586 results about "Edge extraction" patented technology

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention provides a remote sensing coastline automatic extraction method and system based on residual space pyramid segmentation and two-dimensional attention, and relates to the technical field of space analysis. The method comprises the steps of high-resolution remote sensing image acquisition and preprocessing, sea-land segmentation network reasoning, probability graph thresholding and edge extraction and vectorization processing. According to the method, the segmentation precision is improved through multi-scale feature aggregation and attention enhancement, coastline vector data with geographic coordinates are generated in combination with edge detection and topological repair, and the method is suitable for spatial analysis and coastline monitoring.

The invention relates to the technical field of image analysis, in particular to an ETFE film cutting control method and system based on image recognition, and the method comprises the following steps: enhancing an edge through multi-scale Gaussian filtering, extracting a gray level change rate pixel by a Sobel operator, generating a path segment, outputting a coordinate, calculating a gradient direction, constructing a direction continuous sequence, and recording an angle deviation; a dynamic window judges abnormal fluctuation to trigger calibration, a fluctuation segment vector is replaced to update a motion track, and a B spline is fitted to generate a track output cutting instruction. According to the method, multi-scale Gaussian filtering is combined with Sobel operator enhanced edge extraction continuous pixels, the fracture problem caused by a fixed threshold value is solved, vector space projection constructs a direction sequence to quantify angle deviation, dynamic window range judges fluctuation to trigger calibration, and a vector replacement strategy corrects an abnormal section control point direction. A smooth track is generated through B spline interpolation, mechanical vibration is reduced, and the flexible material cutting precision and the equipment cooperation efficiency are improved.

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 a sub-pixel edge extraction method based on an interpolation method, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: obtaining a gray level image, and calculating the gradient of the gray level image in at least one direction based on a preset operator, so as to obtain the gradient amplitude of the gray level image; compressing the gradient magnitude to a preset threshold value, and calculating the gradient direction of each pixel in the gray level image; based on a preset interpolation model, in a preset period, repeatedly fitting the edge intensity in the gradient direction until a complete sub-pixel contour point set is obtained; and deploying the trained interpolation model as a sub-pixel edge extraction model so as to improve the image processing precision in edge detection. By means of the method, the problem that a traditional pixel-level edge detection method is insufficient in precision can be solved, and the detection precision and the measurement repeatability can be effectively improved in the field of high-precision measurement.

The invention discloses an improved filtering and local direction adaptive rust recognition method for a hull surface rust image, which comprises the following steps of: 1, performing gray processing on the hull surface rust image to obtain a gray image; 2, performing global denoising on the image after gray scale by adopting a wavelet transform and total variation fusion denoising algorithm to obtain a globally denoised image; 3, performing local denoising on the globally denoised image by adopting a self-adaptive Gaussian kernel denoising algorithm to obtain a final denoised image; 4, carrying out the edge extraction of the final denoised image through a direction self-adaptive edge detection algorithm, and obtaining a rust image; according to the method, the improved wavelet transform and total variation fusion denoising algorithm is combined, efficient noise removal is achieved, the size and the standard deviation of the filtering kernel are adjusted in a self-adaptive mode, noise of a local area is effectively processed, detail loss is avoided, and the image quality is improved.

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 relates to a method for size measurement and appearance detection of an O-shaped ring. The size measurement of the O-shaped ring is carried out through the modes of image preprocessing, region-of-interest segmentation, edge extraction, central point determination and the like. And then, appearance detection is carried out, edge type defect detection and internal defect detection are respectively carried out, and then result fusion and screening are carried out. Therefore, high-precision measurement can be achieved, the device is suitable for O-shaped rings of different types, existing defects can be found in time, and detection of edge defects such as burrs and deckle edges is met. Moreover, a deep learning algorithm is adopted for internal defect detection, and internal defects such as glue shortage, impurities, damage and fracture can be accurately recognized through the steps of data preparation, model training, model optimization and the like.

