804 results about "Image edge" patented technology

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention relates to the technical field of image defect detection, in particular to a building engineering quality detection and evaluation system based on machine vision, which comprises an image acquisition module, a crack main shaft identification module, a space packaging module, a structure overlapping detection module and a building quality evaluation module. According to the method, a crack boundary layer with a spatial positioning function is constructed through image edge scanning and pixel position marking, pixel segments with high continuity are screened to generate a path line, the continuity and representativeness of crack trend are improved, and a rectangular block is packaged to enhance organization and complete expression of cracks in a spatial level; performing coordinate comparison with a structure stress area to form corresponding association between defect influence and a structure function, judging the influence of the crack on a key area through a space coverage relation between a total block amount and an overlapping amount, and establishing multi-dimensional building quality grade division by combining crack form, position distribution and structure association information; and comprehensive evaluation of building structure defects from geometry to structure level is realized.

The invention relates to the technical field of nondestructive testing, in particular to a leak detection and partial discharge digital imaging detection method and system based on acousto-optic fusion, and the method comprises the following steps: based on channel microphone array sound wave data in a partial discharge signal suspicious region, extracting a sound wave abnormal section, positioning a sound source, matching image edge features, and synchronously marking; and analyzing the phase change of the multi-frequency signal to judge a sound source concentration area, tracking the moving trend of a disturbance point, and outputting an acousto-optic fusion positioning trend track. According to the method, the partial discharge feature recognition sensitivity is improved through high-frequency peak paragraph screening and dominant frequency recognition, the abnormal region positioning precision is enhanced in combination with image edge extraction and sound source space matching, and acousto-optic synchronous positioning and trend trajectory display are achieved through multi-frequency signal phase analysis and image frame disturbance tracking. Through fusion of frequency domain feature extraction, image recognition, dynamic comparison and other actions, the spatial precision of abnormal source recognition and the multi-source fusion analysis efficiency are improved, and the partial discharge traceability and dynamic monitoring capability are enhanced.

The invention relates to the technical field of power transmission line detection, and discloses a power transmission line key component defect identification method based on cloud edge cooperation. The method comprises the following steps: acquiring a multi-source inspection data set consisting of a power transmission line component image acquired by an unmanned aerial vehicle, edge end sensor data and a cloud historical defect database; at the edge end, extracting component region features through an image preprocessing algorithm, and generating environment correlation parameters by using a multi-modal feature fusion algorithm; and at the cloud, performing space-time correlation analysis on the historical defect database to generate a part defect evolution graph. And inputting the information into a cloud edge collaborative recognition model to obtain a component defect feature vector, constructing a multi-stage defect recognition network through a dynamic optimization algorithm, and outputting a component defect classification result and confidence. According to the method and the system, the accuracy, the real-time performance and the reliability of defect identification of the key component of the power transmission line are improved, and the method and the system have good application prospects.

The invention relates to the technical field of automatic driving, in particular to an automatic homeward voyage method for multi-sensor data fusion of an unmanned vehicle, which comprises the following steps: acquiring laser radar obstacle points, image edges and attitude data, completing synchronous registration, identifying space mapping, extracting track and boundary trends, screening forward sections, and judging track offset and trafficability. Screening an error band boundary, adjusting a steering instruction, analyzing path overlapping change, and outputting a path keeping label. According to the method, through time synchronization and space registration of various sensor data, high-precision fusion of the data under a unified space-time framework is realized, the reliability of environment perception is improved, accurate mapping of the data in a unified reference coordinate system is ensured, the reliability of path planning and decision making is enhanced, and the optimal advancing direction is determined by combining a homing point; the accuracy of return route planning is improved, the traffic feasibility is dynamically evaluated, the effective error band boundary is screened to improve the safety, and the reliability of the return route is ensured through track stability analysis.

