149results about How to "Improve segmentation efficiency" patented technology

The invention relates to a CBCT image tooth segmentation method. The method comprises the following steps: generating a two-dimensional oral cavity panoramic film from a CBCT image; carrying out tooth two-dimensional segmentation and tooth ID classification on the two-dimensional oral cavity panoramic film by adopting a deep learning algorithm; calculating a key point coordinate of a two-dimensional single tooth and mapping the key point coordinate to a three-dimensional CBCT image coordinate; respectively cutting the CBCT image to obtain an area of each tooth; segmenting each three-dimensional tooth region by adopting a deep learning algorithm; and assigning ID to all the tooth segmentation results, and splicing to obtain a three-dimensional tooth segmentation result. According to the method, accurate segmentation of the tooth area in the CBCT image can be realized, complex conditions such as tight tooth arrangement, similar shapes and artifact interference can be effectively handled, the problems of tooth segmentation result fusion, ID confusion and metal artifact interference can be effectively solved, and resource consumption is remarkably reduced by determining the tooth key points on the panoramic film, so that the tooth segmentation efficiency is improved. The overall segmentation efficiency is improved, and the segmentation time is shortened, so that the accuracy of the segmentation result and the processing speed are improved.

The invention discloses a CT image segmentation method for spine surgery, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining a spine CT image of a patient, and carrying out the preprocessing of the spine CT image; adopting a centrum gap detection method to detect a centrum gap region of a spine in the preprocessed CT image, and performing automatic segmentation on each centrum based on centrum gap region information to obtain a preliminary segmentation result of the centrum; extracting local texture features of the intervertebral disc and the nerve root in the intervertebral space area, and classifying the local texture features through a classifier to generate a preliminary segmentation result of the intervertebral disc and the nerve root; performing rigid registration and non-rigid registration on the initial segmentation results of the vertebral body, the intervertebral disc and the nerve root and the standard three-dimensional model, and identifying an abnormal region in segmentation; by extracting local texture features of the intervertebral disc and the nerve root and combining a support vector machine classifier, accurate segmentation of various structures can be realized.

The invention discloses a rock pore three-dimensional reconstruction method and system based on an adaptive hierarchical cube algorithm, and a storage medium, relates to the technical field of rock structure measurement, and aims to solve the problems of low extraction execution efficiency, ambiguity and poor reconstruction precision of an existing moving cube algorithm. According to an image segmentation model based on RefineNet, the model adopts three 3 * 3 convolution cascading to replace 7 * 7 convolution, and a coordinate attention mechanism is introduced behind each block of an encoder to improve the segmentation precision and efficiency of a key area; dividing the cube into tetrahedrons by adopting a saddle point-adaptive layered downward subdivision method, solving intersection point coordinates and normal vectors, and constructing a triangular patch index; determining the contour surface shape penetrating through the cube by adopting a cube lookup table, and merging triangles of all tetrahedrons to form a complete contour surface; and creating a new triangular patch by using the vertex of the edge of the hole and the newly generated vertex, and repairing the hole to obtain the accurately reconstructed three-dimensional hole of the rock.

The invention discloses an industrial weld defect detection method based on semantic segmentation. The method comprises the following steps: S1, obtaining an original weld image for training and related parameter information, carrying out binarization processing on the original image, storing a foreground region of the processed image, selecting a region with the maximum area to carry out rectangular shape conversion, and cutting and storing a rectangular region; s2, marking the cut rectangular region image, generating a weld defect image data set, cutting the image in the weld defect image data set by using an intelligent cutting method, and distributing redundant pixels generated by cutting; s3, constructing a semantic segmentation network model according to the data set generated in S2, focusing a defect region by using an attention mechanism, performing selective convolution calculation on the defect region, and training the semantic segmentation network model by using a loss function as an index; and S4, processing the weld seam image to be detected by using the semantic segmentation network model, and splicing and restoring the weld seam image to an original image size. According to the invention, the detection precision is improved, and image processing is not limited.

