748 results about "Triangulation" patented technology

Disclosed is a method for constructing a bionic binocular active visual simultaneous localization and mapping (SLAM) system. The method includes: S1, constructing a binocular active visual system, peripheral vision is simulated using a panoramic camera, a panoramic value map is constructed based on the panoramic camera, and a binocular bionic eye gaze control strategy based on the panoramic value map is proposed; S2, designing a SLAM algorithm framework which is suitable for motion of the two bionic eyes, where relative pose optimization of left and right cameras based on a Perspective-n-Point algorithm and depth calculation for binocular matching points based on triangulation are adopted in a SLAM tracking thread; and S3, with the assistance of camera poses estimated by the SLAM system and sparse landmark point coordinates, training a self-supervised depth estimation network, to provide dense depth estimation for a mapping module of the SLAM system, achieving dense mapping.

The invention discloses a multi-view panoramic point cloud splicing method, which comprises the steps of performing joint calibration on a binocular camera and a laser radar through a calibration algorithm, and aligning a coordinate system; a binocular camera is used for collecting a two-dimensional image and carrying out distortion correction, a depth map is generated based on parallax calculation, and three-dimensional point cloud reconstruction is carried out in combination with a triangulation principle; performing deep learning target detection on the two-dimensional image, and screening a line rod assembly area through non-maximum suppression; multi-view point cloud data are collected through a laser radar, point cloud segmentation processing is carried out, and a telegraph pole assembly point cloud subset is reserved; matching the two-dimensional pixel area of the telegraph pole assembly with a laser radar point cloud projection result, and screening point cloud data belonging to the telegraph pole assembly; and carrying out alignment and fusion on the multi-view point clouds through initial registration and fine registration by adopting an improved point cloud splicing algorithm to generate a panoramic point cloud image. According to the method, the point cloud splicing processing time is shortened, and the robustness, precision and integrity of point cloud splicing are improved.

The invention discloses a real-time three-dimensional modeling method and system based on 5G flight control and a medium, and belongs to the field of three-dimensional modeling, and the method comprises the steps: obtaining a modeling demand and task environment parameters of a user, and planning a route; controlling the unmanned aerial vehicle to fly according to the planned route; judging whether the position data and the attitude data of the unmanned aerial vehicle at each waypoint are within a preset threshold range, and if so, controlling the unmanned aerial vehicle to collect image data of the target area; the collected image data is preprocessed; extracting feature point data of the target area by utilizing machine learning according to the preprocessed image data; converting the feature point data of the target area into sparse point clouds by using a triangulation algorithm; converting the sparse point clouds into dense point clouds through an MVS algorithm; and according to the pre-processed dense point cloud, a Poisson surface reconstruction algorithm is adopted to establish a three-dimensional grid model of the target area. Aiming at the low precision of unmanned aerial vehicle three-dimensional modeling in the prior art, the method improves the precision of unmanned aerial vehicle three-dimensional modeling.

The invention belongs to the technical field of ocean observation, and discloses an ocean wave field reconstruction method and device based on binocular vision. An ocean wave field reconstruction method based on binocular vision comprises the steps that internal parameters of a camera are calibrated through a Bouguet calibration algorithm, a binocular camera provided with an IMU sensor is used for collecting images and pose information, feature points are extracted through an SURF algorithm, feature matching is completed under a game theory framework, and abnormal points are removed through an RANSAC algorithm. Then, camera parameters are calculated based on an eight-point method, and the camera parameters and three-dimensional point coordinates are optimized through an SBA algorithm; thirdly, generating a disparity map by adopting epipolar correction and an SGM algorithm, and performing morphological processing on the disparity map; and the processed image is subjected to triangulation to generate a three-dimensional point cloud, and filtering is carried out. And finally, estimating a sea level position through an RANSAC algorithm, and optimizing a result through a least square method to complete three-dimensional reconstruction of ocean waves.

