a pattern moisture deformation model, and judging whether the pattern is dislocated or deformed by comparing the difference between the current pattern and a reference model; on the basis of the corrected pattern, adding a special waterproof mark on the surface of the wet textile, manufacturing a microscopic mark which does not influence the fabric performance, and capturing the position of the waterproof mark through infrared imaging equipment to serve as an auxiliary reference point for pattern positioning; and based on the waterproof mark position of the pattern, tracking the motion trail of the waterproof fabric, calculating the image distortion degree according to the motion vector field, correcting the moving waterproof mark fabric image in real time, and eliminating the influence of motion blur on waterproof mark identification.

The invention discloses an automobile part quality detection method and system based on artificial intelligence visual inspection, and belongs to the field of artificial intelligence machine visual inspection, and the method comprises the steps: firstly, carrying out the registration of a collected RGB image and a depth image, and extracting a part region through a saliency detection network; two-dimensional key points are extracted based on an RGB region image and are matched with key points of a three-dimensional model, an initial three-dimensional attitude is obtained by adopting a PnP algorithm, iterative registration is performed with the three-dimensional model in combination with a point cloud generated by a depth image, and a fine three-dimensional attitude is obtained. And calculating a geometric transformation matrix from the part to a standard front view attitude according to the attitude, and performing attitude correction on the RGB and depth region image. And then matching the corrected image with a standard template image by using a feature detection and matching network so as to correct the position of the detection window. And finally, the three-dimensional size of the part is calculated in the corrected detection window in combination with the depth value, and tolerance judgment is carried out. And the precision, the robustness and the automation level of online detection of the automobile parts can be obviously improved.

The invention provides a bonding wire defect detection method based on deep learning, which can be used for identifying the defect type of a bonding wire and automatically detecting the bonding quality problem of the wire in an integrated circuit product, and aims to solve the problems of high labor intensity, low detection efficiency and the like. The process comprises the following steps: establishing a bonding wire acquisition physical platform, acquiring a depth image of a bonding wire through a stereo camera, preprocessing the acquired depth image to enhance the image quality, generating a normal diagram corresponding to the depth image through geometric transformation, and stacking the normal diagram and the depth diagram to obtain a final bonding wire data set. Performing data enhancement on the bonding wire data set to expand the data scale; a Depth-YOLO network model is provided, an input layer of an original YOLO model is reconstructed, and an input feature enhancement module is designed; and finally, training a Depth-YOLO model by using the self-built data set, and carrying out end-to-end bonding wire defect detection by using the model.

The invention provides a face image recognition method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the fusion of a topological relation feature vector and a biological feature contour, calculating the curvature manifold of the concave region of five sense organs, and generating a local curvature feature vector through differential geometric transformation; inputting the topological relation feature vector, the local curvature feature vector and the micro feature coding sequence into an evolvable biological feature library, and fusing a space-time weight to update a comparison template; fusing the micro-feature coding sequence, the topological relation feature vector and the local curvature feature vector to obtain a biological feature set, and calculating the multi-scale similarity between the biological feature set and the updated comparison template; and if the multi-scale similarity exceeds an adaptive threshold, generating a face recognition result. According to the invention, the stability and accuracy of face recognition in a complex illumination environment are improved.

The invention discloses a virtual fitting video generation method and system based on multi-view face fixation, and relates to the field of generative artificial intelligence and computer vision, and the virtual fitting video generation method based on explicit geometric constraints comprises the following steps: S1, obtaining an original fitting image and an action cue word, and constructing a multi-source input data set; s2, segmenting the original fitting image to obtain multi-view modeling, and generating a multi-angle face image and a clothing texture feature parameter; s3, analyzing the action cue word to generate a target posture sequence, and generating head and tail frame virtual fitting images; s4, performing pairing analysis on the virtual fitting images of the head frame and the tail frame to generate attitude transition parameters; s5, performing dynamic texture repair on the image to generate a transition frame image sequence; and S6, generating a virtual fitting video and outputting the virtual fitting video. According to the method, accurate segmentation and multi-angle modeling of the face area and the clothing area are realized through image segmentation and a rigid geometric transformation algorithm.

The invention belongs to the technical field of defect identification, and particularly relates to a gas turbine blade defect identification method based on an improved YOLOV8 network. Comprising the following steps: capturing a defect image; effective information is extracted from the image or subsequent target detection, classification and segmentation task requirements are met; performing data enhancement through geometric transformation and color perturbation; adding structured labels or annotations to the images, and endowing the images with semantic information; the scheme is improved on the basis of YOLOv8 so as to be realized in gas turbine blade defect detection, and training of the model is completed in a distributed heterogeneous computing framework by using an acquired training data set and an acquired verification data set and is used for a prediction task. The method can achieve the pixel-level detection and positioning of the defects of the blade, can effectively meet the daily detection demands, and gives consideration to the detection precision and real-time performance.