The invention relates to the technical field of image processing, in particular to an intelligent processing method for an ultrasonic image, which comprises the following steps: acquiring a grey-scale map of a liver ultrasonic image; obtaining all filtering windows under a plurality of window sizes on the grey-scale map; obtaining an optimal initial global filtering window according to the global first dispersion degree; obtaining a window adjustment parameter according to the foreground region probability, the second discrete degree and the foreground connected domain average area; obtaining the size of an adaptive filtering window of each pixel point according to the window adjustment parameters; filtering and denoising the grey-scale map according to the sizes of the adaptive filtering windows of all the pixel points to obtain a denoised grey-scale map; according to the method, lesion area edge segmentation is clearer, and the efficiency of ultrasonic image segmentation is improved.

The invention discloses an intelligent segmentation method and system for space steel structure nodes, and belongs to the technical field of building construction. The method comprises the following steps: collecting point cloud data of a steel structure, and preprocessing the point cloud data; clustering the point clouds to form a plurality of super voxels, and calculating concavity and convexity between adjacent super voxels; abstracting boundaries between adjacent super voxels into point clouds with convex-concave information, endowing the concave points and the convex points with weight values, and calculating an optimal segmentation plane; according to the method, the super voxels are segmented according to the optimal segmentation plane, and the segmentation result is optimized, so that the nodes are accurately segmented from the whole steel structure model, and the nodes are conveniently monitored by using a finite element model. The method can achieve the intelligent and rapid recognition and segmentation of the complex space steel structure nodes, remarkably improves the recognition and segmentation efficiency of the nodes, can guarantee the high consistency of segmentation results, improves the precision of the segmentation results, and can guarantee the comprehensiveness and accuracy of damage detection.

The invention belongs to the technical field of transformer substations, and particularly relates to a transformer substation three-dimensional model construction method based on deep learning, which comprises the following specific steps: acquiring point cloud data of a transformer substation and constructing a transformer substation equipment model library; preprocessing the point cloud data of the transformer substation; segmenting the preprocessed point cloud data of the transformer substation to obtain a single point cloud set; and identifying the single point cloud set, and then searching a point cloud model of corresponding equipment in a substation equipment model library. According to the method, the PCSCK method is designed, the requirement for computing resources can be greatly reduced, meanwhile, the segmentation efficiency is remarkably improved, and rapid and accurate segmentation of the substation equipment is achieved. According to the point cloud model registration method provided by the invention, deep learning networks SERN and KPSM are utilized, the efficiency and robustness of transformer substation equipment registration are improved, and the problem that a registration algorithm falls into local optimum is avoided.

The invention relates to a kidney component segmentation method and system based on deep learning, belongs to the technical field of medical image segmentation, and solves the problem of low accuracy of kidney component segmentation in the prior art. The method comprises the following steps: acquiring a kidney pathological image and corresponding component labeling information, cutting the kidney pathological image, screening a target image from the cut image, and constructing a sample set based on the target image and the corresponding component labeling information; constructing a multi-channel coding fusion image segmentation model, and training the multi-channel coding fusion image segmentation model based on the sample set to obtain a kidney component segmentation model; cutting a to-be-segmented kidney pathological image, screening a target image from the cut images, and inputting the screened target image into the kidney component segmentation model to obtain a segmentation result corresponding to the target image; and obtaining a component segmentation map of the to-be-segmented kidney pathological image based on the segmentation result. And high-precision kidney component segmentation is realized.

The invention relates to the technical field of rice quality detection, in particular to a head rice rate online detection method and system based on machine vision, and the method comprises the steps: obtaining an initial binary image of a rice image; intelligently screening a rice grain adhesion region in the initial binarized image by using a BP neural network to obtain a to-be-segmented binarized image; segmenting each rice grain adhesion region in the to-be-segmented binary image by using a multi-distance transformation watershed segmentation method to obtain a first segmentation result image; segmenting a rice grain adhesion area in the first segmentation result image by using an angle limitation concave point matching segmentation method to obtain a second segmentation result image as a final segmentation result image; and obtaining the length of each rice grain in the rice image on the basis of the obtained segmentation result image by using a minimum bounding rectangle method so as to calculate the head rice rate of the rice image. According to the method, the segmentation accuracy is improved, the segmentation method is high in applicability, and subsequent measurement of characteristic parameters of single-grain rice is facilitated.