The invention discloses a seismic isolation structure support three-dimensional deformation monitoring method and system based on binocular vision. The method comprises the steps that a binocular camera is adopted to collect monitoring scenes under the view angle of a left camera and the view angle of a right camera; performing image distortion removal and three-dimensional correction on the monitored image pair by adopting camera internal and external parameters obtained by camera calibration; carrying out target detection on the corrected image by adopting a target detection algorithm; according to the coordinates of the target detection frame, calculating a cutting frame and cutting target areas in the left image and the right image; carrying out stereo matching on the cut image pair; according to the disparity value of each pixel in the disparity map, depth information of the target is calculated through a triangulation method, and finally a three-dimensional dense point cloud of the target is generated; performing optimization processing on the target three-dimensional dense point cloud obtained in the previous step to obtain an optimized point cloud; solving a rigid body transformation matrix of the two-point clouds by using an iterative nearest point algorithm; according to the invention, the cost of monitoring the three-dimensional pose deformation of the rubber support can be reduced, and the robustness and precision are effectively improved.

The invention provides a three-dimensional modeling and mapping method, system and device based on a fixed-wing unmanned aerial vehicle, and relates to the technical field of geographic mapping. According to the method, through synchronous data acquisition of an inclined camera, a laser scanner and a position and attitude measurement system, and in combination with navigation trajectory calculation, point cloud coordinate transformation, image attitude calculation and multi-source data fusion processing, space collaboration of image data and point cloud data is realized under a unified coordinate system. And performing aerial triangulation based on the fused data, further carrying out spatial modeling and texture mapping, and effectively improving geometric consistency and modeling continuity among multi-source data. According to the method, ground control points do not need to be arranged, high-precision and high-consistency geographic space information expression can be achieved, and real scene modeling and multi-type surveying and mapping data result generation in a large-scale mapping scene are supported.

The invention discloses an air-ground integrated measurement method for an original terrain, and relates to the technical field of engineering surveying, and the method comprises the steps: S1, carrying multi-sensor aerial survey by an unmanned aerial vehicle, obtaining an image and POS positioning data, scanning a blind area of the unmanned aerial vehicle by using a three-dimensional laser scanner, obtaining laser point cloud data, and carrying out the scanning of the laser point cloud data; constructing a multi-source data set including optical images, POS positioning data and laser point cloud, and forming an air-ground complementary data acquisition mode; s2, geometric correction and aerial triangulation encryption are carried out on the unmanned aerial vehicle image, denoising and multi-station registration are carried out on the laser point cloud data, and the data quality is improved; s3, unifying a multi-source data space reference through coordinate system unification, feature fusion and decision-level fusion, and realizing feature complementation; s4, constructing a digital elevation model and a digital surface model based on the fused data, and generating a topographic mapping map; the method breaks through the limitation of a single technology through air-ground technology complementation, and ensures the global data coverage of complex terrains.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention belongs to the technical field of super high-rise building three-dimensional reconstruction, and particularly relates to a multi-source data fusion super high-rise building group live-action three-dimensional model construction method. According to the method, an initial three-dimensional model is generated through a series of processing such as aerial triangulation encryption and triangulation network construction based on multi-source image data, in the process of recognizing a fuzzy region and performing data supplementary collection, regions with texture loss and structure distortion in the initial three-dimensional model can be positioned, supplementary collection requirements are determined according to characteristics of different regions and a preset threshold value, and the recognition accuracy of the initial three-dimensional model is improved. The method comprises the following steps of: performing oblique photography on an unmanned aerial vehicle to acquire data in a supplementary manner, fusing the data with original data, extracting a building structure contour, matching high-resolution texture data, performing texture binding and processing and the like to form a building monomer model, and performing spatial position and texture fusion on the building monomer model and a process three-dimensional model to generate a regional three-dimensional live-action model. And finally, splicing and fusing the three-dimensional live-action models of all the areas to form a complete super high-rise building group live-action three-dimensional model.

