78 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 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 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 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 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 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 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 application relates to the field of image recognition, and more particularly to a kind of idle intelligent discrimination method and system of rural house based on multi-source data fusion.The method comprises the following steps: obtaining multi-source remote sensing satellite image, multi-time point fitting is carried out, and multi-temporal image set is constructed;Multi-temporal image set is carried out rural house target identification and fine profile segmentation, and multiple rural house image frame is marked;Based on multiple rural house image frame, time sequence tracking segmentation is carried out, and the multi-temporal image frame of each image frame is obtained;Based on the multi-temporal image frame, time sequence idle evolution mining is carried out, and the idle evolution track of each rural house is generated;According to the idle evolution track, idle state trend analysis is carried out, and intelligent discrimination is carried out, and idle house recognition result is obtained.The present application realizes the fast, accurate identification idle rural house, improves rural house resource management efficiency.

The application discloses a curve segmentation fitting method and device for image segmentation and a storage medium, and comprises the following steps: pre-processing an image to obtain contour data; performing a contour segmentation algorithm of a DCE evaluation standard according to the contour data to obtain a segmentation point list; and performing a curve fitting algorithm based on an Euler curve model according to the segmentation list to obtain a final fitting result. The automatic segmentation of the contour can be realized, and subjective errors caused by human intervention are reduced.

The present invention discloses a 3D printing path planning method, device, and medium based on a sinusoidal curve, and relates to the field of 3D printing technology. The method comprises: slicing and layering a 3D printing model to obtain the contour line of the target slice; setting a starting point and an end point on the contour line to divide the corresponding contour line into an upper contour and a lower contour; evenly setting the same number of points on the upper contour and the lower contour, connecting each upper contour segmentation point with the corresponding lower contour segmentation point to obtain a plurality of segmentation lines, and using the line connecting the midpoints of each segmentation line as the central axis of the filling path; setting parameter information of the filling path of the sinusoidal curve to be filled according to the direction of the central axis, and determining the printing order of the sinusoidal curve to be filled; presetting the amplitude of the sinusoidal curve to be filled according to the length of the segmentation line, and determining the filling path of each sinusoidal curve to be filled according to the parameter information and the printing order. The present invention can shorten printing time, enhance the strength of printed parts, and improve 3D printing quality.

The invention discloses a three-dimensional model reconstruction method based on a sheet metal bent part expansion drawing, and the method is characterized in that the method comprises the following steps: S1, analyzing a sheet metal expansion drawing, classifying the analyzed primitive entities, and storing the primitive entities in corresponding primitive sets; s2, primitive preprocessing: performing de-weighting, breaking and discrete processing on the primitive, and screening out a metal plate bending line; s3, closed contour searching; s4, judging whether a closed contour exists or not, if not, repeating S3, and if yes, entering S5; s5, contour segmentation and topology construction: performing inner row segmentation on the outer contour and the inner contour by using a bending line, and establishing a geometric adjacency relation between the inner contour and the outer contour; s6, bending deduction is conducted, specifically, a deduction value is calculated according to the workpiece and bending information, and contour deduction is conducted on the contour; and S7, performing three-dimensional reconstruction, and constructing a three-dimensional entity model. According to the method, the technical defects that three-dimensional model reconstruction cannot be correctly carried out on a complex sheet metal expansion drawing and the model size error is large are overcome.

The embodiment of the invention provides a robot environment object recognition method and system, and relates to the technical field of robot visual recognition, and the method comprises the steps: obtaining a background diffuse reflection light spot, and collecting a multispectral image data stream which comprises a visible light image and a near-infrared image; analyzing the multispectral image data stream, and quantifying the response difference of the background diffuse reflection light spot and the coffee cup under different spectral bands to obtain a target spectral response difference; generating a de-interference image based on the target spectral response difference; performing contour segmentation on the interference-removed image to obtain the contour of the coffee cup; and calculating the spatial position and posture of the coffee cup based on the contour. The success rate of grabbing operation of the robot and the overall working efficiency can be improved.