The invention relates to the field of data processing, in particular to a spacer vibration endurance capacity detection method, which comprises the following steps of: acquiring a historical amplitude value of a wire and a stress value of a connecting part of the wire corresponding to the spacer based on a time sequence, and acquiring the change credibility between the amplitude value and the stress value at each moment in the history; performing curve fitting on the data points of the stress values at all the moments according to a least square method so as to calculate the deviation degree of the stress values at all the moments; an isolated forest algorithm is adopted, the change credibility and the deviation degree are combined, the priority of break points is obtained, the optimal break point is determined, an isolated tree is efficiently constructed, abnormal stress data are accurately recognized, the vibration endurance capacity of the spacer is calculated according to the abnormal stress data, and the endurance capacity is compared with a preset threshold value to evaluate the state of the spacer. By improving the segmentation efficiency and accuracy of the model, safe and stable operation of the power system is ensured.

The invention relates to a multi-center applicable automatic segmentation method for a brain structure of a human brain magnetic resonance image, and the segmentation method specifically comprises the steps: obtaining MRI data of different platforms, and carrying out the N4 bias field correction and image standardization processing of the MRI data; u-Net is used as a basic framework, a self-attention mechanism module is fused, and an MRI brain image structure segmentation network model suitable for multiple platforms is constructed; and inputting the processed MRI data into a segmentation network model, training the segmentation network model by using transfer learning and domain adaptation technologies, performing uncertainty estimation on an output result, and further performing model training by using screened high-quality unlabeled data so as to realize automatic segmentation of the brain structure of the human brain magnetic resonance image. Compared with the prior art, the method has the advantages that brain structures of different magnetic resonance imaging platforms can be accurately and quickly segmented, a more universal, efficient and reliable tool is provided for the field of brain image analysis, and wider application and research exploration are facilitated.

The embodiment of the invention discloses an ear canal CT image segmentation method based on improved nn-UNet, and the method comprises the steps: collecting and marking the ear canal CT data of a plurality of patients, and making a data set; preprocessing the data; training on a training set by using a model improved based on nn-UNet, dividing a data set for multiple times by using a five-fold cross validation method, then training and verifying each fold, and storing a training model after training is completed; reasoning and testing the trained model on the test set data which are not seen; post-processing is carried out, and an output result of the model is optimized; and formulating an evaluation scheme to evaluate the prediction result. According to the method, the segmentation efficiency and accuracy can be greatly improved, and subjective deviation and human resource waste possibly generated in the manual segmentation process can be effectively avoided.

The invention relates to a dam disaster point identification method based on improved DeepLabV < 3 + >, and the method comprises the following steps: Step 1: data preprocessing: changing a data set composed of dam images into a size which can be trained by a network according to the demands; step2, establishing a dam disaster point identification model based on the improved DeepLabV < 3 + >, applying a lightweight network MobileNetV2 in the model, optimizing the expansion rate of a cavity space pyramid pooling module, and introducing a channel attention mechanism and a space attention mechanism, and Step3, training by using the dam disaster point identification model based on the improved DeepLabV < 3 + >, and obtaining a dam disaster point image segmentation result. According to the algorithm, the recognition precision of dam disaster points is improved by introducing an adaptive attention mechanism, a feature extraction module in the algorithm adopts specific cavity convolution configuration, the capture capability of small-scale disaster features is enhanced, and a decoder structure is improved, so that more efficient feature fusion is realized, and the accuracy of semantic segmentation is improved.

The invention provides a remote sensing image photovoltaic identification method based on an SAM model, and relates to the technical field of computer vision, and the method comprises the steps: collecting and preprocessing a multi-resolution photovoltaic segmentation data set, and enabling the data set to be used for adjusting an image encoder; constructing an SAM segmentation network comprising the image encoder, the prompt encoder and the mask decoder, and inputting interactive click information and the data set into the SAM segmentation network; adding a segmentation output layer into the mask decoder to generate a preliminary segmentation result; and feeding the preliminary segmentation result back to an interaction module for interactive adjustment, and decoding again after adjustment to generate a final segmentation result.