The invention discloses a construction site three-dimensional scene reconstruction method based on an unmanned aerial vehicle image and a monitoring video. The method comprises the following steps: acquiring a construction site scene multi-view image; a sparse three-dimensional point cloud and a camera pose are generated through a feature matching and motion recovery structure algorithm, and dense reconstruction is carried out to obtain a global three-dimensional point cloud; aligning the global three-dimensional point cloud with a world coordinate system by using geographic position information; estimating the position of a shooting camera in the three-dimensional point cloud, sampling a candidate view angle and rendering a virtual RGB image; based on two-dimensional feature matching of the shot image and the virtual RGB image, internal parameters and external parameters of the shooting camera are iteratively solved through triangulation and a pose optimization algorithm; monocular depth estimation is carried out on the shot image, and the shot image is converted to a measurement scale through static region depth alignment; and projecting the depth of the shot image to the three-dimensional point cloud, updating the dynamic object in real time, and fusing the dynamic object into a complete three-dimensional scene model. The method has the advantage that the real-time three-dimensional reconstruction of the dynamic scene of the construction site is realized.

The invention discloses a shock wave overpressure field global measurement method based on multi-view image fusion, belongs to the technical field of explosive shock wave measurement, and is suitable for weapon equipment power evaluation and blasting safety analysis. The method comprises the following steps of: synchronously acquiring a time sequence image of the whole explosion process through a distributed multi-view high-speed imaging system, preprocessing the image by adopting pixel-by-pixel comparison, square operation enhancement and normalization processing, and positioning a shock wave edge contour; the blasting center coordinate is positioned through binocular parallax, the shock wave initial radius is determined by combining spatial domain analysis, and the key feature points of the front and rear edges of the wavefront are detected by using a dynamic search window and a radial gradient field. Based on multi-view constraints, a three-dimensional point cloud is generated through direct linear triangulation, and a three-dimensional wave front form is reconstructed in combination with least square spherical fitting. And finally, constructing a wavefront radius-time evolution model, and deducing a quantitative relationship between the instantaneous propagation velocity and the peak overpressure in combination with a Ranki ne-Huton iot relationship, thereby realizing the global high-precision calculation of the overpressure field.

The invention provides a three-dimensional space track identification method based on multi-view three-dimensional reconstruction, which comprises the following steps of: shooting the same scene from different angles by using a plurality of cameras, and determining a picture intersection part of the plurality of cameras as a research scene; frame extraction and pedestrian detection are carried out on the video of each camera, pedestrians and position information of the pedestrians in each frame of video are identified, a unified identity number is distributed to the same pedestrian, registration is carried out on video frame images of different cameras, the pixel position of each pedestrian in each camera is obtained, and a multi-view-angle triangulation method is adopted, and internal and external parameters of the cameras are combined, so that the pedestrian positioning accuracy is improved. Solving a three-dimensional point coordinate of each pedestrian in a camera coordinate system; the three-dimensional point coordinates are converted into coordinates in a world coordinate system, and world coordinate system coordinates of pedestrian point locations of all frames and all numbers are obtained; and according to the time information, connecting the pedestrian point locations of each frame and each number to form a track, and completing reconstruction of the pedestrian three-dimensional space track in the world coordinate system. According to the invention, manpower and calculation cost can be reduced.

The invention discloses an unmanned aerial vehicle camera attitude estimation optimization method and device, and relates to the technical field of unmanned aerial vehicles. The method comprises the following steps: acquiring data by using an unmanned aerial vehicle carrying various sensors, preprocessing, carrying out feature extraction and matching on preprocessed image data, removing mismatching points, and based on an initial point cloud, carrying out sparse reconstruction in an incremental expansion and bundle adjustment optimization stage; integrating the re-projection error term, the flight path constraint, the epipolar geometric constraint and the triangulation constraint into a target function; constructing a model for predicting the pose correction based on the deep residual convolutional neural network, and taking the initial pose of the camera and the corresponding local image as input; and adjusting the weight of each constraint in the target function based on the predicted pose correction and pose confidence, and minimizing the target function to improve the precision of unmanned aerial vehicle camera pose estimation. The problem of reconstruction scene deviation caused by inaccurate camera attitude estimation in the prior art is solved.

