96 results about "Contour segmentation" patented technology

The invention provides a heavy truck battery compartment guiding method and system based on visual perception. The method and system are used for automatic battery replacement operation in a complex industrial environment. According to the system, a multi-camera fusion visual perception platform is constructed, a plurality of industrial cameras arranged on the ground or ceiling of a battery swap station are used for collecting local images of different visual angles of a battery compartment, and a complete visual field image is generated through feature matching and image splicing. A battery compartment is coarsely positioned by adopting a YOLO series model, a bounding box region is extracted, pixel-level contour segmentation is realized by introducing SAM, and the complex background and multi-interference environment recognition capability is enhanced. And after segmentation, calculating a minimum enclosing rectangle of the battery compartment, obtaining a center coordinate and a deviation angle, and transmitting a pose parameter to an upper computer control system. According to the method, the defects of the laser radar are avoided, image processing, the deep neural network and multi-view information are fused, the recognition precision and stability are improved, the battery replacement efficiency and the unmanned level of the electric heavy truck can be remarkably improved, and reliable support is provided for green traffic.

The invention relates to the technical field of industrial visual inspection, and discloses a leather surface defect identification method and system based on image processing. The method comprises the steps of performing preprocessing and multi-scale feature extraction on a leather image, generating a defect thermodynamic diagram through an attention mechanism, and dividing candidate regions; innovatively constructing a regional association graph, updating node embedding by using a graph attention network, and fusing global context information; and generating instance segmentation results through adaptive clustering and classifying the instance segmentation results. According to the method, region relevance is modeled through a graph structure, so that recognition of each region is benefited from global context information, and the problem that segmentation of adhesion and irregular defects is inaccurate in a traditional method is effectively solved; a clustering type instance generation mechanism can automatically aggregate discrete regions according to deep feature similarity, so that the defect contour segmentation precision and instance distinguishing capability under a complex texture background are improved, and accurate conversion from pixel-level prediction to instance-level segmentation is realized.

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 water level identification method and device based on image fusion and electronic equipment, and relates to the field of image water level identification or other related fields, and the method comprises the steps: employing a target instance segmentation model to carry out the segmentation processing of a target image in which a water area exists, outputting a global contour segmentation map including the water area and an initial mask map for completing preliminary segmentation of the water area; performing depth estimation on the target image through an improved target depth estimation model to generate a depth map; inputting the initial mask graph, the global contour segmentation graph and the depth graph into a multi-image fusion network model, and outputting a water area segmentation mask graph for complementing the sheltered water area part; extracting a water level line for calibrating the water area mask area in the water area segmentation mask graph; and outputting a water level detection report based on a comparison result of the water level line and the warning line. According to the invention, the technical problem of low water level detection precision caused by object shielding and wrong segmentation of a water area in the prior art is solved.

The invention provides a laying hen feeding scheme adjustment method and system based on moulting image recognition, and relates to the technical field of intelligent breeding, and the method comprises the steps: obtaining a multi-angle body surface image sequence synchronously collected by a plurality of laying hen individuals in a target chicken flock under a natural light condition; performing semantic segmentation according to the multi-angle body surface image sequence to obtain a feather contour segmentation map set; performing group feature joint extraction according to the feather contour segmentation map set to obtain a multi-dimensional feature tensor representing individual features and group distribution information at the same time; performing group state collaborative inference according to the multi-dimensional feature tensor to obtain a three-dimensional probability distribution vector; performing group nutrition demand mapping according to the three-dimensional probability distribution vector to obtain an optimal nutrition parameter combination suitable for the whole chicken flock; and generating a feeding scheme according to the optimal nutrition parameter combination to obtain a feeding instruction stream. The accuracy and adaptability of nutrition regulation and control in the moulting period are effectively improved.