The invention provides a rock image classification method based on edge enhancement and multi-scale feature fusion. By combining the edge enhancement and multi-scale feature fusion technology, the accuracy of mineral classification in the rock slice image is remarkably improved. According to the method, rock slice image interference is eliminated through filtering and denoising, and mineral particle edges are enhanced by fusing morphological top-hat transformation and a Laplace operator; and performing multi-scale pyramid decomposition on the enhanced image to extract high-frequency information, performing multi-direction response enhancement to generate a direction feature map, and performing channel-level fusion on the original image, the edge enhanced image and the direction feature map. And finally, inputting the fused image into a convolutional neural network to complete rock type classification. According to the method, mineral boundary expression is effectively enhanced, the classification accuracy is improved, and a reliable technical scheme is provided for geological analysis and lithology identification.

The invention discloses a circuit breaker image edge detection method fusing spatial constraint fuzzy clustering and lightweight network optimization, and the method carries out the local adaptive threshold calculation through combining spatial constraint FCM and Otsu algorithms, and optimizes the edge detection process of a Canny operator. According to the method, fuzzy classification is carried out on a circuit breaker image by adopting spatial constraint FCM to obtain a strong marginal probability graph; the circuit breaker image is subjected to block processing through local adaptive threshold calculation, a global threshold is generated through integration, and then the global threshold is input into a Canny operator for accurate edge extraction. In order to further improve the detection effect, a lightweight neural network PiDiNet is used to correct a Canny output image. According to the method, the edge detection precision of the circuit breaker image can be effectively improved, and the method is suitable for edge extraction tasks in high-noise and complex background environments.

The invention discloses a large-size grinding wheel concentricity error measurement method and system based on machine vision, and relates to the technical field of grinding wheel concentricity error measurement, and the method comprises the steps: calibrating an industrial CCD camera through PyCharm software, collecting a part of images of a grinding wheel, and carrying out the RGB three-channel extraction, gray conversion, filtering and binary segmentation processing of the collected images; secondly, a Canny edge operator is adopted to carry out coarse positioning on the edge of the image, and then a novel edge judgment criterion is established to construct an edge detection model with sub-pixel-level precision; and finally, carrying out feature fusion on sub-pixel contours of RGB channels, clustering extracted discrete point sets by using an improved DBSCAN algorithm, and then carrying out ring fitting by using improved RANSAC to realize accurate measurement of the inner and outer diameters and concentricity of the grinding wheel.

The invention discloses a graph neural network embedding method combining a heterogeneous graph and multi-modal data, a medium and equipment, and the method is characterized in that the method comprises the following steps: carrying out the feature initialization of nodes and edges in the heterogeneous graph, employing a text pre-training model, an image convolution network or an audio processing model to initialize the node features according to the node types, carrying out weight distribution and feature coding on the semantic edge, the image edge or the social edge according to the edge type; multi-modal data is mapped to the same potential space through a modal self-adaptive fusion mechanism, weights of different modals are dynamically adjusted, and the modal self-adaptive fusion mechanism comprises construction of a shared embedding space and a multi-head attention mechanism; performing weighted aggregation on node information based on heterogeneous edge types, updating node embedding through propagation of a multi-layer graph neural network, and defining different weighting functions according to the edge types in an aggregation process; and embedding and applying the final node to a downstream task, and optimizing the model through a task-related loss function.

The invention relates to the field of infrared image processing, in particular to an infrared blind pixel detection method based on temperature self-adaption and time-frequency domain synthesis, which comprises the following steps of: traversing an infrared image by using a sliding window to obtain gray values of pixel points around a current pixel point; carrying out image edge judgment by using a Sobel-Otsu algorithm based on working temperature dynamic adjustment, and reserving a non-image edge window to participate in blind pixel judgment; grouping pixel points in the window according to horizontal and vertical directions; calculating the alpha filtering value of the gray scale of each group of pixels; calculating the deviation between the gray value of each pixel in the window and the alpha filtering value of the corresponding row coordinate and column coordinate group; if the deviation is greater than an adaptive threshold, setting the blind pixel as a potential blind pixel, and constructing a potential blind pixel distribution matrix; a time domain accumulation method for pre-screening periodic noise based on FFT is adopted, periodic noise interference is eliminated through frequency domain analysis, and then high-probability blind elements are screened through time domain accumulation; and obtaining a final blind pixel detection result. The method can effectively cope with the working temperature change of the sensor and the noise interference in a complex environment, and remarkably improves the accuracy and anti-interference capability of blind pixel detection.