The invention discloses an improved filtering and adaptive edge detection steel plate gap identification method for a hull surface image, and the method comprises the steps: 1, carrying out the denoising of a hull surface image containing a steel plate gap through employing a local weighted Gaussian blur and dynamic bilateral filtering fusion algorithm, and obtaining a denoised hull surface image; 2, carrying out the edge detection of a local region of the denoised hull surface image through employing a self-adaptive dynamic threshold Canny edge detection method, and obtaining a binary image; and step 3, carrying out edge extraction on the binary image to obtain a steel plate gap image. According to the method, through a local weighted Gaussian blur and dynamic bilateral filtering fusion denoising algorithm, the noise in the image is efficiently removed in a proportional fusion mode, meanwhile, through self-adaptive adjustment of filtering parameters, the noise influence of a local area is improved in a targeted mode, loss of detail information is avoided, and the image quality and detail presentation are remarkably improved.

The invention provides a point cloud edge segmentation method and system suitable for a complex industrial part, and the method comprises the steps: extracting a point cloud neighborhood point estimation normal vector of each point in point cloud data after the sampling processing of a voxel grid, carrying out the analysis processing based on the point cloud neighborhood point estimation normal vector, and obtaining an enhanced curvature; utilizing a Canny edge detection algorithm to extract an edge region in a two-dimensional view after two-dimensional processing of the point cloud data, and utilizing a communication component to screen to obtain an effective edge region; and screening the enhanced curvature and the effective edge region based on a preset threshold to obtain a segmentation result. According to the method, the extraction precision and accuracy of the point cloud edge features are effectively improved, omission and redundancy of the edge features are avoided, the reliability of an edge extraction result is improved, parts of complex geometric structures can be processed, and the method is suitable for the fields of industrial detection, reverse engineering and the like.

The invention discloses a color printed matter defect automatic detection system and method based on machine vision, and relates to the technical field of computer vision, and the method comprises the steps: converting a color image into a gray level image, generating a boundary enhanced gray level image through a Canny edge detection algorithm, calculating the offset of edge pixels through sub-pixel recovery, and obtaining a color printed matter defect detection result. Extracting an edge pixel proportion based on the sub-region division table, performing amplification tracking on edge dense sub-regions, and analyzing RGB color deviation; through a multi-scale Gaussian pyramid decomposition and edge refinement technology, analyzing multi-scale features of the boundary enhanced grayscale image and optimizing edge precision; and classifying edge defects and color defects of each sub-region by utilizing a defect threshold value, calculating a comprehensive risk value, increasing a risk level according to a boundary condition, and generating a detection report. According to the method, the Canny edge detection algorithm is combined with sub-pixel recovery, so that the edge extraction precision and the positioning capability of color printed matter defect detection are improved.

The invention provides a carbon powder particle distribution real-time analysis method based on image processing, and the method comprises the steps: applying an adjusted gray threshold parameter to the processing and analysis of a carbon powder particle image, and carrying out the edge extraction, geometric feature extraction and particle size distribution analysis of carbon powder particles again, the adjusted analysis result is used as a basis for regulating and controlling a carbon fiber recovery process; establishing a quantitative relationship between the particle size distribution of the carbon powder particles and the centrifugal rotation rate of the solution by adopting a mathematical statistical method according to a particle size distribution analysis result, and analyzing the influence of the centrifugal rotation rate on particle aggregation and particle size distribution; the carbon powder particle distribution in the carbon fiber centrifugation process is analyzed and monitored in real time, the centrifugation process parameters are dynamically regulated and controlled according to the real-time analysis result, and the online monitored data are used for optimizing the carbon fiber recycling process.