The present invention provides an automatic semen analysis method based on multi-frame information fusion with occlusion awareness. The present invention first proposes the combination of an edge-sensitive U-Net model and an occlusion-aware tracker based on joint probabilistic data association to achieve accurate and stable sperm detection and tracking. Thanks to the pixel-level contour segmentation and the overlap inference based on multi-frame target contours of the present invention, the present invention can accurately distinguish two or more spermatozoa that are close but not overlapping, and provide accurate head positioning; can accurately predict and judge the occurrence, progress, and end of target overlap; and can effectively match spermatozoa before and after overlap to ensure the consistency of tracking. The present invention can be conveniently integrated into existing computer-aided sperm analysis systems to provide semen analysis data with better accuracy and robustness.

The invention provides an optimal estimation method and device for intravascular ultrasound image segmentation, equipment and a medium. The method comprises the following steps: acquiring an intravascular ultrasonic image; performing initial contour segmentation on the intravascular ultrasound image to obtain an initial contour of the intravascular ultrasound image; and performing forward Kalman filtering on the initial contour of the polar coordinate system to obtain an optimal estimated contour of the polar coordinate system. In the mode, the optimal segmentation problem of the blood vessel lumen wall and the vascular membrane contour can be converted into the optimal estimation problem of the contour position by using Kalman filtering, and the Kalman filtering is performed on the position information of the obtained initial contour point to obtain the minimum variance estimation of the contour position. And a stable and accurate contour segmentation result can be obtained.

The invention provides a digital person display method and system based on an intelligent VR space, and relates to the technical field of three-dimensional models.The method comprises the steps that multi-dimensional feature extraction is conducted on an original person video, and a dynamic feature data set is obtained; joint point cloud construction and registration are carried out based on the dynamic feature data set, and a space-time consistency skeleton model is obtained; performing surface mask generation on the video frame of the original character video through the space-time consistency skeleton model to obtain a dynamic human body grid with a separated background; and carrying out physical illumination fusion on the dynamic human body grid and the VR environment parameters to obtain a digital human VR entity model. According to the method, dynamic feature data set construction, joint point cloud registration, parameterized human body grid generation and physical illumination fusion are combined, high-fidelity digital human migration from a common video to a VR space is achieved, the key problems of motion distortion, rendering penetration and the like are solved, the motion track error is reduced, and the human body contour segmentation precision is improved.

The invention discloses a mine car obstacle recognition and distance measurement method and device and a storage medium. The method comprises the steps of constructing a segmentation template, marking image feature points, extracting an obstacle contour line, performing coarse and fine segmentation on a target obstacle based on the intersection point proportion of a background segmentation surface and the obstacle contour line, and determining a contour segmentation area of the target obstacle; the method comprises the following steps: marking point cloud feature points, constructing an obstacle template, carrying out matching mapping on a target obstacle and the obstacle template by adopting a non-rigid iterative nearest point algorithm, correcting feature point offset in a matching process in combination with a KD-Tree method, and calculating to obtain point cloud parameters of the target obstacle. And identifying the type of the target obstacle and measuring the distance according to the contour segmentation region and the point cloud parameters. According to the method, the target obstacle image and the point cloud information are combined, the target obstacle is segmented according to the intersection point proportion, the fuzzy area is smoothly segmented, the point cloud parameters are measured while the target obstacle is segmented, and the purpose of identifying the type and the distance of the target obstacle is achieved.

The invention discloses a solder paste defect detection method based on machine vision, relates to the technical field of printed circuit board detection, and aims at the challenge of solder paste printing quality detection in superfine-pitch high-reliability manufacturing, high-precision three-dimensional point cloud data is collected and an attitude matrix is marked through laser triangle-grating interference dual-mode imaging and board micro-vibration sensing; constructing an adaptive reference surface based on the point cloud and real-time fitting warping curved surface parameters, executing local height remapping, and eliminating the influence of micro warping; calculating a warping composite slope index and a glare texture robust index by using the height matrix, and adjusting a confidence threshold of the boundary enhanced convolutional network to realize real contour segmentation of adjacent pads; dynamically updating imaging parameters, accumulating real scene samples, and optimizing the adaptive ability of the model; and cross-shift knowledge reuse is realized through the shared model library. According to the invention, the volume measurement consistency and the first pass yield of the ultra-fine pitch bonding pad can be improved, and the process debugging time is shortened.