The invention relates to the technical field of digital twinning, and discloses a digital twinning modeling method, device and equipment, a storage medium and a product, and the method comprises the steps: building a static environment model through selecting a corresponding processing technology based on the characteristics of a fixed facility and the characteristics of an operation machine, obtaining the point cloud data of a material pile through a multi-source sensor and a two-dimensional laser radar, and obtaining the point cloud data of the material pile; obtaining a virtual material pile irregular triangulation network model through a point-by-point insertion algorithm based on the material pile point cloud data, extracting material pile surface data from the virtual material pile irregular triangulation network model, and constructing a dynamic scene model based on the material pile surface data according to the material pile boundary and the material pile surface data; and a digital twin bulk cargo model is obtained by integrating the static environment model and the dynamic scene model. According to the invention, based on the fixed facility features, the operation machinery features and the two-dimensional laser radar, the static environment model and the dynamic scene model are integrated, so that the bulk cargo modeling data precision and the model real-time mapping stability are improved.

The invention belongs to the technical field of marine surveying and mapping, and discloses an unmanned aerial vehicle laser / shipborne multi-beam data land and water topographic mapping method, which comprises the following steps of: coarsely extracting a data overlapping region through a virtual grid, finely extracting the overlapping region by using an alpha-shape algorithm, and obtaining boundary points; removing edge triangles in an irregular triangulation network constructed by the unmanned aerial vehicle laser sounding data and the shipborne multi-beam sounding data; calculating a vertex normal vector, taking a vertex tangent plane of the multi-beam triangular irregular network as a registration element, carrying out weight distribution according to the distance from a point to the vertex, and carrying out coarse registration through a point-to-plane ICP registration algorithm; and constructing a precise registration model based on a point-surface distance, and eliminating the elevation difference of the overlapping region by minimizing the normal distance from the unmanned aerial vehicle laser sounding point to the multi-beam sounding triangular surface element. By adopting the point cloud fusion model for processing, the phenomenon that different source point cloud elevations in the overlapping area are inconsistent is effectively improved, and underwater topographic data fusion is realized.

The invention discloses a three-dimensional vision calibration and positioning method and system based on an event camera, and belongs to the field of positioning. The method comprises the steps of constructing an array containing a plurality of unique frequency coding light sources, constructing a binocular event camera system, collecting light source array light signal event streams, extracting pixel positions, frequencies and timestamp information, determining pixel coordinate centroids of the light sources through frequency and pixel clustering analysis, and establishing frequency and centroid mapping. Auxiliary light sources with the same coding rule are arranged on the surface of a target object, and after a binocular system collects light signals of the auxiliary light sources, the center-of-mass coordinates of the light sources are matched according to the mapping relation. And based on event camera calibration parameters and binocular external parameters, correcting and stereoscopically matching centroid coordinates, and calculating three-dimensional coordinates of the auxiliary light source through triangulation. And in combination with the known geometric topological relation of the auxiliary light source on the surface of the object, the spatial pose of the target object is solved by using a rigid transformation algorithm, and high-precision positioning is completed. According to the embodiment, the calibration precision can be improved in a complex application environment.

The invention particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster hidden danger point detection method and system. Comprising the following steps: S1, synchronously acquiring vegetation canopy penetration data, multispectral soil humidity spatial distribution data and lithology distribution data by adopting an unmanned aerial vehicle carried image data acquisition device and a multi-band laser radar, and generating an orthoimage and a three-dimensional point cloud data set; s2, fusing the orthoimage and the three-dimensional point cloud data through an aerial triangulation encryption algorithm to establish a three-dimensional model, loading the multispectral soil humidity spatial distribution data into the three-dimensional model to generate a dynamic digital elevation model, and generating a contour line topological structure through a TIN triangulation network algorithm; s3, extracting a slope factor, topographic relief, vegetation coverage, fracture distance and a lithologic factor based on the three-dimensional model to construct a geological disaster hidden danger system; according to the system, the geological disaster occurrence time can be estimated, and the rescue and protection treatment period can be effectively planned based on the geological disaster occurrence time.