The invention discloses an online AOI detection system based on an industrial intelligent sensor, and the system comprises an image collection and preprocessing module which is used for collecting and preprocessing image data of an industrial product; the mask auto-encoder modeling module is used for constructing a mask auto-encoder model; the structure parameter optimization module is used for optimizing the mask auto-encoder model; the image reconstruction and difference extraction module is used for generating a reconstructed image, extracting an image difference region and determining a candidate defect region; the defect identification module is used for carrying out edge extraction and aggregation analysis, identifying a final defect area and acquiring spatial position information; the defect classification and labeling module is used for extracting defect area features and generating corresponding classification labels and grade labels; and the control response module is used for generating a control instruction and issuing the control instruction to the production line control device. According to the method, the high-precision automatic identification and real-time classification processing of the surface defects of the industrial product are realized by fusing the mask auto-encoder and the Tiancattle herd optimization algorithm.

The invention discloses a geological map-oriented breakpoint repairing and closed curve reconstruction method and system and a medium. The method comprises the following steps of: preprocessing an original geological map to obtain a grayscale image; carrying out binarization and morphological closed operation processing to obtain a grayscale image of the edge of the smooth contour curve; performing pixel skeletonization to obtain a pixel-level skeleton diagram; performing secondary edge extraction, connected domain marking and endpoint statistics on the pixel-level skeleton diagram, and screening out a non-closed curve and an endpoint set; carrying out nearest neighbor retrieval by adopting KD-Tree to obtain a candidate pairing set of each end point; performing priority pairing in combination with the spatial distance and the direction smoothness; and generating a transition line segment according to a pairing result to supplement the fracture part of the non-closed curve. According to the method, non-closed breakpoints generated by scanning or drawing errors in the geological map can be automatically restored, and a closed curve with topological integrity and a smooth boundary is generated.

The invention relates to the technical field of measurement and automation, in particular to a non-contact wire diameter identification method and system, and the method comprises the steps: enabling a CCD camera and a laser range finder to aim at a remote cable identification target, and obtaining a cable image and the spatial position information of the cable image; inputting the cable image into a trained cable type identification model for feature extraction and classification processing to obtain a cable type, and determining an edge detection preset parameter corresponding to the cable type; edge extraction and contour segmentation are carried out based on the cable image and edge detection preset parameters, and the pixel length of the cable diameter is obtained according to geometric characteristic estimation of the segmented contour; based on the spatial position information of the cable and the pixel length of the diameter of the cable, performing scale conversion from an image to a physical world to obtain the actual length of the diameter of the cable; and based on the actual length of the cable diameter and the cable type, searching a matching item in a preset cable model database to obtain a cable model identification result.

The invention discloses a fan blade clearance intelligent monitoring method based on multi-modal fusion. A laser radar and a visual sensor are adopted to collect point cloud data and image data of the fan blade; environment parameters are collected, validity judgment is conducted on the point cloud data and the image data of the fan blade according to the environment parameters, and radar monitoring data and visual monitoring data are obtained; modeling the fan blade according to the radar monitoring data to obtain a three-dimensional model of the fan blade, and further obtaining a radar measurement value of the blade clearance according to the three-dimensional model of the fan blade; performing image segmentation, edge extraction and depth estimation on the visual monitoring data in sequence to obtain a visual measurement value of the blade clearance; and the radar measurement value and the vision measurement value of the blade clearance are combined, and a multi-modal fusion algorithm is adopted for processing to obtain the fusion clearance distance of the fan blade. The accuracy and real-time performance of fan blade clearance monitoring are remarkably improved, blade collision accidents can be effectively prevented, and reliable guarantee is provided for safe operation of a wind turbine generator.

A video generation framework that is controllable, unsupervised and based on events (CUBE) includes an event camera, which captures changes in light intensity at each pixel of a scene asynchronously and generates event camera data. A text-to-image diffusion model that is conditioned on textual descriptions integrates the event camera data to control video synthesis. Further, an edge extraction module translates event data into a format usable by the text-to-image diffusion model, whereby the diffusion model synthesizes detailed and contextually accurate videos based on textual prompts. Further, an improved system (CUBE Plus) includes a content frame identification module which selectively identifies and uses only the most information-rich event segments of the event camera data to drive cross-frame attention, and an event driven attention mechanism that allows the framework to focus on event-dense moments.