The invention discloses a building contour labeling method and device, and a building contour segmentation method and device, and the labeling method comprises the steps: obtaining a plurality of to-be-labeled images, so as to obtain an original data set; performing manual annotation on a part of the data set in the original data set to obtain a real annotation; processing the part of the data set by adopting a first image enhancement method to obtain first enhanced data, processing the part of the data set by adopting a second image enhancement method to obtain second enhanced data, and taking the first enhanced data, the second enhanced data and the part of the data set as an enhanced data set; inputting the enhanced data set into a pre-training large model to obtain a corresponding first prediction label; training the conversion network by adopting the first prediction label and the real label to obtain a trained conversion network; labeling the other part of the data set in the original data set by adopting the pre-trained large model and the trained conversion network to obtain a labeling result; and therefore, the manual labeling cost is greatly reduced.

The invention relates to a spine bone structure and bone mineral density automatic measurement method based on a CT image. The method comprises the following steps: performing CT scanning on a phantom with known bone mineral density to obtain a real bone mineral density calculation formula; all CT images in the DICOM format are read, DICOM analysis is carried out, and three-dimensional CT value data and other main information of all the images are obtained; performing outer contour segmentation on the whole spine in the CT image by using a deep learning network to obtain an outer contour of each vertebral body in the image; the method comprises the following steps: carrying out preliminary segmentation on cortical bones and cancellous bones by adopting a dynamic threshold method aiming at each vertebral body, selecting a preliminarily segmented cancellous bone region, using a maximum connected domain algorithm in a three-dimensional direction, retaining a maximum connected region, and further measuring the bone mineral density, namely calculating overall topology parameters, the bone mineral density and the bone mineral content of the current vertebral body; calculating the rubbing parameters, the bone mineral density and the bone mineral content of the current vertebral cortical bone and cancellous bone; and measuring the content of the cross section of the cone.

The invention relates to the technical field of computer vision and image processing, in particular to an end-to-end image registration method based on a monocular camera and micro rendering. The objective of the invention is to solve the problems of low registration precision and insufficient real-time performance caused by complex calculation, serious error propagation and poor adaptability to multi-modal data in a traditional image registration method. According to the main scheme, the method comprises the steps of extracting structured boundary information of a target object in an input image through a contour segmentation model, and generating a binary mask image; based on the contour segmentation result, estimating a 6D pose parameter of the target object through a 6D pose estimation deep network fusing a convolutional neural network CNN and a Transform module; and inputting the 6D pose parameters into a micro-renderable module, generating a prediction image, comparing the prediction image with an original input image, and performing end-to-end optimization and adjustment on model parameters to realize high-precision registration.

The invention discloses a high-accuracy house type image inner contour segmentation method, system and device. The method comprises the following steps: carrying out normalization processing (such as data enhancement and noise optimization) on data; detecting a target area based on an improved DETR model, extracting multi-layer features through an optimized encoder and decoder, and generating a bounding box and confidence and category thereof; combining the bounding box with the original image, randomly expanding the bounding box to extract a house area, and reducing watermark and advertisement interference in the image; and on the basis of an improved Mask2Former model, an optimized Transform decoder and an attention mechanism are utilized to generate a segmentation mask and a classification result. According to the method, DETR and Mask2Former models are combined, all functional rooms of the house type image are quickly and accurately segmented, and the blank of a large-scale house type image processing method is filled.