The invention provides a minimum cost flow phase unwrapping method and device based on phase gradient rate assistance. The method comprises the steps that an SAR data set and DEM data of a to-be-monitored area are acquired; based on this, a differential interferogram and a coherence map are generated; respectively calculating a shortest time baseline phase gradient rate and a full time baseline phase gradient rate based on the differential interferogram; constructing a Delaunay triangulation network based on the differential interferogram and the coherence graph, and determining a rotation-free constraint and a design matrix; on the basis of the coherence graph, the shortest time baseline phase gradient rate and the full time baseline phase gradient rate, performing weight determination on each triangular arc segment in the triangulation network to obtain a weight corresponding to each triangular arc segment; and based on the non-rotation constraint, designing a matrix and weights corresponding to the triangular arc segments to solve the integer ambiguity, and completing a phase unwrapping task based on the integer ambiguity. According to the embodiment, the absolute phase of the large-gradient deformation area is obtained, so that high-precision deformation monitoring of the earth surface is realized.

A measurement and positioning system based on machine vision and laser triangulation, which includes a machine vision system, a laser ranging system, and a standard positioning target configured to be installed on a positioned workpiece, where the laser ranging system is configured to project laser light onto the standard positioning target so as to perform a three-dimensional height measurement, and the machine vision system is configured to capture and image a positioning target and a laser image projected on the standard positioning target and to measure a two-dimensional coordinate position. The measurement system of the present application can realize a rapid three-dimensional measurement and precise positioning of a spatial position of the positioned object by means of the standard positioning target installed on the positioned object.

The invention discloses a farmland precise fertilization control method based on multi-source data fusion and deep learning, and the method comprises the steps: obtaining the multi-dimensional heterogeneous data of a target farmland in real time, including the crop spectrum time series data of a satellite image, the ion concentration data collected by a soil sensor network, and the microenvironment time series monitored by a meteorological station; and constructing a Delaune triangulation network spatial index, and re-sampling satellite image data to be matched with a soil sensor network space. Calculating a time-varying mutual information entropy, extracting related microenvironment parameters as coupling factors, combining the coupling factors with soil basic parameters to form a three-dimensional feature matrix, and converting the three-dimensional feature matrix into a growth period feature vector fused with time-space correlation; the collaborative decision model is input, the main branch predicts the basic fertilization amount, the auxiliary branch detects the ion concentration space mutation area and corrects the fertilization amount, a fertilization control instruction is output, and the fertilization device is controlled to conduct precise fertilization. The problem that fertilization control cannot be accurately and efficiently performed on farmland in the prior art is effectively solved.

The invention provides a crop yield prediction method and system based on image analysis, relates to the field of yield prediction, and obtains a farmland multi-view remote sensing image through an unmanned aerial vehicle multispectral sensor, and executes aerial triangulation and ortho-rectification to obtain accurate exterior orientation elements and ortho-images. Then, vegetation indexes are extracted and combined with field yield measurement data, a yield regression model is constructed, and rapid prediction and grading of the crop yield are achieved. In order to reduce multi-view brightness inconsistency and give consideration to crop growth characteristics, prior information such as canopy row direction, leaf angle distribution and sealing degree is introduced, iterative correction is performed on the image by adopting a BRDF model, and radiation difference is corrected while geometric accuracy is guaranteed. Particularly, through staged trend analysis and multiple rounds of detection, priori conflicts can be found in time, incremental images can be flexibly collected, and resolving robustness and yield prediction precision are improved. The method can be widely applied to unmanned aerial vehicle remote sensing agricultural monitoring scenes.