The invention relates to the technical field of image quality evaluation, in particular to an SAR image quality evaluation method for edge structure definition, which comprises the following steps: setting a brightness threshold, separating highlight spots in an SAR image, and calculating the highlight spot aggregation density of the SAR image; performing edge extraction on the SAR image by using two edge extraction methods, and calculating the primary edge extraction similarity of two edge extraction results; if the primary edge extraction similarity exceeds a set threshold value, carrying out secondary fuzzy processing on the image and carrying out edge extraction again, and calculating secondary edge extraction similarity; and calculating a comprehensive quality evaluation value of the SAR image according to a relation selection expression of the primary edge extraction similarity, a set threshold value and the secondary edge extraction similarity. According to the method, through combination of highlight spot aggregation density analysis and multi-stage edge similarity dynamic evaluation, the adaptability of quality evaluation to a complex scattering scene and the objective characterization capability of edge definition are remarkably improved.

The invention relates to the technical field of machine vision and product defect visual prediction, in particular to a product defect visual prediction method and device based on X rays. According to the method, edge extraction and entropy-based analysis are utilized to enhance defect positioning, meanwhile, the challenges of image noise and non-uniform intensity change are solved, and in order to improve robustness, a self-adaptive threshold strategy combined with DBSCAN clustering is adopted to distinguish defects and noise. Therefore, the problems that in the prior art, the ratio of product defects is low, the visual prediction efficiency of edge blurring is low, and the accuracy is not high are solved. Compared with the prior art, noise reduction, edge detection and crack identification are enhanced. By integrating X-ray image processing, the method improves accuracy, reduces false positive and improves detection efficiency.

The invention provides a low-dose CTA image noise reduction model training method, a noise reduction method and equipment. The method comprises the following steps: acquiring cardiovascular and edge noise enhancement data of a low-dose CTA image based on a cardiovascular segmentation model and an edge extraction model; according to the data and the conventional dose CTA image, a cyclic generative adversarial network is trained based on target loss so as to train the cyclic generative adversarial network into a low dose CTA image noise reduction model, and the target loss comprises cardiovascular maintenance loss obtained based on a cardiovascular segmentation model and edge maintenance loss obtained based on an edge extraction model. According to the invention, the blood vessel and edge enhancement and the global noise reduction of the low-dose CTA image can be combined in the training process of the low-dose CTA image noise reduction model, so that the model can give consideration to the reservation of the fine structure of the image and the improvement of the overall image quality. Therefore, the application effectiveness and reliability of a low-dose CTA image noise reduction result obtained by adopting the model can be effectively improved, and the reliability of CTA clinical diagnosis is improved.

An image processing method, system, device and medium, comprising: acquiring an image to be processed, and inputting the image into a preset super-resolution processing model for super-resolution processing to obtain a super-resolution processing result; the super-resolution processing at least comprises the following steps: acquiring a to-be-processed image and inputting the to-be-processed image into an image encoder to obtain a latent code corresponding to the to-be-processed image; obtaining a description text corresponding to the to-be-processed image and inputting the description text into a text encoder to obtain text features; inputting the to-be-processed image into the edge extractor to obtain edge features; inputting the to-be-processed image into the semantic extractor to obtain semantic features; inputting the latent code into a UNet de-noising device, and carrying out iterative diffusion de-noising to obtain a de-noised latent code; and fusing the denoised latent code and the edge feature, and inputting the fused image into an image decoder to output a super-resolution processing result.

