1035 results about "Colour image" patented technology

The invention relates to the technical field of automatic spraying, and particularly discloses a spraying system based on multi-modal vision and artificial intelligence self-correction, comprising a multi-modal vision acquisition module for acquiring color image information, depth information and infrared feature information; the control module is used for generating a spraying sensing model according to the information of the multi-modal visual acquisition module; the control module carries out spraying area identification, track planning and spraying parameter decision making based on the spraying sensing model; the spraying execution module is used for spraying; and the feedback self-correction module iterates the spraying parameter decision in the control module based on a reinforcement learning algorithm according to the difference between the actual coating state information and the expected state. The control method based on multi-modal vision and artificial intelligence self-correction is applied to a spraying system based on multi-modal vision and artificial intelligence self-correction. The scheme is used for solving the problems that the spraying quality of complex workpieces is not high due to the single sensing dimension of an existing spraying system, and the production flexibility is poor due to the rigid control mode.

The invention discloses an electronic cabin assembly quality detection method based on three-dimensional vision. The method comprises the following steps: firstly, generating data for comparison during detection for a standard electronic cabin; shooting all to-be-detected objects in the to-be-detected electronic cabin to obtain a depth image and a color image of each to-be-detected object; according to the depth image and the color image of the to-be-detected object, combining with the data of the standard electronic cabin for comparison during detection; and whether screws of the electronic cabin to be detected are neglected and not installed in place, whether cable plugs are neglected and not installed in place, whether connectors and cable plugs in the wiring unit are wrongly matched, and whether the bending radius of cables is too small are detected in sequence. Through fusion of depth information, three-dimensional quantitative detection of the assembly quality is realized, and the detection precision, reliability and automation level are significantly improved.

The invention discloses an uncertainty mapping method and device based on selective learnable depreciation, and the method comprises the steps: S1, collecting color images and depth maps at different time points, and projecting the color images and the depth maps to a three-dimensional coordinate system; s2, predicting an evidence vector for each pixel by using an evidence generation network; s3, calculating total evidence intensity based on the evidence vector, and defining credibility and uncertainty based on the total evidence intensity; s4, constructing a noise mask to represent the observation quality, and predicting a damage coefficient by using a selective damage network; s5, the evidence intensity is adjusted in a zooming and depreciation mode; and S6, outputting a final probability, carrying out basic probability distribution of multi-frame fusion observation on the three-dimensional voxel grid, and constructing a semantic map containing uncertainty estimation. According to the method, the conflict rate and uncertainty in the semantic map fusion process can be effectively reduced, and the accuracy and credibility of the map are remarkably improved.

The invention discloses a LiDAR-IMU-camera tight coupling positioning and mapping method and device for a mobile platform, and belongs to the technical field of robot SLAM. Synchronously triggering the color camera, the laser radar and the IMU through hardware, and unifying timestamps; performing compensation and distortion removal on the laser point cloud motion by the IMU data; based on curvature, intensity and density self-adaptive downsampling, local plane fitting errors are used for distributing observation weights; the IMU pre-integration pose is used as an initial value, point-to-surface registration of the weighted point cloud and the local map is carried out, and a laser-IMU tight coupling odometer factor is obtained; the color image and the laser intensity graph are fused into a multi-mode loopback descriptor, and loopback factors are generated through global retrieval and geometric verification; and inputting an odometer, an IMU, a laser loopback factor and a visual loopback factor into an increment factor graph optimizer, jointly solving a global optimal key frame pose, and outputting a dense laser point cloud map and a color visual point cloud map. The method can be operated in real time on an embedded platform, effectively inhibits drifting, and realizes centimeter-level global consistent positioning and mapping.

The invention discloses an indoor scene three-dimensional reconstruction method based on deep fusion and confidence modeling, and belongs to the technical field of computer vision. According to the method, composite data frames such as a color image, a depth map, an IMU (Inertial Measurement Unit) and a camera attitude are comprehensively utilized to carry out regional three-dimensional reconstruction on an indoor scene: firstly, the scene is divided into a smooth region (such as a wall, a ground, a ceiling, a glass plane, a mirror surface, a blackboard and other planes) and a complex curved surface region; aiming at the smooth area, adopting geometric prior guide plane fitting provided by a visual large model, and combining sensor attitude information to quickly reconstruct a regular plane model; for a complex curved surface area, a multi-frame point cloud fusion strategy is adopted to accumulate different view angle information, and a deep residual error refining network is utilized to recover curved surface details, so that a high-precision curved surface model is obtained. According to the method, the three-dimensional structure of the indoor scene can be efficiently reconstructed, the global framework of the smooth area is reserved, and the details of the surface of a complex object are depicted in detail.