The invention discloses an unmanned aerial vehicle inspection image registration and alignment method based on deep learning, and belongs to the technical field of image processing. Comprising the steps of video acquisition and segmentation processing, semantic-space-time alignment and synchronization subsequence selection, content enhancement and alignment frame pair extraction, multi-source key point extraction and fusion, cross-video high-quality feature matching and geometric transformation estimation and registration, and solves the technical problem of high-precision frame alignment and matching in cross-view and asynchronous image sequences. The invention provides a semantic-spatio-temporal combined image sequence alignment method, which realizes high-robustness alignment of cross-sequence image frames, and enables matching to be more inclined to frame pairs in time proximity, thereby effectively inhibiting semantic interference of cross-time drift, enhancing physical rationality of a matching path, and improving matching accuracy. The robust alignment capability under asymmetric sampling, speed change and visual angle deviation in the real flight process is remarkably improved.

The invention discloses an inter-frame image segmentation method and device based on a recurrent neural network, and a medium, and relates to the technical field of image processing, and the method comprises the steps: extracting the local feature information of a current continuous inter-frame image through a convolutional neural network; constructing a geometric transformation model through the Lie group theory according to the local feature information, and extracting geometric change information of the target object in the image based on the geometric transformation model; based on the geometric change information, fusing the dynamic information of the adjacent inter-frame images through a recurrent neural network to extract time sequence features between the adjacent inter-frame images; and performing segmentation prediction on each inter-frame image in the current continuous inter-frame images through a segmentation network according to the time sequence characteristics. According to the method, the Lie group is used for capturing continuous geometric transformation between image frames, and the accuracy of the shape and the edge of a target object is kept, so that the segmentation method has better robustness for geometric transformation, and particularly, the method is excellent in performance when processing complex backgrounds and rapid motion scenes.

The invention discloses an isovariant consistency image fusion method and system based on task driving, and the method comprises the steps: carrying out the feature decomposition of an input infrared image and a visible image, obtaining the basic features and detail features, and carrying out the geometric transformation operation meeting the isovariant consistency constraint in the subsequent fusion processing, so as to guarantee the transformation stability of the features. And performing semantic constraint on the fusion model by utilizing supervision information of a semantic segmentation task of the semantic segmentation model. According to the method, the equal-variant consistency constraint and the semantic loss are collaboratively optimized through the loss function part, the robustness of geometric transformation can be kept, meanwhile, the reservation of local details and the enhancement of global semantic information are effectively considered, a high-quality fused image is generated, and the performance of a downstream advanced visual task is improved.

The invention provides a building identification method and system based on three-dimensional point cloud, and particularly relates to the technical field of computer vision and three-dimensional target identification, and the method comprises the steps: carrying out the undersampling, oversampling and data enhancement preprocessing of inputted building roof point cloud data, and carrying out the T-net network space alignment to output the aligned data. Then, an NEWPCT model is obtained by improving a PCT model, aligned data are input into the NEWPCT model, a point cloud feature matrix is generated through a linear coding layer, query, key and value matrixes are obtained through linear transformation, multi-head attention output is calculated by introducing an offset matrix, intermediate correlation features are obtained after splicing and fusion, and a global feature vector is generated through global maximum pooling; and finally, inputting the vector into a classifier, and outputting a building roof type, and the method solves the technical problems of geometric transformation sensitivity and local feature loss while improving the precision and robustness of three-dimensional point cloud building identification, thereby effectively improving the identification precision and robustness.

The invention relates to the technical field of medical detection, and discloses a capsule endoscope image auxiliary diagnosis method based on deep learning, which comprises the following steps: acquiring an original image sequence of an alimentary canal shot by a wireless capsule endoscope, screening out an image sequence related to the alimentary canal in the original image sequence, and labeling the image sequence; sequentially performing data enhancement, geometric transformation and standardization processing on the marked image sequence, and clustering a prior anchor frame by using a K-means algorithm to form a capsule endoscope image data set; a deep learning model LD-YOLO is built, a YOLOv5 model is selected as a basis, optimization is carried out on the basis of the basic model, and a capsule endoscope image auxiliary diagnosis model is built; and training a capsule endoscope image auxiliary diagnosis model through the capsule endoscope image data set, and detecting an image to be diagnosed by using the trained capsule endoscope image auxiliary diagnosis model. According to the invention, the workload of doctors in image analysis can be greatly reduced, and the film reading efficiency is improved.