The invention relates to the field of live-action three-dimensional modeling, in particular to a ground video stream and unmanned aerial vehicle multi-view image fusion modeling method, which comprises the following steps of: 1, acquiring an aerial image, arranging an image control point, and acquiring a spatial three-dimensional coordinate of the image control point; step 2, processing and modeling the aerial image; 3, extracting marked space points, and exporting space coordinate data; 4, acquiring video stream data, extracting a high-resolution image, and screening image data; 5, taking the spatial point coordinate information as a reference, carrying out aerial triangulation processing on the image model, and obtaining an aerial triangulation result under an absolute reference; step 6, according to the aerial triangulation result, combining the aerial triangulation result of the aerial image with the aerial triangulation result of the video streaming image; and 7, performing model reconstruction based on the combined aerial triangulation result, and mapping textures to obtain a final fused three-dimensional model result. The advantages of the aerial image of the unmanned aerial vehicle and the ground video stream are fused, and the multi-view image data of the target scene can be comprehensively acquired.

A navigation system and method are disclosed for operation in GPS-denied environments using quantum-inspired sensor fusion, dynamic virtual anchor points (VAPs), and predictive environmental modeling. The system represents multiple position hypothesis using wavefunction-like expansions and integrates VAP-based triangulation for drift correction. A predictive modeling module ingests solar, geomagnetic, and environmental data to proactively adjust sensor weighting. A cybersecurity module employs quantum-algebraic key generation and location-derived ephemeral keys to secure inter-device communication. The system includes an augmented reality (AR) interface to visualize and edit anchor references, and a neurofeedback module that adapts the AR interface based on real-time physiological signals from the user. The method further enables anchor optimization via AI-driven repositioning and supports low-power edge execution using approximate amplitude filtering. Additional modules may include fractal antennas, neuromorphic processors, and adaptive forecasting layers to maintain positional accuracy and user experience in subterranean, multi-floor, or magnetically complex environments.

The invention relates to the technical field of intelligent maintenance of tunnel electromechanical facilities, in particular to a tunnel lamp recognition and spatial positioning method based on binocular vision, which comprises the following steps: synchronously acquiring tunnel environment images through left and right cameras subjected to polar horizontal correction, inputting the images into a lightweight tunnel lamp detection model, and detecting an area image containing a lamp bounding box; extracting feature points in the bounding box by adopting a multi-scale adaptive FAST feature extraction algorithm, describing the feature points by rotating an invariant binary descriptor, and generating a disparity map in combination with an adaptive Hamming distance matching algorithm; calculating three-dimensional space coordinates of the lamp according to the disparity map by using a binocular camera triangulation principle and combining camera parameters; when the ambient illumination is lower than a threshold value, a multispectral image fusion module is started to enhance the feature matching robustness; and when continuous matching fails, visible light communication assisted positioning is started. According to the invention, the problem of low positioning precision of the lamp in a complex tunnel environment is solved, and the reliability and efficiency of identification and positioning are improved.

The invention discloses a hot-dip galvanized steel pipe ground cage verticality measuring system based on machine vision, and relates to the technical field of physical measurement, and the system comprises a projection unit which is used for projecting polarization gray scale stripes with polarization codes to a to-be-measured hot-dip galvanized steel pipe ground cage; the imaging unit comprises at least two cameras which form a triangulation base line with the projection unit so as to synchronously acquire multi-view images from different views; and the processing unit is configured to perform polarization difference fusion on the multi-view images to suppress mirror reflection, generate corresponding polarization difference fusion images, reconstruct pipe body three-dimensional point clouds of all the hot-dip galvanized steel pipes in the corresponding hot-dip galvanized steel pipe ground cages, generate pipe body axial vectors by adopting robust cylinder axis fitting, and obtain the pipe body three-dimensional point clouds of all the hot-dip galvanized steel pipes in the corresponding hot-dip galvanized steel pipe ground cages. And calculating the perpendicularity deviation angle of the pipe body according to the axial vector of the pipe body and the gravity direction. Therefore, on the premise that the measurement precision is guaranteed, the spatial postures of the multiple steel pipes are synchronously collected and recognized at a time, multi-point repeated scanning is not needed, and the detection efficiency is remarkably improved.