The invention relates to the technical field of medical image processing and computer aided design, in particular to a preoperative CT-guided thoracic stent parameterization customization system, which comprises an image processing module for acquiring and preprocessing a patient trachea CT image; the contour segmentation module is used for carrying out trachea contour segmentation on the preprocessed CT image and carrying out correction to obtain trachea three-dimensional contour data; the parameter extraction module is used for extracting morphological parameters of the trachea; the modeling optimization module is used for establishing a stent model according to the morphological parameters of the trachea, constructing a comprehensive objective function which aims at minimizing the geometric matching error of the stent and meeting the mechanical property of the stent, and optimizing the stent model based on the objective function to obtain an optimal stent model; and the parameter output module is used for outputting support design parameters. According to the method, the customization precision and efficiency of the thoracic cavity stent are improved, and the influence of subjective experience factors in a traditional customization method is reduced.

The invention discloses a real-time house type image outer contour detection and segmentation method, system and equipment, and the method comprises the steps: employing a two-stage processing flow, firstly carrying out the rapid detection of an input image, namely a house type image region, through employing an improved DETR target detection model, and positioning the rectangular position of a house; using the rectangular position as a prompt input of a subsequent segmentation model so as to provide an accurate target area for a subsequent segmentation step; and carrying out high-fineness segmentation processing on the extracted outer contour region by using an improved SAM segmentation model. According to the method, the rapid target detection capability of the DETR model and the high-quality segmentation performance of the SAM model are combined, the method is suitable for a large-scale building house type drawing outer contour segmentation task, can be widely applied to the fields of intelligent house property analysis, building design assistance, house type drawing automatic archiving and the like, shows higher robustness and precision in the task, and can be widely applied to large-scale building house type drawing outer contour segmentation. And the automation level of building design and analysis can be obviously improved.

The invention discloses a gas-liquid two-phase flow field real-time synchronous measurement method and system, and relates to the technical field of flow field measurement. The method comprises the following steps: acquiring a high-speed image acquired by a high-speed camera; setting parameters are adopted by the high-speed camera; performing post-processing on the high-speed image to obtain an optimized image; the post-processing comprises image denoising, gray scale segmentation, contour segmentation and roundness denoising; a PWC-Net network is constructed; and performing feature extraction on the optimized image by using the PWC-Net network, and performing gas-liquid two-phase flow field prediction according to the extracted features to obtain a final fluid velocity field. According to the invention, rapid, accurate and stable gas-liquid two-phase flow field velocity measurement can be realized.

The application discloses a kind of gas-liquid two-phase flow field real-time synchronous measurement method and system, it is related to flow field measurement technical field.The method comprises: obtaining the high-speed image collected by high-speed camera;The high-speed camera uses setting parameter;The high-speed image is post-processed, and optimization image is obtained;The post-processing includes image denoising, gray segmentation, contour segmentation and roundness denoising;PWC-Net network is constructed;The optimization image is extracted using the PWC-Net network, and gas-liquid two-phase flow field is predicted according to the extracted feature, and the final fluid velocity field is obtained.The application can realize the fast, accurate, stable gas-liquid two-phase flow field velocity measurement.

The application discloses a kind of based on the improved U-net's ultrasonic image's disassembly method, it is related to ultrasonic image technical field, by combining depth learning technique and image post-processing algorithm, can efficiently handle ultrasonic image type (single figure, double jigsaw, four jigsaw) The identification of operation, ultrasonic regional map contour segmentation and jigsaw disassembly, through the depth learning framework of U-net network, in combination with the feature extraction capability of ResNet50, efficient classification and segmentation of jigsaw image are realized, compared with traditional image processing method, the present application can process large-scale different organ tissue ultrasonic image in short time;Through accurate classification and segmentation, the contour of ultrasonic region in ultrasonic image can be accurately extracted, and jigsaw is effectively disassembled, the integrity of ultrasonic image is restored, avoids the case of misremoval or loss of information, and has wide clinical application prospect.