A method for identifying, locating, and mapping targets of interest using unmanned aerial vehicle (UAV) camera footage in GPS-denied environments. In one embodiment, the method comprises obtaining UAV visual data, passing the UAV visual data through a convolutional neural network (CNN) in order to detect targets of interest based on visual features disposed in the UAV visual data, wherein the detection by the CNN defines reference points and pixel coordinates for the UAV visual data, applying a geometric transformation to known and defined pixel coordinates to obtain real-world orthogonal positions; and projecting the detected targets of interest onto an orthogonal map based on the obtained real-world orthogonal positions, all without GPS data.

The invention discloses a real-time data stream processing method for multi-device collaborative management of a dome cinema, which relates to the technical field of cinema device data management, and comprises the following steps of: establishing communication connection between a central management node and a plurality of playing devices, collecting device identifiers, projection area numbers and geometric position parameters of the playing devices, and establishing a central management node; generating an equipment mapping relation table; realizing local clock correction of the playing equipment based on a unified time synchronization protocol; the central management node generates a control instruction stream according to the equipment mapping relation and the playing content, the instruction stream comprises space frame distribution information and fusion control parameters, and the space frame distribution information and the fusion control parameters are distributed to the playing equipment through communication channel numbers; and the playing device completes image geometric transformation and edge fusion processing according to the received control instruction and then executes synchronous playing. According to the invention, multi-device automatic mapping is realized, the time synchronization precision is improved, the image playing is coordinated and consistent, and the collaborative display capability and the playing stability of the dome cinema are improved.

The invention provides a brain tumor classification method based on deep learning, and the method comprises the steps: uniformly segmenting a multi-mode magnetic resonance imaging image into a plurality of small blocks, generating an enhanced puzzle through random geometric transformation, inputting the enhanced puzzle into a ResNet50 backbone network, extracting primary local features, and optimizing feature distribution; multi-scale feature maps of different stages are obtained, lightweight convolution blocks are constructed, and local pathological relevance is enhanced; performing full-connection layer splicing on the multi-scale feature maps in different stages, introducing a learnable weight matrix to dynamically allocate weights in each stage, and generating fusion features; a progressive parameter unfreezing mechanism is adopted to ensure orderly learning of the model from local to global; the sample weight is dynamically adjusted according to the category frequency, and the local discriminant force and the global consistency are balanced in combination with multi-stage classifier loss weighted fusion; and a final classification result is generated through multi-stage prediction probability weighted average and dynamic threshold adjustment, and clinical availability is improved in combination with a sigmoid calibration module.

The invention discloses a medical image semantic segmentation method based on attention mechanism optimization, and relates to the technical field of medical image processing, and the segmentation method comprises the specific steps: S100, data collection and label preprocessing: collecting medical image data from different medical institutions and a plurality of imaging devices, according to the method, the attention mechanism is introduced to carry out deep preprocessing on the medical image data, the precision and efficiency of semantic segmentation of the medical image are remarkably improved, the attention mechanism is utilized, key areas, such as diseased regions or tissue boundaries, in the image can be recognized and enhanced, meanwhile, noise and irrelevant information are effectively removed, and the accuracy of semantic segmentation of the medical image is improved. The refined preprocessing mode not only improves the quality of the image, but also provides a more accurate data basis for subsequent image detection and segmentation, and the method is also combined with a self-adaptive denoising algorithm, dynamic adjustment of contrast, brightness and color and a geometric transformation advanced preprocessing technology, so that the availability and diagnostic value of the image are enhanced.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

The invention provides a multi-modal remote sensing image progressive registration method and system, and the method comprises the steps: 1, obtaining a pre-disaster and post-disaster multi-modal remote sensing image pair of a disaster region, constructing a disaster scene multi-modal disaster remote sensing image registration data set, and dividing a training set and a test set; step 2, constructing a multi-scale depth feature extraction network, performing depth coding on the multi-modal remote sensing image pair, respectively extracting local details and global structure features, and generating a multi-scale feature pyramid; 3, constructing a progressive cross-modal transformation network based on the multi-scale feature pyramid, mining modal invariant features through a cross-modal attention mechanism, and regressing a multi-scale symmetric dense deformation field pixel by pixel; and 4, based on the multi-scale symmetric dense deformation field, training a deep learning model through a consistency loss function, performing geometric transformation on test set data by using the trained deep learning model, and outputting a registration result. According to the invention, automatic geometric correction of the multi-modal remote sensing image is realized.

The invention provides a Res-Transform urban solid waste incineration flame state recognition method based on multi-feature enhancement. The method comprises the steps of image acquisition and preprocessing, construction of an urban solid waste incineration flame state recognition model and urban solid waste incineration flame state recognition. According to the invention, through adoption of the preprocessing method of image denoising, image defogging and geometric transformation, training data are enriched, and the generalization ability of the model is improved at the same time; efficient automatic feature extraction and state recognition of the urban solid waste incineration flame image are realized through a deep learning technology based on multi-feature enhancement, and the recognition accuracy and robustness are improved by enhancing the detail capture capability and fusing different scale features; according to the method, the end-to-end automatic identification process is realized, the operation steps are simplified, the identification efficiency is improved, and the reliability and the stability of the urban solid waste incinerator system are remarkably improved.