This disclosure relates to a method, apparatus, and storage medium for cloaked target detection in millimeter-wave images based on deep learning. The method includes: preprocessing the acquired image to statistically analyze the target distribution patterns; segmenting the target contours according to the target distribution patterns and fusing them with a background sample set; detecting specific target objects based on a target detection network; and detecting cloaked targets in millimeter-wave images based on training an improved target detection model. Through the various embodiments of this disclosure, the generalization ability of the model and its ability to detect targets at different scales are effectively improved, meeting the needs of practical applications.

This case involves a target contour uncertainty error quantification method based on conditional constrained probability generation, which is used to solve the problem that existing technologies cannot provide intuitive explanations of nodule ultrasound diagnostic results, and that existing technologies find it difficult to simultaneously learn clinical opinions from multiple experts. The method proposes a target contour uncertainty error quantification method based on conditional constrained probability generation. It captures the semantic and structural coding information in nodule ultrasound images by establishing a deep learning model, maps the coding information to the latent space distribution, and then performs sampling. Based on the sampling results, the target contour segmentation results and uncertainty quantification are obtained. The method uses the model to learn the annotation distribution from multiple experts, outputs the uncertainty quantification of its own prediction results, effectively improves the interpretability of the model, and helps doctors identify high-risk areas of nodules, thereby enhancing the transparency and reliability of the method in actual clinical applications.

The application discloses a multi-target recognition and contour segmentation method based on an infrared thermal image temperature gradient, and particularly relates to the technical field of infrared image recognition, and is used for solving the problem that in the existing multi-target recognition of an infrared thermal image, adjacent target thermal zones are connected due to continuous temperature transition and unclear boundary gradient, and thus the target contour is connected, and it is difficult to accurately distinguish multiple targets and the contour attribution of the targets. Through local standardization processing on the infrared thermal image of a to-be-recognized scene, a gradient stable boundary band is formed, and when a same candidate thermal connected region contains multiple target temperature peak seed regions, a temperature descending section, a gradient direction reverse section and a gradient amplitude contraction section are extracted to determine a gradient saddle channel, and then a target separation boundary is generated, so that when temperature transition connection occurs in the adjacent target thermal zones, the separation position can be accurately determined, the target merging recognition risk is reduced, and the multi-target recognition and contour segmentation accuracy of the infrared thermal image is improved.

The invention relates to an irregular cross scratch contour segmentation algorithm, which comprises the following steps of S1, setting input data {p1, p2, p3,..., pn}; s2, contour processing: S21, drawing contour points on an all-black grey-scale image with the same size as the original image, and filling white in the middle of the contour; s22, calculating midpoints of two sides of the contour; s23, using a machine to cluster midline points by using a DBSCAN clustering algorithm so as to obtain midlines segmented at the cross points; s24, discrete noisy points are removed through data filtering; s25, cyclically traversing the center line point array, and calculating the slope of each center line; s26, carrying out center line classification; s27, according to the contour points to which the midline points belong, the contour points are sorted according to the types of the midline points, and a complete contour belonging to one scratch is obtained; s3, outputting a result; according to the method, the number of the connected and crossed scratches in the irregular contour can be accurately segmented, so that the problem that the crossed scratches cannot be segmented by a deep learning technology is solved, and the method is suitable for equipment with the requirement for controlling the number of the scratches.

The application discloses a three-dimensional body construction method and device based on CT images, a storage medium and a terminal. A CT sequence image of a target object is acquired. In response to a user's manual contour segmentation operation on a first target CT image in the CT sequence image, the first target CT image is subjected to first contour segmentation, and a first contour image of the target object is obtained. Based on each first contour image, a second target CT image in the CT sequence image, except for the first target CT image, is subjected to second contour segmentation, and a second contour image of the target object is obtained. A three-dimensional body of the target object is constructed according to each first contour image and each second contour image. Since the second contour segmentation is active processing and calculation on the manually segmented first contour image, when the second contour image is obtained, manual operation on a large number of images can be avoided, human-computer interaction is reduced, the three-dimensional body construction efficiency is improved, and the accuracy of the three-dimensional body of the target object can be ensured by the manually segmented first contour image.