The invention relates to the field of remote sensing image processing and computer vision, and discloses a satellite image cascade matching method and system based on double-branch context awareness, and the method comprises the following steps: constructing a heterogeneous double-branch feature extraction network, extracting multi-scale detail features through a main feature network, and capturing global semantic information through a context coding network; fusing multi-scale main features and context features to construct a group-related cost body, and adaptively enhancing key region matching response in combination with a channel incentive mechanism; optimizing the cost body by adopting a cross-scale information transfer and multi-dimensional attention fusion strategy, and generating a multi-scale initial disparity map; image edge geometric features and semantic contexts are fused, and high-resolution parallax details are recovered through a multi-stage residual decoder. According to the method, the problem of matching fuzziness of a traditional method in weak texture, repeated structure and parallax abrupt change areas of a satellite image is solved, and the three-dimensional reconstruction precision and the edge detail integrity in a complex scene are remarkably improved.

The invention records a multi-scale attention and median enhancement semantic segmentation method, and the method comprises the following steps: S1, extracting the initial features of an original image, and forming the output features of an Inception block; s2, inputting the output features into a multi-scale feature enhancement module, and outputting multi-scale features; s3, inputting the multi-scale features into a median enhancement space channel attention module, and outputting an attention feature map; s4, generating a final fusion feature of the high-level feature and the low-level feature through the cross feature fusion block; s5, performing up-sampling on the attention feature map, adding the attention feature map with the final fusion feature, and performing up-sampling to obtain a fusion feature map; s6, performing up-sampling and splicing on the fused feature map, and outputting a spliced feature map; and S7, performing up-sampling and full-connection layer expansion on the spliced feature map to obtain a segmentation prediction map of the original image. According to the method, feature distribution information can be captured more comprehensively, and a clearer and more accurate original image edge segmentation result is obtained.

The invention discloses an image edge cutting method and device based on boundary detection, equipment and a storage medium, and relates to the technical field of image boundary detection, and the method comprises the steps: carrying out the preprocessing of an original image, and obtaining a target image; determining a scene type of the target image; based on an edge detection strategy corresponding to the scene type, performing edge detection on the target image to obtain an edge image; performing straight line detection on the edge image to obtain a plurality of candidate straight line segments; and based on a boundary positioning rule, determining a target boundary line segment from the plurality of candidate straight line segments, so as to carry out edge cutting on the original image according to the target boundary line segment. According to the invention, a more reliable image edge cutting effect can be generated.

The invention discloses a lightweight single-image super-resolution reconstruction method based on local and global feature collaborative enhancement perception, which is used for solving the problems of insufficient utilization of high and low frequency clues in single-image super-resolution, low feature fusion efficiency and difficulty in consideration of structural consistency and visual fidelity of reconstructed images. The network adopts a double-flow heterogeneous architecture; a local branch uses multi-type differential convolution explicit coding image edge and texture prior to enhance detail characterization capability; the global branch effectively models long-range dependency and low-frequency semantic information by integrating local, cross-regional and global multi-level spatial self-attention mechanisms. The frequency sensing fusion module provided by the invention generates a channel specific space weight based on frequency characteristics, self-adaptively fuses double-branch characteristics, accurately balances structure maintenance and detail enhancement requirements in a characteristic fusion process, and effectively reduces characteristic redundancy. According to the super-resolution network, the model complexity is remarkably reduced, high-resolution images which are consistent in structure, vivid in vision and rich in details can be generated while lightweight design is kept, and an effective scheme is provided for efficient and high-performance lightweight single-image super-resolution reconstruction model design.

The invention discloses a BEV perception optimization method based on semantic projection compensation and edge structure enhancement, and the method comprises the steps: constructing an image edge feature pyramid under convolution perception through an MSEII module, and enhancing the boundary significance of a weak texture region; and then image semantic embedding is extracted by using a deep semantic segmentation network, geometric projection is carried out based on camera-radar external participation internal reference, image semantic features are mapped to a point cloud space, and an SPE module is constructed to complete semantic completion and structure alignment. After the image and the point cloud features are fused in a unified BEV coordinate system, a spatial context relation is further modeled through sparse point convolution, and a three-dimensional target bounding box and a semantic tag are output through a decoupling detection head. According to the method, on the basis of guaranteeing model lightweight and deployment efficiency, the long-distance target detection capability and semantic consistency in a shielding scene are remarkably improved, and the method is suitable for multi-mode BEV perception application in other resource-constrained equipment such as a mobile robot.