The invention discloses a full-field non-contact health monitoring method for a large-scale structure. The method mainly comprises the following steps: S1, dividing monitoring sub-regions; dividing the to-be-monitored area into a plurality of to-be-monitored sub-areas; arranging a plurality of distributed channel discrete speckle interferometers to respectively and synchronously image and monitor each to-be-detected sub-region; s2, acquiring a speckle interferogram and a dual-channel color map; the monitored object is deformed; respectively collecting speckle interferograms and dual-channel color maps before and after deformation of the to-be-detected sub-region; s3, processing a speckle interferogram; processing the speckle interferograms before and after deformation, extracting a phase fringe pattern reflecting defect information of the to-be-detected sub-region, and determining a defect form; s4, splicing the monitoring data; splicing the two-color channel images shot by the sub-regions to be detected; and performing geometric transformation and fusion on the phase change information by using the two-color channel image splicing parameters obtained before and after splicing to generate full-field monitoring data, and obtaining the defect position and damage evolution of the monitored object.

The invention provides an image bilinear interpolation parallel optimization algorithm, system and device and a medium, and the algorithm comprises the steps: setting four storage regions which can be read at the same time in a clock period in an FPGA, carrying out the conversion according to the coordinates of an original pixel point, and obtaining a storage region and a storage address corresponding to the pixel point, and writing the image information corresponding to the original pixel point into the address to complete the storage of the original image. According to the coordinates of the transformed pixel points subjected to geometric transformation, storage areas and storage addresses corresponding to four original pixel points around each transformed pixel point are obtained, and image information stored in the four areas is read at the same time to complete bilinear interpolation operation; according to the method, image information is stored in a reasonable odd-even mode, it is guaranteed that four adjacent pixel points needed by interpolation calculation are located in four storage areas respectively, the operation efficiency is improved through the parallel characteristic, and reading and operation of the four pixel points are completed in each clock period.

The invention relates to the technical field of image processing, in particular to a rubber ring contour burr detection method and system based on geometric transformation, and the method comprises the steps: obtaining a rubber ring image, and extracting the outer contour of a rubber ring and the contour of each burr in the rubber ring image; fitting the outer contour of the rubber ring by adopting a least square method to obtain an ellipse, and mapping the ellipse into a standard circle: carrying out bilinear interpolation resampling on the rubber ring image, and carrying out polar coordinate transformation on the rubber ring image by taking the circle center of the standard circle as an original point to generate an expanded image; generating a reconstructed image with enhanced burr features from the expanded image through wavelet transform, determining burr parameters of the reconstructed image, mapping the burr parameters back to a Cartesian coordinate system of the rubber ring image, and generating a detection result containing burr number, position and size information; according to the invention, the accuracy and robustness of tiny burr detection can be improved.

The invention relates to a GIS (Gas Insulated Switchgear) equipment mechanical defect diagnosis method based on a Gramb angle field and a dual-channel PCNN-Attention neural network, and belongs to the technical field of gas insulated switchgear mechanical vibration defect diagnosis. The method solves the problems that traditional diagnosis depends on artificial feature extraction, so that subjectivity is high, information mining is insufficient, and defect severity evaluation is missing. According to the technical scheme, the method comprises the steps that a one-dimensional vibration signal is converted into a GASF two-dimensional image and a GADF two-dimensional image through a GASF field so as to completely reserve time sequence topological features; carrying out data enhancement by adopting an image geometric transformation technology so as to improve the generalization ability of the model; and a dual-channel PCNN-Attention model is constructed, and synchronous intelligent identification of defect types and severity is realized through parallel feature extraction and dynamic weight optimization of an attention mechanism. According to the method, the diagnosis accuracy, reliability and adaptive capacity are improved, and support is provided for equipment state operation and maintenance.

The present disclosure relates to intra prediction of a target block of a video picture based on template matching that compares a target template block to a candidate reference template block. The intra-frame prediction comprises the following steps: reconstructing sample availability according to a target block neighborhood and determining a candidate reference block neighborhood according to a geometric transformation type; obtaining a candidate reference template block by applying a geometric transformation identified by a geometric transformation type to the candidate reference block neighborhood; selecting a candidate reference block having a minimum cost obtained by comparing the candidate reference template block with the target template block; and generating an intra prediction block for the target block by applying a geometric transform identified by a geometric transform type associated with the selected candidate reference block to the selected candidate reference block.