The invention is suitable for the technical field of radar signal processing, and provides a space target SFM three-dimensional reconstruction method based on a multi-frame ISAR image, and the method comprises the steps: carrying out the preprocessing and imaging of large-angle inverse synthetic aperture radar ISAR echo data of a space target, and obtaining a multi-frame ISAR image sequence; extracting scattering points in each frame of ISAR image sequence according to an orthogonal matching pursuit algorithm, and performing feature matching on adjacent frames of ISAR image sequences to obtain a scattering point set; obtaining instantaneous radar sight line information, and constructing a projection matrix from a space target scattering center three-dimensional coordinate to an ISAR two-dimensional pixel plane according to the instantaneous radar sight line information; and obtaining a three-dimensional reconstruction result according to the scattering point set and the projection matrix in combination with a triangulation principle in an incremental motion recovery structure SFM method. According to the method, the robustness and precision of three-dimensional reconstruction can be improved.

The invention discloses a space-ground-air integrated multi-source data acquisition and fusion method based on highway facilities, and relates to the field of data acquisition and fusion, and the method comprises the steps: obtaining the three-dimensional geographic information data of the highway facilities based on a diversified data acquisition technology, and carrying out the fusion processing of the three-dimensional geographic information data through employing a data fusion registration algorithm, outputting multi-source data information; verifying the fusion precision of the multi-source data information by using the elite centroid and a reverse learning strategy, and performing secondary fusion processing of the three-dimensional geographic information data according to a verification result to obtain a multi-source data fusion result; and performing triangulation network reconstruction processing on the multi-source data fusion result based on a triangulation algorithm, and performing visual display on the reconstruction result, the multi-source data fusion result and the three-dimensional geographic information data by using a three-dimensional digital display technology. According to the invention, multi-source point cloud processing software is used to carry out precision check and other operations on the fused point cloud, so that the integrity and smoothness of the point cloud data are ensured.

The invention discloses a multi-target tracking method, system and device, a medium and a product, and relates to the technical field of computer graphic vision, and the method comprises the steps: based on a plurality of single-view two-dimensional images, employing a lightweight key point detection model to extract human body two-dimensional key point coordinates under different views; based on the camera parameters corresponding to different visual angles and the human body two-dimensional key point coordinates under different visual angles, performing three-dimensional reconstruction on the human body key points by using a triangulation method to obtain a three-dimensional reconstruction model; projecting the three-dimensional reconstruction model, and determining a projection frame corresponding to each target in the tracked area; and a tracking algorithm and Kalman filtering are combined to complete real-time tracking of a plurality of targets in the tracked area. According to the method, the three-dimensional target tracking problem is reduced into the two-dimensional target tracking problem based on the projection frame of the three-dimensional reconstruction model relative to the horizontal plane, the accuracy and robustness of the model can be improved in a multi-target tracking scene, and meanwhile, the calculation amount is reduced.

The invention relates to the field of metal waste detection, in particular to a metal waste detection and identification method and system. The method comprises the following steps: applying high-frequency ultrasonic waves to a waste area to be detected, and collecting high-frequency ultrasonic wave response signals; performing metal vibration mode parameter mining on the high-frequency ultrasonic response signal to obtain metal vibration mode characteristics in the region; performing ultrasonic excitation path identification on the high-frequency ultrasonic response signal, performing ultrasonic spatial propagation modeling, and constructing a spatial ultrasonic trajectory propagation model; carrying out sound beam positioning detection on the high-frequency ultrasonic response signal based on the metal vibration modal characteristics in the region, and carrying out propagation error compensation calculation to obtain a response beam propagation error compensation value; and performing ultrasonic triangulation positioning measurement on the space ultrasonic trajectory propagation model to obtain accurate three-dimensional coordinates of the metal wastes. According to the invention, efficient and accurate detection, identification and positioning of the metal wastes are realized.