The invention discloses a cloud type inversion method and system based on cavity window attention, and the method comprises the following steps: obtaining satellite remote sensing image data, extracting multi-dimensional spectral features, converting the latitude and longitude information of an image into spatial features through a position coding module, carrying out the feature splicing of the two features, and obtaining a fusion feature; a convolutional layer is input to extract basic features of image edges, textures and the like, a plurality of cavity window attention modules are stacked, each cavity window attention module comprises a cavity window division module and a weighted attention recovery module, the cavity window attention modules are used for extracting multi-scale image features, image resolution is recovered step by step through up-sampling, feature representation is refined, and a multi-scale image is obtained. According to the method, the features are mapped to the category space, the classification probability is output, cloud type inversion with higher accuracy and robustness is achieved, and an effective solution is provided for cloud information processing in the remote sensing image.

The invention discloses a micro-sensing trapezoidal method, device and equipment based on Aruco recognition and a medium, and relates to the field of computer vision. The method comprises the following steps: S1, projecting an Aruco grid and collecting an image, and extracting marked angular points to construct a position set; s2, calculating a trapezoid tension factor based on the trapezoid area and the opposite side distance, and quantifying local deformation; s3, fusing multi-labeled spatial parallax and tension information, constructing a continuous tension equilibrium domain, and generating a final mapping position through gradient correction basic inverse projection mapping and boundary disturbance compensation; and S4, outputting an inverse perspective matrix based on a fusion result, and estimating a microscopic pose offset parameter for fine adjustment of the projector. And through tension factor and spatial parallax modeling, adaptive correction of a distortion region is realized, the edge consistency and overall symmetry of the image are improved, and the stability and precision of the projected image are enhanced.

The invention discloses a camouflage target segmentation method based on multi-scale cross-spectral feature interaction, and belongs to the technical field of computer vision. The implementation method comprises the following steps: 1, constructing a camouflage target detection network; 2, obtaining visible light and infrared dual-mode features for the network subjected to feature extraction; 3, performing multi-layer feature fusion on the visible light image features and the infrared image features; therefore, bimodal complementary information extraction is realized; 4, constructing an edge sensing network for performing detail processing on the edge of the image so as to obtain an edge prediction result; 5, training the camouflage target detection network to obtain training parameters of the camouflage target detection network; optimizing training parameters of the camouflage target detection network; 6, inputting the image pair into the training optimization camouflage target detection network to obtain a predicted camouflage target detection result; compared with the prior art, the accuracy of boundary fuzzy recognition and appearance similarity recognition of camouflage target detection is improved.

The invention discloses an image edge feature enhancement correction fusion method based on a guide filter, and aims to solve the problems that detail features of a low-illumination visible light image and an infrared image are not obvious, focusing edge information is not clear, and registration of a multi-focus image is wrong. The invention provides an edge feature enhancement correction fusion method based on a guide filter. In the illumination enhancement stage, a visible light image is divided into a base layer and a detail layer through a guide filter, and the image contrast and detail information are enhanced. For an infrared image, an infrared background is reconstructed by using a quadtree decomposition and Bezier interpolation method, unclear focusing edge feature information is extracted, and the visibility of image details is enhanced. And finally, reconstructing the two processed images by using a multi-scale weighted gradient method, and performing fusion by calculating large-scale and small-scale weight scales.

The invention belongs to the field of photovoltaic panel hot plate image processing, and particularly relates to a photovoltaic panel defect detection method fusing multi-scale wavelets and a lightweight attention mechanism. According to the method, an RHDWT discrete wavelet transform module based on multi-scale decomposition is added in a model input stage to strengthen image edge and texture detail representation; a Mix Structure Block module is introduced into a backbone network of the YOLOv11, so that multi-scale features are fused, and the feature expression capability is improved; an LWGA lightweight global attention mechanism is introduced into a neural network connection layer to enhance the context modeling capability, and the detection effect on small target defects such as fine cracks and hot spots is improved. According to the model, through collaborative optimization in three aspects of input preprocessing, feature extraction and an attention mechanism, the precision and robustness of defect detection in a complex photovoltaic module infrared or visible light image are remarkably improved, and the model is suitable for scenes such as high-precision photovoltaic panel image detection and intelligent maintenance.