The invention provides a grain template automatic extraction method and system based on two-step contour segmentation, and the method comprises the steps: estimating the periodicity of a grain, and determining the initial width and height of the grain according to the periodicity in the horizontal and vertical directions; enhancing edge information in the wafer image; extracting straight lines in four directions in the image, wherein the four directions comprise a vertical direction, a horizontal direction, a 45-degree direction and a 135-degree direction; setting an initial contour of the crystal grain; performing image segmentation by using the active contour model, and iteratively updating the fine contour of the crystal grain; repeating two-step contour segmentation on the plurality of areas on the wafer image to obtain segmentation results of the plurality of wafer images; and smoothing a plurality of obtained results, and outputting the results as a crystal grain template. According to the secondary contour extraction method, the defects that the accuracy of the crystal grain contour based on linear extraction is low and an initial value needs to be manually set for an active contour model are overcome. According to the invention, the high-precision template of the crystal grain can be automatically extracted, so that real-time wafer defect detection is facilitated.

The application discloses a kind of small sample image classification methods of multistage distribution propagation, first, image dataset is acquired, contour segmentation is carried out to the image to intercept target area picture and do feature extraction, then the feature vector extracted is used as sample;GMDP module is constructed, and it is trained using training set sample, test set sample is input into the GMDP module trained, and the category of target object in corresponding image can be directly predicted and output.The MDPN network proposed in the application is optimized for the noise interference problem in small sample classification task and the multi-level feature extraction problem, through the target area positioning method based on instance segmentation, the influence of background and pseudo target on small sample classification is greatly reduced, and the multi-level distribution features of image are obtained by cascading GNN.

The invention provides a data fusion method and device for intravascular ultrasound image segmentation, equipment and a medium. The method comprises the following steps: acquiring an intravascular ultrasonic image; contour segmentation is carried out on the intravascular ultrasound image through a plurality of image segmentation modes, and a plurality of contour segmentation results are obtained; and performing data fusion on the plurality of contour segmentation results of the polar coordinate system based on Kalman filtering to obtain a final contour segmentation result of the polar coordinate system. In the mode, contour segmentation results of a plurality of vascular lumen walls and vascular membrane contours are obtained by using a plurality of image segmentation modes for an intravascular ultrasound image, and data fusion is performed on the plurality of contour segmentation results of the same type of contours based on the characteristic that Kalman filtering is used for data fusion, so that optimal estimation of contour positions is obtained. And a stable and accurate contour segmentation result can be obtained.

The invention discloses a particle contour recognition and segmentation model, an establishment method thereof, a particle size distribution analysis method, equipment and a storage medium. The establishment method of the particle contour recognition and segmentation model comprises the following steps: (1) data acquisition and labeling: collecting SEM graphs of a plurality of particles of a material, labeling the particle category in each SEM graph according to the particle size, and labeling the contour of the particles to obtain a labeled data set; wherein the particle category comprises a first particle and a second particle; and (2) model training: training a contour recognition model by using the labeled data set so as to accurately recognize the types and contours of the particles, and training a contour segmentation model so as to accurately segment the contours of the particles, thereby obtaining a particle contour recognition and segmentation model. Based on the obtained particle size distribution analysis method, the accuracy and the reliability of particle size measurement are improved, and large-scale particle size distribution detection can be rapidly processed.

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 application relates to a method and system for automatically identifying defects on a radiographic inspection film, belonging to the technical field of nondestructive testing. The method comprises collecting the film to obtain a digital image; based on the structural features of the weld, the image is preprocessed by partitioning, the weld area, heat-affected zone and base material background are identified and segmented, and different image enhancement strategies are adopted for different areas; the preprocessed image is input into an improved deep learning model combining the YOLOv8 target detection network and the U-Net segmentation network for detection, and the positioning, classification and contour segmentation information of the defects are output; based on the pre-set confidence threshold, the identification results are filtered and distinguished as clear defects and suspicious defects; a film evaluation report is generated, and the digital fingerprint of the film image is calculated, the fingerprint and the report are encrypted and uploaded to the blockchain storage for subsequent authenticity verification. The application realizes full automation and intelligentization of the film evaluation process, improves the identification accuracy and efficiency, and ensures the credibility and non-tamperability of the detection results.