The invention discloses an SAR (synthetic aperture radar) water depth inversion method driven by variable window sliding segmentation, belongs to the technical field of synthetic aperture radars, and is used for water depth inversion including image segmentation and water depth inversion. The image segmentation comprises original image regularization adjustment, information storage, coastline identification and noise processing, pixel extreme value judgment, pixel change function determination and window sliding; after a coastline is extracted and a starting point is determined, pixels are expanded to two sides of the starting point on the coastline and pixels invaded by the coastline are eliminated, so that utilization of a near-shore image is improved; and the more accurate surge wavelength is obtained by dynamically adjusting the size of the pixel. Compared with the prior art, the method has the advantages that wavelength information of different areas is accurately extracted, complex manual judgment is avoided, and segmentation efficiency and inversion precision are improved; for the irregular coastline, the utilization rate of the water depth inversion of the image in the area near the coastline is increased by changing the starting point along the coastline and expanding outwards.

The embodiment of the invention discloses a medical image segmentation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target abdominal medical image; inputting the target abdominal medical image into a pre-trained student diffusion model to obtain a liver region image of the target abdominal medical image; wherein the pre-trained student diffusion model is obtained based on the trained teacher diffusion model, and the model parameter quantity of the student diffusion model is less than the model parameter quantity of the teacher diffusion model. According to the technical scheme of the embodiment of the invention, the medical image is segmented through the student diffusion model, so that the calculation overhead is reduced, the segmentation speed is increased, and the segmentation efficiency is improved.

The invention discloses a flexible ankle joint exoskeleton control method based on a multi-modal large model, and the method comprises the steps: enabling a flexible ankle joint robot to collect the data of a surrounding environment through a visual camera part, carrying out the environment recognition, and enabling an image recognition module to carry out the property classification of a text language through a multi-modal technology; the lower limb master controller carries out operation processing analysis on original data collected by the IMU and the force sensor; the operation main control board is used for processing visual part information and human body actual movement gait information in real time; performing weight distribution on text features representing the environment by using CHATGPT, scheduling together with current gait information, and generating gait parameters at the next moment and scheduling a gait control function; and the main control board completes auxiliary control under real-time interaction of the ankle joint and the environment by executing a control algorithm. According to the scheme, a new-generation exoskeleton robot which is low in cost, light in weight and capable of achieving body intelligence is successfully built.

The invention relates to the technical field of data processing, in particular to a power equipment asset management method and system. The method comprises the following steps: determining the selection probability of each dimension under child nodes according to the distribution of power data in the child nodes of an isolated tree of power equipment, determining the segmentation necessity of the child nodes according to the time sequence regularity and the selection probability of the power data in the child nodes and the number of the power data in the child nodes, and when the segmentation necessity is greater than a segmentation threshold value, determining the segmentation necessity of the child nodes; according to the selection probability, randomly selecting one dimension in an unequal probability manner to divide the sub-nodes, continuously updating the isolated forest according to the newly generated power data, identifying the current state of the power equipment according to the abnormal score of the current power data, and synchronizing the current state of the power equipment to the digital twin model of the power equipment in real time. And power equipment asset monitoring and management are realized. According to the invention, the state identification of the power equipment is accurate and efficient, and the real-time performance of power equipment asset monitoring and management is improved.

The invention discloses an OCT (Optical Coherence Tomography) image segmentation method based on fast Fourier transform and an SKAttention mechanism. According to the method, the image preprocessing module is introduced into the UNet network, namely the initial stage of the backbone structure, so that the segmentation precision and efficiency of the oedema region of the OCT eye fundus image are remarkably improved. The image preprocessing module combines FFT and SKAttention mechanisms, and the module is designed to carry out preliminary transformation and feature weighting processing on input OCT image data so as to strengthen the recognition capability of the network on edema features. According to the technical scheme, the feature processing flow is optimized, the recognition capability of the model on fundus lesion features is enhanced, and particularly, high efficiency and high accuracy are shown when a complex or low-contrast edema region is processed. Besides, the method has good adaptability and expandability while keeping high processing speed, and is suitable for large-scale medical image processing application.

