20results about How to "Improve segmentation efficiency" patented technology

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 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.

The invention discloses a rock core CT image crack segmentation method and device based on deep learning. The method comprises the following steps: making a training data set; expanding the number of training samples in the training data set to form a new data set; a deep learning network model is constructed, the deep learning network model comprises a U-Net network, a contraction path for feature extraction in the U-Net network adopts a feature extraction network based on a VGG-16 network, and an up-sampling expansion path in the U-Net network is added with a CBMA attention module. The CBMA attention module is used for changing the attention of the model to different spatial positions and different channels in the feature map and improving the weight of the area where the rock core crack is located; performing training and parameter adjustment on the deep learning network model by using the training set until the model converges; and testing the trained deep learning network model by using the test set, and evaluating the crack segmentation effect of the deep learning network model. According to the invention, the efficiency of rock core CT image automatic analysis work can be improved.

The application provides a vitiligo lesion area segmentation method and device based on image editing and electronic equipment, and belongs to the technical field of image processing. The method comprises the following steps: contrast learning pre-training of dual-mode vitiligo data; data set expansion based on Poisson image editing mode; pre-processing of the expanded data set is input into a segmentation neural network for training; the model parameters based on the above segmentation training are used as the baseline segmentation model of the semi-supervised framework of the student-teacher model, and the unmarked data and the marked data are jointly trained; the prediction result of the unmarked data input into the teacher model is refined to obtain a pseudo label as the corresponding label to train the network; and the segmentation network obtains the segmentation result of the vitiligo lesion area. Thus, the pre-trained segmentation network is used, and a reliable neural network model is obtained by using image editing to automatically segment the vitiligo lesion area. This process does not need to rely on manual work, and the segmentation efficiency is improved.

The utility model discloses a kind of clean vegetable processing cutting device, it is related to clean vegetable processing technical field, including conveyer belt, conveying table, the conveyer belt one end is provided with conveying table, each corner at conveyer belt and conveying table bottom is uniformly fixedly connected with support leg, conveying table upper end surface is rotatably connected with threaded rod and threaded rod outer wall is threadedly connected with threaded sleeve, threaded sleeve upper end surface is fixedly connected with the vegetable feeding plate, conveying table left and right two ends are fixedly connected with extension plate, extension plate upper end surface is fixedly connected with support rod, fixedly connected with main plate between two groups of support rods, main plate upper end surface is provided with pressing mechanism and cutting mechanism, by the coordinated work of cam, reciprocating plate, sliding sleeve and the like structure, the automatic cutting of vegetable is realized, and reset is completed after each cutting, ensure the continuous operation and efficient production of equipment, after cutting, automatic trigger is achieved by the operation of plate, and the design of slanting end of plate can more effectively successfully push the cut vegetable into conveyer belt.

Embodiments of the present application provide a neural network-based medical image segmentation system, which comprises a feature extraction module for extracting features from a medical image and a classifier for segmenting the medical image according to the features extracted by the feature extraction module, wherein the feature extraction module comprises an encoder and a decoder constructed based on a convolutional neural network and a transformer connected between the encoder and the decoder. This scheme automatically learns and focuses on important regions at any position in the image via the transformer and suppresses irrelevant interference information, and can effectively exchange information between feature representations of different regions, different sub-pixels in the image, accurately depict the dependence and relationship between pixels, and thus accurately and explicitly capture and model long-distance global context information, thereby improving the segmentation efficiency and segmentation accuracy of medical images.

This invention provides a pipeline network segmentation method based on a node explosion algorithm, comprising: constructing an original pipeline network topology; node explosion: breaking the original pipeline network topology at designated segmentation nodes and deleting the designated segmentation nodes to obtain multiple subgraph topologies; node interpolation: in each subgraph topology, constructing edges between the designated segmentation nodes and their neighboring nodes, and re-inserting the designated segmentation nodes into each subgraph topology; and correcting the flow direction of all subgraph topologies after node interpolation. This invention solves the technical problem that the traditional DFS traversal segmentation method suffers from differences in segmentation results due to the different root node input order, and improves the efficiency of topology segmentation and the accuracy of simulation calculations.

The application relates to a multi-center applicable human brain magnetic resonance image brain structure automatic segmentation method, and the segmentation method specifically comprises the following steps: acquiring MRI data of different platforms, and performing N4 bias field correction and image standardization processing on the MRI data; taking U-Net as a basic framework, fusing a self-attention mechanism module, and constructing an MRI brain image structure segmentation network model suitable for multiple platforms; inputting the processed MRI data into the segmentation network model, training the segmentation network model by using transfer learning and domain adaptation technology, estimating the uncertainty of the output result, and further training the model by using high-quality unlabeled data screened out, so that the human brain magnetic resonance image brain structure automatic segmentation is realized. Compared with the prior art, the application can accurately and quickly segment the brain structure of different magnetic resonance imaging platforms, provides a more universal, efficient and reliable tool for the field of brain image analysis, and helps more extensive application and research exploration.