The present application relates to a kind of intelligent detection method and detection system of surface coating micron-sized pore, scheme as follows: obtain coating surface image to be detected and execute standardization preprocessing;Preprocessing image is input into trained hole profile segmentation model, and hole profile coordinates are obtained;Based on hole profile coordinates, construct hole connected region by morphological operation, and according to geometric feature, Gap region that does not need to be punched is screened;Preprocessing image is input into trained missed hole detection model, and real missed hole region is obtained;According to hole profile coordinates, calculate geometric feature parameter, determine collapse hole and calculate hole distribution density, finally output detection quality result.The present application realizes the high-precision, automation detection of micron-sized pore by the combination of deep learning model and morphological operation, geometric parameter calculation, solves the inherent defects of artificial detection, meets the high consistency, large batch fast control demand of modern manufacturing industry to surface coating quality.

The application provides a digital human display method and system based on intelligent VR space, and relates to the technical field of three-dimensional models.The method comprises the following steps: performing multi-dimensional feature extraction on an original character video to obtain a dynamic feature dataset; performing joint node cloud construction and registration based on the dynamic feature dataset to obtain a space-time consistency skeleton model; generating a surface mask for video frames of the original character video through the space-time consistency skeleton model to obtain a dynamic human body grid separated from a background; and performing physical light fusion on the dynamic human body grid and VR environment parameters to obtain a digital human VR entity model.The method combines dynamic feature dataset construction, joint node cloud registration, parameterized human body grid generation and physical light fusion, realizes high-fidelity digital human migration from an ordinary video to a VR space, solves key problems such as motion distortion and rendering mistakes, reduces motion trajectory errors and improves human body contour segmentation accuracy.

The invention provides a crab instance segmentation system and method based on multi-branch feature fusion. The method comprises the following steps: acquiring a high-resolution crab image in a unified illumination and fixed environment by using image acquisition; performing fine polygon segmentation marking on crab shells and step feet of the crab images, converting the crab shells and the step feet into a COCO format, and constructing a high-quality data set; a multi-branch feature fusion model is constructed in model training, shallow details, a local structure and high-level semantic information are respectively extracted by introducing a three-branch feature fusion path, and feature enhancement is performed on a weak texture region and a fuzzy boundary in combination with a feature enhancement module, so that the target boundary perception capability and the small target segmentation precision are improved, and the target segmentation accuracy is improved. Training to obtain a crab identification model; and performing instance segmentation on an acquired image by using the crab identification model, and outputting a target contour, a segmentation mask and related feature information. Therefore, high efficiency and cost advantages are achieved while high-precision instance segmentation is guaranteed, and the method has good practicability and popularization value.

The present invention provides an image region boundary line segment detection method, device, and storage medium, belonging to the technical field of pattern recognition. It solves the problems of unclear contour segmentation and redundant detection in non-boundary regions of image objects existing in existing straight line detection algorithms or applications during the straight line detection process. The image region boundary line segment detection method of the present invention includes the following steps: Step S1: Calculate the truncated nuclear norm of the image set; Step S2: Input the image set with labeled region boundary line segments as the training sample set, and in Step S2.1: Perform deep neural network training on the training sample set; Step S3: Obtain the trained neural network capable of performing image boundary line segment detection. The present invention has the advantages that the calculation of the truncated nuclear norm can highlight the structural details of the image set, thereby improving the training effect of the neural network, so that when the trained neural network performs image boundary line segment detection, obvious contour segmentation and clear and concise non-boundary regions of image objects can be obtained.