The invention provides a multimodal-based model segmentation method and a segmentation model training method, and the method comprises the steps: obtaining a part model, and generating a graph embedding representation of the part model based on the part model; obtaining prompt information, and generating embedded representation of the prompt information based on the prompt information; and based on the embedded representation of the prompt information and the graph embedded representation of the component model, segmenting the component model by using the segmentation model to obtain a target surface adjacent sub-graph of the component model. According to the technical scheme, model segmentation can be carried out based on the multi-modal data, the segmentation result oriented to part semantics is obtained, the segmentation result is more accurate, and the segmentation efficiency is higher.

The invention discloses a zero-labeling target segmentation method and system based on point cloud data optimization. The method comprises the following steps: optimizing actual production scene target point cloud data to be processed through a point cloud data optimization method based on self-supervised training to obtain optimized actual production scene target point cloud data; optimization is performed from structural integrity, data density and data smoothness; training the standard point cloud segmentation network based on the marked indoor target point cloud data to obtain a trained standard point cloud segmentation model; the marked indoor target point cloud data has the characteristics that the structural information of the target is complete, the data density is high, and the data smoothness is high; and inputting the optimized actual production scene target point cloud data into the trained standard point cloud segmentation model, obtaining segmentation results of different parts of the target, and obtaining related phenotypic parameters. The marking problem of point cloud data collected in an actual production scene is avoided, and the capability of accurately segmenting different parts of a target is ensured.

The invention discloses a temporal bone CT automatic segmentation method based on a convolutional neural network, and relates to the technical field of medical image processing, and the method comprises the steps: S1, data standardization; s2, a coarse segmentation stage; s3, a fine segmentation stage; and S4, a post-processing step. According to the temporal bone CT automatic segmentation method based on the convolutional neural network, through the four main steps of data standardization, coarse segmentation, fine segmentation and post-processing, rapid, accurate and automatic segmentation of the inner ear area in the temporal bone CT image can be achieved, the segmentation efficiency and accuracy are remarkably improved, and compared with the prior art, the method has the advantages that the segmentation efficiency is improved, and the segmentation efficiency is improved. According to the temporal bone CT key anatomical structure segmentation method, rapid and accurate segmentation of the temporal bone CT key anatomical structure is achieved through the two-stage convolutional neural network, the temporal bone CT key anatomical structure segmentation method has the advantages of being high in segmentation speed, precision and generalization ability and the like, important support is provided for clinical diagnosis, and the temporal bone CT key anatomical structure segmentation method has high clinical application value.

The invention relates to the technical field of artificial intelligence, and discloses a target detection method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed vehicle image, inputting the to-be-processed vehicle image into a block embedding layer of an SAM model image encoder, and generating block embedding features; acquiring data set sample information, inputting the data set sample information into an adapter of the SAM image encoder, and generating prompt features; inputting the block embedding features and the prompt features into an encoding layer of an SAM image encoder to generate an encoding vector; and inputting the coding vector into an SAM model image decoder to generate a salient target detection result corresponding to the vehicle image. The adapter model can learn data sample information and transmit the data sample information to the SAM image encoder as a prompt, adaptive training is efficiently performed in a salient target detection task through additional training of a small amount of data, the salient target detection accuracy is improved, and the segmentation efficiency of the vehicle image is further improved.

The invention discloses a software engineering source code segmentation method and system, and relates to the technical field of software engineering. Comprising the following steps: S1, segmenting a source code file by using a deep machine learning method; before formal segmentation operation is carried out, code file samples which are in the current market and have the same programming language as a source code file needing segmentation are collected to serve as a data training set to carry out data learning training, code fragments are manually marked and classified before training, and the code fragments are classified into different code blocks; segmenting the source code through deep mechanical learning and obtaining a plurality of software modules; the source code file is automatically segmented by using the deep machine learning method, the efficiency is higher, more complex modes and dependency relationships can be identified, dependence on manual intervention is reduced, the segmentation efficiency is improved, manual review is performed after testing, errors caused by automatic segmentation are avoided, and the method is suitable for popularization and application. And the reliability and the running speed of the software are improved.