The invention discloses an object three-dimensional reconstruction method, device and system based on deep learning. The reconstruction method comprises the following steps: acquiring a multi-view color image of an object through a controllable image acquisition device; reconstructing a sparse three-dimensional point cloud by using a motion recovery structure method and obtaining a camera pose; initializing parameters of the three-dimensional Gaussian sputtering model based on the sparse point cloud and performing training optimization; a target object semantic segmentation data set is constructed, and a low-rank adaptive technology is adopted to finely segment all models; generating prompts through an open vocabulary detection model at each view angle, obtaining an accurate segmentation mask, and optimizing a three-dimensional segmentation weight by adopting a joint loss function fusing color consistency loss and edge perception loss; and finally outputting the color three-dimensional point cloud of the target object. According to the method, the original image is segmented, so that the influence of the quality of the rendered image is avoided; the segmentation precision of the model in a specific scene is improved through field adaptive fine tuning; and the accuracy of the segmentation boundary is ensured by adopting a double-loss joint optimization mechanism.

The invention provides a steel plate surface defect detection method and system based on multi-source data fusion, and relates to the technical field of steel production automatic detection.The method comprises the steps that water stain pretreatment is conducted on the surface of a steel plate, the residual humidity is reduced to be below a preset low humidity threshold value, and a dry surface is obtained; based on the dry surface, collecting multi-source data through a 3D laser line scanning camera and a 2D industrial line scanning camera which are synchronously triggered, obtaining line scanning laser point cloud data, a line scanning reflectivity gray level image, a line scanning depth gray level image and an area array reflectivity color image, and establishing a space corresponding relation among the multi-source data; and forming a multi-source data set based on the spatial correspondence among the multi-source data. According to the method, a high-precision and high-robustness steel plate surface defect detection system covering the whole detection process is constructed by eliminating water stain interference, unifying a multi-source data reference, optimizing data quality, accurately identifying defects and realizing automatic feedback and data closed loop.

The invention discloses an intelligent robot navigation system with a body based on a multi-modal large model, and the system comprises an RGB-D camera module which captures a color image and a depth image of an environment in real time, and carries out the preprocessing of the color image and the depth image, and outputs the preprocessed image; the laser radar SLAM module provides a basis for subsequent coordinate calculation and navigation path planning; the multi-modal VLM module is used for calculating the three-dimensional coordinates of the target location in the map; and the navigation control module controls the robot to move. The beneficial effects of the invention lie in that the system realizes efficient environmental perception and natural language understanding through multi-modal fusion, and can accurately identify a target location, calculate a three-dimensional coordinate and plan a safe path, thereby improving the autonomous navigation precision and reliability of the robot, and being suitable for intelligent movement control in a complex scene.

The invention relates to the technical field of computer vision, and discloses a jade defect intelligent detection method and system based on machine vision and deep learning, and the method comprises the following steps: S1, based on a high-resolution industrial camera and a laser three-dimensional scanner, adopting a multi-mode synchronous collection strategy, and rotating a jade sample through a precise motion control system, a jade surface high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are respectively obtained, and a jade multi-mode original data set is generated. A high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are integrated through a multi-modal synchronous acquisition strategy, and multi-dimensional feature expression under unified coordinates is constructed, so that the limitation of a single data source is effectively overcome; an image registration algorithm and a feature pyramid network are combined with a point cloud network to perform multi-modal feature fusion, complementarity of color texture and geometric morphology information is enhanced, and image quality is optimized based on adaptive histogram equalization and non-local mean filtering.