The present application relates to the technical field of mine safety production and geological disaster monitoring, in particular to a kind of open pit slope floatstone dangerous intelligent sensing method and related equipment, method is, obtain target slope image, and reconstruct depth map.Prepreg floatstone region segmentation is carried out to slope image using pre-trained PSPNet image segmentation model, and floatstone segmentation map is generated.Pixel-level fusion is carried out to segmentation map and depth map, and three-dimensional point cloud data of floatstone is extracted.Convex hull algorithm is used to carry out three-dimensional grid reconstruction to point cloud, and the geometric characteristic parameter of floatstone is calculated, based on fuzzy comprehensive evaluation theory, in combination with above-mentioned geometric characteristic parameter, the danger level of floatstone is comprehensively evaluated, and scientific basis is provided for mine safety management.The method realizes the full-process automation from image acquisition to danger level evaluation, improves the evaluation efficiency and accuracy.

This invention discloses a method for extracting character coordinates. The method includes: inputting a target text image into a feature extraction backbone network; obtaining character segmentation features and text line segmentation features through feature fusion of different layers in the backbone network; inputting the character segmentation features and text line segmentation features into a text line segmentation module and a character segmentation module, respectively, to obtain a character segmentation heatmap and a text segmentation heatmap of the target image; and calculating the coordinates of a single character based on the character segmentation heatmap and the text segmentation heatmap. Through this method, this invention avoids redundant feature extraction; exhibits high robustness for character segmentation; accelerates network convergence and improves network segmentation efficiency; and improves the accuracy of single-character coordinate extraction by back-deriving coordinates based on CTC recognition results and combining it with character segmentation methods.

The invention provides a chicken thigh meat and chicken breast meat transmission rack, which comprises a transmission rack, the transmission rack is provided with a plurality of layers of transmission pieces from top to bottom at equal intervals, the lower ends of two supporting legs on the left side of the transmission rack are connected with L-shaped supporting rods, vertical rods of the L-shaped supporting rods are provided with erection clamping pieces for transmitting and erecting chicken carcasses, and the erection clamping pieces are connected with the L-shaped supporting rods. The upper end and the lower end of a supporting rod on the left side of the conveying rack are each provided with a cutting piece used for achieving chicken carcass cutting, and the cutting pieces correspond to the conveying pieces one to one. According to the invention, the chicken carcass segmentation efficiency and precision can be improved, and manual intervention is reduced.

The invention discloses a material microstructure interactive efficient three-dimensional reconstruction method, and relates to the technical field of material microstructure analysis and three-dimensional reconstruction, and the method comprises the steps: completing the selection of a target microstructure on a material microscopic image based on the prompt segmentation capability of SAM2; tracking a target microstructure in a material microstructure series section by using a memory mechanism of SAM2, and performing tracking optimization by modifying a memory code of a segmentation mask; introducing a tracking constraint condition of a morphological change trend of the microstructure between adjacent layers, and combining a bidirectional tracking strategy to obtain a corresponding relationship between target microstructure layers; and on the basis, a contour surface extraction algorithm is adopted to generate a corresponding three-dimensional structure model. According to the method, continuous tracking and high-quality three-dimensional reconstruction of the complex material microstructure can be achieved without retraining the model, and the method has the advantages of being high in universality, low in interaction cost and high in reconstruction stability and is suitable for three-dimensional representation and analysis of the material microstructure.

The utility model relates to the technical field of electronic packaging, and discloses a packaging base, a composition board and an electronic device.The packaging base comprises an insulating substrate, the insulating substrate comprises a main body part and a concentric-square-shaped part, the bottom of the main body part is provided with a first end face, and the top of the concentric-square-shaped part is provided with a second end face; the main body part and the concentric-square-shaped part jointly define a concave cavity, and the circumferential outer walls of the main body part and the concentric-square-shaped part are connected to form an outer side face; the plurality of external terminals are arranged on the first end face; and a plurality of connection wires, one end of which is connected to one of the external terminals, and the other end of which extends inside the main body portion and is exposed at the outer side surface to form a first exposed portion. The beneficial effects of the utility model are that the wiring structure is simplified, and the electroplating conduction of the external terminals between the adjacent packaging bases is facilitated; and meanwhile, the wiring structures are not exposed on the surfaces of the segmentation grooves, so that the wiring structures between adjacent packaging bases are prevented from being adhered when the composition board is electroplated, the segmentation efficiency of the packaging base composition board is improved, and the production efficiency is effectively improved.