The invention discloses a defect edge extraction method and device based on a terahertz image, terminal equipment and a storage medium, and belongs to the technical field of defect detection, and the method comprises the steps: obtaining an original terahertz image; performing enhancement processing on the original terahertz image to obtain an enhanced terahertz image; segmenting the enhanced terahertz image into a plurality of target areas through a mean shift algorithm; for each target area, through a maximum between-class variance method, calculating segmentation threshold values of a foreground and a background in the target area; according to the segmentation threshold, segmenting a foreground region from the target region as a defect region; and inputting the defect area into an active contour model, so that the active contour model generates a defect edge curve according to the defect area. The problems that in the prior art, a defect area needs to be specified manually in advance, and the defect edge extraction error is large can be solved.

The invention relates to the technical field of image data processing, in particular to an automobile part quality visual inspection method and system, and the method comprises the steps: obtaining a plurality of original images of a target object; preprocessing each original image to obtain a plurality of target images; performing edge extraction on each target image to obtain all edges of each target image; performing crack growth characteristic screening on each edge to confirm a crack corresponding to each target image, wherein the crack growth characteristics comprise curvature change and gradient change; respectively evaluating each crack to obtain a feature score of each crack; color marking is conducted on the image of the position where each crack is located according to the feature score of the crack, a marked image containing a color mark is obtained, accurate recognition is conducted by comprehensively considering the curvature and gradient change characteristics of crack growth, the recognition efficiency is improved through feature scoring and marking, quality evaluation deviation caused by edge misjudgment is reduced, and the quality evaluation accuracy is improved. And the product quality and safety are ensured.

The invention relates to a pavement disease detection method, system and device based on a hybrid architecture, and a storage medium. The method comprises the following steps: obtaining pavement image data; the method comprises the steps that data are input into a re-parameterized feature extraction network, the network is based on an HGNetV2 architecture, a RCHGBlock module is formed by embedding a RepConv structure into HGBlock, and a plurality of modules are cascaded and stacked to construct a four-stage progressive feature pyramid structure; based on a high-level semantic layer of a feature pyramid, integrating a space edge perception enhanced attention mechanism, enhancing disease edge features through a dual-path complementary processing framework of an edge extraction path and a standard convolution path, and performing multi-scale feature fusion by using an improved C3K2-SEAM module as a feature fusion unit; and performing end-to-end disease detection based on the fusion features, and outputting disease types, positions and confidence information. Compared with the prior art, the method has the advantage that the disease detection precision and robustness in a complex scene are remarkably improved.

The invention discloses an attitude detection method and system for a photovoltaic panel. The method comprises the following steps: S1, scanning through a laser radar to obtain light point cloud data; s2, segmenting the point cloud data through a clustering algorithm, and determining and storing clustering point cloud data to which the photovoltaic panel belongs; s3, performing plane fitting on the clustering point cloud data to which the photovoltaic panel belongs to obtain a plane equation, and projecting the point cloud data to the plane equation; s4, performing edge extraction on the point cloud of the photovoltaic panel to obtain a top edge linear equation; s5, postures are combined according to the vectors. According to the invention, the laser radar is used for data acquisition, the stability of equipment is improved, the operation is simple, other sensors are not depended on, a training set is not needed to train a model, the use stability and reliability are high, the system can be used in extreme environments such as night, heavy fog and rainy days, and the real-time analysis effect is achieved through algorithm optimization of point cloud processing. And the calculation efficiency of the attitude of the photovoltaic panel is improved.

The invention discloses a machine vision-based automatic identification and calculation method for pore change of a rock section under an SEM (scanning electron microscope), and the method comprises the steps: obtaining original image data of the rock section through a scanning electron microscope, transmitting the original image data to a machine vision SEM pore intelligent quantification cloud platform, and dividing the original image data into a plurality of image sub-regions which are not overlapped with each other; calling a special threshold inversion model to analyze the gray information of each sub-region, and determining a dual-phase pore segmentation threshold; carrying out edge extraction on the sub-regions after threshold processing by adopting a pore edge gradient enhancement algorithm; performing topological structure analysis on the intermediate image data by using a biphase pore topological inversion model, constructing and optimizing a pore space topological network, and eliminating false pore structure data; and according to the optimized pore space topology network, integrating calculation results of all the sub-regions to obtain a pore change identification calculation result of the whole rock section. The method improves the pore identification precision and processing efficiency, and adapts to different types of rock samples.