The invention relates to the technical field of digital photoetching equipment, in particular to an LDI machine image segmentation and exposure splicing method, which comprises the following steps: carrying out primary segmentation on an original complex graph, and carrying out segmentation again after refinement processing; in the exposure process, periodically collecting a gray histogram of an exposure pattern, aligning the exposure images of adjacent areas to form a continuous exposure image, and dynamically adjusting exposure parameters by a light beam modulator according to the characteristics of each second segmentation area to realize closed-loop control on the exposure quality; all exposed images are spliced through a search algorithm and a multi-frame fusion technology, smooth transition of image edges is realized, discontinuity of local features is effectively avoided, the overall consistency and integrity of the spliced images are ensured, the imaging quality and precision are further improved, finally, the spliced images are verified, and the image quality is improved. And seamless splicing of each segmented region is ensured, so that high-quality graphic output is generated.

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention discloses a video noise estimation method, a video noise estimation device and a computer storage medium. The video noise estimation method comprises the following steps: calculating an inter-frame difference result between corresponding pixel points in adjacent frame images; performing image edge calculation on each pixel point in each frame image to obtain an edge detection feature value corresponding to each pixel point in each frame image; determining a static edge point in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image; and determining a video noise estimation value according to an inter-frame difference result between the static edge point in each frame image and each corresponding pixel point in the adjacent frame image. By adopting the method, the influence of image texture details and inter-frame motion on the video noise estimation result can be avoided, so that the video noise estimation result can better reflect the real noise level.

The invention provides a scanning electron microscope image edge detection method, which comprises the following steps: carrying out anisotropic diffusion filtering on a scanning electron microscope image to suppress noise and reserve edges to obtain a filtered image; an original scale gradient and a down-sampling scale gradient of the filtered image are calculated, a plurality of pixel regions in the original scale gradient and the down-sampling scale gradient are fused based on a plurality of adaptive weights to obtain a gradient map, the adaptive weights are determined based on the complexity of the texture of the filtered image, and each pixel region corresponds to one adaptive weight; multi-dimensional features are extracted from the gradient map, the multi-dimensional features are input into a machine learning model for threshold prediction, a continuous pixel-level threshold map is generated based on a predicted threshold, and a gradient threshold included in the continuous pixel-level threshold map is a critical value for distinguishing different types of pixels in the gradient map; and comparing the gradient map with the continuous pixel-level threshold map to obtain a target edge pixel, and generating an edge detection result map based on the target edge pixel.

Aiming at the problems of insufficient noise and detail distinguishing capability and insufficient cross-scale feature modeling in the existing infrared image stripe removing method, the invention provides an infrared image stripe removing method based on residual dual-path downsampling. The method comprises the following steps: firstly, aiming at a stripe noise discrimination problem, designing a residual dual-path downsampler to learn image features and realize preliminary separation of noise and details, in a horizontal path, adaptive pooling is adopted to suppress stripe noise, and in a vertical path, image edge details are reserved through step length convolution; secondly, aiming at a feature cross-scale modeling problem, designing mixed attention to construct local and global information interaction, modeling cross-scale feature correlation by utilizing convolution and multi-head stripe attention, and reserving low-frequency characteristics of the image through stripe self-correction attention; and finally, the integrity of image details can be kept while stripe noise of the noise image processed by the method is removed, and the network has relatively good robustness.

The invention provides an unmanned aerial vehicle real-time flight path dynamic adjustment system and method based on artificial intelligence, equipment and a medium. The system comprises a data acquisition module for acquiring target data; the image processing module performs image preprocessing and enhancement processing on the target image data; the image segmentation module can extract image edge features in the enhanced image according to a multi-scale multi-direction morphological gradient algorithm, segment the enhanced image according to the image edge features, and merge segmented images according to the same marks; a scene recognition and classification module performs scene recognition on the processed segmented image according to a convolutional neural network model to obtain a scene type; and the path dynamic adjustment module dynamically adjusts the flight path. The scene type can be accurately judged by using an image segmentation technology, and the flight control parameters corresponding to the scene are matched, so that the accuracy of path dynamic adjustment is improved.