The invention discloses a method for automatically marking crack semantic segmentation and improving a segmentation effect. According to the method, the minimum value is continuously searched for the crack pixel in the image neighborhood after preprocessing, the preliminary semantic segmentation crack picture can be optimized, and the precision and efficiency of crack pixel segmentation are improved. In addition, the invention further provides an automatic labeling method, each pixel of the crack does not need to be manually labeled, and the efficiency of crack pixel segmentation is remarkably improved. And after the crack pixel list and the corresponding crack binary image are obtained, a crack segmentation scheme of axis extraction and watershed algorithm filling is adopted, so that the problems of strong subjectivity and insufficient refinement of crack edge pixel judgment when a deep learning algorithm is directly adopted for segmentation or manual segmentation is also avoided, and the crack pixel segmentation quality is improved.

The utility model relates to the technical field of cake production, and disclose a kind of cake production segmented device, including support seat, the support seat top is provided with conveying table, the conveying table top is provided with segmented mechanism, the support seat right side is rotatably connected with guard door, the support seat inside is provided with cleaning mechanism, the segmented mechanism includes support frame, the support frame is fixedly connected in conveying table top, the support frame top is fixedly connected with hydraulic cylinder, the hydraulic cylinder bottom is fixedly connected with fixed frame, the fixed frame top is fixedly connected with limit rod, the fixed frame is fixedly connected with fixed rod in, when using, when cake needs to be segmented in the process of production, first, cake is placed on conveying table to be transported, and make cake at the middle position of conveying table top, hydraulic cylinder drives fixed frame to move down, drive slitting knife to segment operation to cake.

The invention discloses a gene amplification fluorescence in situ hybridization image intelligent identification system based on deep learning. According to the technical scheme provided by the invention, in the system, an image acquisition module is used for performing image acquisition on a sample marked by a FISH technology by using a microscope to obtain an initial image; the SAM segmentation module is used for carrying out image segmentation on the initial image based on an SAM model to determine a segmented image; the ResNet comparison module is used for comparing each quasi-cell image by using the standard cell image, and topping a real cell image; the fluorescent dot counting module is used for carrying out fluorescent dot identification and counting based on a color filtering technology, and carrying out gene amplification detection based on a target gene signal and a chromosome centromere signal. According to the method, more accurate intelligent segmentation of the FISH image is realized based on the SAM model, the accuracy of cell judgment is further improved by using a deep learning technology, the identification and counting of target gene signals are quickly realized, and the gene amplification level is better judged.

The application discloses a multi-intention sentence segmentation method and device, a storage medium and an electronic device, relates to the technical field of smart homes, and comprises the following steps: extracting the character features of the characters included in a to-be-segmented sentence carrying multiple execution intentions; converting the to-be-segmented sentence into a feature image according to the character features, wherein the feature image is used to represent the character features through image pixels; performing semantic segmentation on the feature image to generate multiple semantic images, wherein each semantic image is used to represent a target execution intention in the multiple execution intentions; and identifying the target sub-sentence corresponding to each semantic image in the multiple semantic images to obtain multiple target sub-sentences corresponding to the to-be-segmented sentence. By adopting the technical scheme, the problems, such as low segmentation efficiency, in the process of segmenting multi-intention sentences in the related art are solved.

The invention discloses a corn seed variety identification and contour segmentation method and device based on deep learning, and relates to the technical field of corn seed category identification. The problems that in the prior art, a trained corn variety recognition model is difficult to achieve integrated detection of variety recognition and seed segmentation, tiny differences between similar varieties and morphological changes between seeds of the same variety in a natural placement state are difficult to effectively process, the model parameter quantity is large, and the calculation cost is high are solved. The method comprises the steps that a training data set is constructed, each corn seed image comprises a plurality of corn seeds, an improved YOLOv11 model is constructed, on the basis of the YOLOv11 model, a backbone network is replaced with a ConvNeXt V2 network to optimize feature extraction efficiency and network weight reduction, an image-level variety classification head is newly added, a loss function adopts a double-task collaborative loss function, and the number of the corn seed images is smaller than the number of the corn seed images. Parallel output of image-level variety classification and seed contour segmentation is realized, and an improved YOLOv11 model is trained and applied.

According to the present disclosure, a method for decoding an image by a decoding device may signal picture partitioning related information based on a flag indicating whether sub-picture information exists and a flag indicating whether sub-pictures each include a single slice.