This invention discloses an intelligent detection method for smoking behavior in no-smoking areas based on target detection and image segmentation. The method involves scaling up the image / video frames of the no-smoking area to be detected, and then preprocessing the scaled images using a standardized approach. A YOLO11 target detection model is then constructed to detect smoking-related targets in the preprocessed no-smoking area images, generating detection images. Rectangular regions containing suspected cigarettes and related human hands are cropped from the detection images and input into a SAM image segmentation model for optimization. Simultaneously, the center coordinates of the suspected cigarettes are also input into the model, achieving pixel-level precise segmentation of the cigarettes, hands, and smoke, extracting accurate target contour features. Finally, by combining spatial relationships and feature judgment rules, the final identification and determination of smoking behavior is completed.

The application discloses an ovarian mass segmentation method based on an ultrasonic image, and relates to the field of medical image processing, and comprises the following steps: uploading an ultrasonic image, calculating a phase compensation factor based on acoustic propagation parameters of the ultrasonic image, and performing adaptive acoustic phase calibration on the ovarian ultrasonic original image through the phase compensation factor; based on acoustic impedance differences of pixel points, three types of core information, including pixel spatial coordinates, gray gradient changes and adjacent pixel acoustic attenuation correlations, are fused to construct a three-dimensional feature tensor for representing an ovarian mass; the application corrects image deviation through adaptive acoustic phase calibration, and strengthens ovarian mass feature representation by fusing multi-dimensional core information, so that effective areas are accurately screened, and discrete interference pixels are removed; a smooth closed contour is constructed based on feature extreme points and anatomical shape constraints, and multi-objective optimization is used to realize accurate boundary separation of the mass and normal tissues.

The application discloses an image processing method, which comprises the following steps: determining an image to be segmented; performing calculation analysis on a region of interest included in the image to be segmented by using a preset response function, and determining a target analysis size corresponding to the region of interest included in the image to be segmented; wherein the response function is, for each pixel point in the region of interest included in the image to be segmented, a first characteristic value, a second characteristic value, a preset coefficient, and a pixel point of the image to be segmented; and based on the target analysis size corresponding to the region of interest included in the image to be segmented, performing image segmentation processing on the image to be segmented to obtain a target image. The application also discloses an image processing device, equipment and a storage medium.

The application relates to a grinding area planning method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining medical image data of a hip joint part of a target object; inputting the medical image data into a pre-trained deep learning model to obtain bone tissue data and position information of a hip socket center point output by the deep learning model; generating a pelvis model to be ground based on the bone tissue data; determining a grinding area of the pelvis model according to the pelvis model, the position information and a preset acetabular cup model, and visually displaying the pelvis model and the grinding area. Through the application, the problem that manual segmentation based on pelvis tissue depends on the experience and proficiency of operators in the related art, resulting in low segmentation efficiency and low segmentation accuracy, is solved, automatic segmentation of medical image data based on a pre-trained deep learning model is realized, the segmentation accuracy and efficiency can be improved, and the pelvis model and the grinding area can be visually displayed.

The utility model provides a duck head and duck body cutting device which comprises a main box body, an auxiliary box body and an auxiliary box body. The conveying device 200 is used for conveying the whole duck; the central control module is used for controlling the equipment to start or stop; the first slide rail is used for sending out the cut duck body; the second slide way is used for sending out the cut duck heads; the cutting device is used for cutting the duck head and the duck body. By arranging the conveying device 200, the cutting device, the first slide way and the second slide way, automatic cutting of the duck heads and the duck bodies is achieved, meanwhile, the duck heads and the duck bodies can be sent out respectively, and follow-up classification is facilitated; the duck head cutting device is simple in structure, low in manufacturing cost and capable of accurately cutting the duck body and the duck head, and the problem that the duck head or the duck body is damaged due to the fact that the sizes of the cut ducks are different is solved.

The application provides a page processing method and device, wherein the page processing method comprises the following steps: determining an element set corresponding to a target page according to a layout sketch of the target page; drawing an element block corresponding to each element in the element set according to attribute information of each element, and generating an element block distribution diagram according to the element block corresponding to each element; performing iterative segmentation processing on the element block distribution diagram to determine a target segmentation region generated by a segmentation processing result of each iterative segmentation period; and constructing a page layout tree structure of the target page according to a generation order of each target segmentation region. The element block distribution diagram corresponding to the target page is drawn according to the layout sketch of the target page, and then the element block distribution diagram is subjected to iterative segmentation processing to obtain the target segmentation region corresponding to each iterative segmentation period, so that the page layout tree structure of the target page is constructed according to the generation order of each target segmentation region, without relying on manual segmentation of the target page, thereby improving the segmentation efficiency.