The invention discloses a road traffic detection control method based on a vehicle-mounted camera module, and belongs to the technical field of traffic detection, and the method specifically comprises the steps: collecting road image data in real time through the vehicle-mounted camera module; preprocessing the acquired road image data, and extracting a road area; performing edge extraction on the preprocessed road image data by using an edge detection algorithm to obtain road edge information; according to the road edge information, detecting straight-line segments in the road through Hough transform, and calculating an included angle between adjacent straight-line segments to determine the line type of the road; calculating the distance between the vehicle and the road boundary and the offset of the vehicle relative to the road center line according to the line type of the road in combination with the speed and position information of the vehicle; according to the distance between the vehicle and the road boundary and the offset of the vehicle relative to the road center line, the driving direction and speed of the vehicle are adjusted through a preset control strategy; according to the invention, real-time safety detection of the vehicle in the driving state is realized.

The invention discloses a metal strip surface defect automatic identification and cutting method based on edge detection, and belongs to the field of computer vision, and the method comprises the following steps: (1) setting a parameter list; (2) constructing an edge detection module; (3) constructing a clustering module; (4) constructing a boundary condition judgment module; and (5) constructing a defect grade judgment module. According to the method, the parameter list is set, so that the parameters required to be used in the operation process can be appointed in advance; through the edge detection module, the low-quality image can be enhanced, and defect edge extraction and centroid calculation can be automatically carried out; through a clustering module, the fragmented defects of the same cluster obtained through clustering can be regarded as the same defect; iteratively optimizing the cutting area according to a threshold value set in a parameter list through a boundary condition judgment module; and the defect grade judgment module can judge the defect grade information of the cutting area according to a threshold value set in the parameter list.

The invention provides a remote sensing image building extraction method fusing semantic and edge features, and relates to the technical field of remote sensing image processing. On the basis of a semantic segmentation network HRNet, PSABlock is designed to reconstruct a backbone network, the backbone network serves as an image semantic information extraction branch, input features are decomposed into spatial components and channel components, spatial relations and channel dependency are captured respectively, and effective fusion of global and local features is achieved. According to the method, a module fusing multilayer edge features is designed as an edge feature extraction branch, an edge feature map of a building is obtained by generating an edge prediction result and performing weighted fusion, and an edge extraction effect is optimized through explicit edge loss. In addition, the invention provides a'main body-edge-fusion 'joint loss function. Compared with a semantic segmentation model of single feature extraction, the method has the advantages that the precision of the building extracted from the test data set is higher, the edge is more complete, and the requirements of practical application are met.

The invention relates to the technical field of medical image processing, in particular to an ultrasonic image segmentation method based on edge guidance, and the method comprises the steps: inputting an ultrasonic image into an edge extraction branch and an image encoder; outputting an edge mask image corresponding to the image through the edge extraction branch; performing noise suppression and topological correction on the edge mask image to generate a closed edge constrained by an anatomical structure; generating prompt box information based on the closed edge, and inputting the prompt box information to a prompt encoder; fusing the image prompt features output by the prompt encoder, the image features extracted by the image encoder and the edge mask information; the fusion features are input into a decoding module, a final segmentation result is obtained, the synergistic effect of edge information and visual prompt is fully utilized, the perception ability of the model for the anatomical structure in the ultrasonic image is improved, and the accuracy and robustness of segmentation are remarkably improved. Therefore, the problems of fuzzy boundary, inaccurate prompt, weak structure identification capability and the like in related technologies are solved.