The invention provides a remote sensing image cloud removal method and system fusing gradient fidelity and time-spectrum consistency, and relates to the technical field of remote sensing image processing and tensor modeling, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image and cloud mask data, and generating a fault mask through active fault recognition; then calculating a guide gradient tensor and carrying out low-rank approximate processing to obtain a spatial characteristic factor and a time-spectrum characteristic factor; constructing a multi-objective optimization model of a gradient domain fidelity term, a pixel domain fidelity term and a time-spectrum consistency constraint term based on the multi-objective optimization model; and a near-end alternating minimization algorithm of an embedded alternating direction multiplier method is adopted to efficiently solve, a reconstruction result is corrected in combination with a cloud mask and an active fault mask, and a high-quality cloud-removed image sequence is output. According to the method, the edge structure, texture details and space-time consistency of the image can be effectively kept in a complex cloud coverage scene, and the usability and analysis value of remote sensing data are remarkably improved.

The invention discloses a method for reconstructing a three-dimensional edge of a multi-view image. The method comprises the following steps: step 1, carrying out two-dimensional image edge detection on the multi-view image to obtain an edge image and calculating to obtain a structural point cloud; 2, initializing a spherical 3D Gaussian primitive based on the structural point cloud; 3, supervising and optimizing the number and attributes of the spherical 3D Gaussian primitives by using the edge graph; step 4, carrying out linear fitting on the optimized spherical 3D Gaussian primitive based on interpolation rough; and step 5, further optimizing a straight line fitting result to a third-order rational Bezier curve to obtain a final multi-view picture three-dimensional edge reconstruction result. According to the method, a spherical 3D Gaussian primitive is introduced as a three-dimensional edge representation medium, and the attribute of the spherical 3D Gaussian primitive is optimized by using edge detection information of a two-dimensional image, so that three-dimensional edge information is extracted from the two-dimensional image; the 3D Gaussian splashing technology is used for monitoring spherical 3D Gaussian attributes, and the consistency of spherical 3D Gaussian primitives in two-dimensional rendering and three-dimensional representation is guaranteed.

The invention relates to the technical field of contour extraction, in particular to a knitted label edge contour extraction and optimization method, which comprises the following steps of: extracting an image edge gray scale trend and acquiring a fracture coordinate, extracting an edge track to generate a connection path, reconstructing an interference region connection path, converting rough edge region path distribution and screening out an abnormal path. According to the method, the gray abrupt change and edge interruption features in the image are identified, the fracture area is accurately positioned, structural integrity identification is enhanced, space correlation and direction fitting are carried out on edge endpoints in the interference area, the boundary recovery capability and the closure degree are improved, abnormal path segments are screened out through polar distribution, and therefore the detection accuracy is improved. Control nodes are set based on the position relation between the boundary segments and the overall chain, a boundary joint trend coordination structure is fused, the contour consistency and robustness under the complex background are improved, and the coherence recognition capability and extraction precision are enhanced.

The invention relates to the technical field of medical image processing, and discloses an ultrasonic image edge segmentation method and system, a terminal and a medium, and the ultrasonic image edge segmentation method comprises the steps: constructing an initial model, obtaining a training set, inputting the training set into the initial model, and obtaining a prediction probability graph; performing data preprocessing on the training set to obtain boundary supervision information; boundary sensing information is obtained according to the prediction probability graph and the boundary supervision information; obtaining a target model according to the boundary perception information, the training set and the initial model; and acquiring an echocardiogram of a user, and inputting the echocardiogram into the target model to obtain a segmentation probability graph. According to the method, the boundary monitoring information is provided by preprocessing the training set to solve the problem of boundary blur, the perceptual ability of the model to the boundary is enhanced through the boundary monitoring information, and the boundary segmentation precision of the ultrasonic image can be improved.