The invention discloses an SAR image orientation sensitive target detection method based on sub-aperture image saturation characteristics, and belongs to the technical field of synthetic aperture radar image target detection. The azimuth sensitive target detection is realized through the following steps: 1) sub-aperture image generation: dividing an azimuth frequency spectrum into a plurality of sub-bands, and generating a plurality of sub-aperture images through inverse Fourier transform; 2) color synthesis and feature extraction: allocating different hues to each sub-aperture image by using an HSV color space, synthesizing an RGB color image, converting the RGB color image to the HSV color space, and extracting a saturation channel in the RGB color space as an azimuth sensitivity feature map; and 3) target detection: carrying out threshold segmentation on the saturation feature map, and identifying a pixel region with a high saturation value, namely, an orientation sensitive target. According to the method, the color saturation change caused by the scattering difference of the target in different sub-apertures is utilized, effective detection of azimuth sensitive targets such as artificial buildings and vehicles is achieved, and the method has the advantages of being simple in calculation and high in robustness.

The invention relates to the technical field of image processing, and discloses a pitaya fruit pose estimation method based on point cloud three-dimensional reconstruction. The pitaya fruit pose estimation method comprises the steps that RGB color images and depth images of pitaya fruits are collected, expanded and marked; the DeepLabV3 + network model is improved and trained, and then an RGB color image is segmented; segmenting the depth image by using the semantic segmentation mask image, then matching the depth image with the RGB color image to obtain a pitaya tree point cloud, and preprocessing the pitaya tree point cloud; registering the pitaya fruit tree point cloud by using an improved point cloud registration algorithm to obtain a fruit tree three-dimensional point cloud model, and segmenting fruit local point clouds from the fruit tree three-dimensional point cloud model; establishing a pitaya fruit point cloud coordinate system by using a PCA principal component analysis method; and performing spherical fitting and ellipsoid fitting on the pitaya fruit point cloud coordinate system to obtain an ellipsoid model with unique pose parameters. According to the invention, a fruit tree three-dimensional point cloud model and accurate fruit three-dimensional space pose information can be provided for a pitaya fruit picking system.

The invention provides an array image demosaicing method based on dynamic convolution and adaptive coding, which relates to the technical field of image processing, and comprises the following steps: acquiring single-channel original image data and a corresponding color filtering array arrangement type identifier; converting the arrangement type identifier into a multi-dimensional physical feature vector, and inputting the multi-dimensional physical feature vector into a feature processor of a neural network model to generate a weighting coefficient vector; carrying out weighted combination on the plurality of special arrangement transformation matrixes through a weighting coefficient vector to obtain a transformation component, adding the transformation component and a basic convolution kernel parameter to obtain a dynamic convolution kernel parameter, and carrying out directional modulation on a specific spatial position of the dynamic convolution kernel parameter based on a direction weight component in a multi-dimensional physical feature vector; and performing convolution operation on the original image data by using the dynamic convolution kernel parameters, extracting multi-scale features, reconstructing image features, and outputting multi-channel color image data, so that the method can be adaptive to different color filter array types, and the demosaicing precision and generalization capability are improved.

The invention discloses an electronic component packaging defect detection method and system based on image acquisition, and relates to the field of image analysis, and the method comprises the steps: collecting a multi-mode image of a to-be-detected electronic component package; pixel alignment is carried out on the multi-modal image, the multi-modal image after pixel alignment is used as an R channel, a G channel and a B channel to be stacked, and a three-channel pseudo-color image is generated; inputting the three-channel pseudo-color image into a pre-trained convolutional auto-encoder model to obtain a reconstructed three-channel image; analyzing the difference between the three-channel pseudo-color image and the reconstructed three-channel image to obtain an analysis result, and generating a three-channel residual image according to the analysis result; generating a single-channel defect saliency map based on the three-channel residual image; and when a connected region of which the pixel value exceeds a preset pixel threshold value exists in the single-channel defect saliency map, judging that the connected region is a real defect. The detection false alarm rate can be effectively reduced, and the production efficiency is improved.

The invention discloses a navel orange grabbing pose estimation method based on multi-feature segmentation and a visual hedgehog algorithm, and relates to the technical field of computer vision, and the navel orange grabbing pose estimation method comprises the steps: collecting a color image and a depth image of a target navel orange, carrying out the spatial registration, and generating a three-dimensional point cloud; performing sphere fitting on the independent navel orange instance point cloud, outputting sphere center coordinates and radius parameters of each navel orange, and taking the sphere center coordinates and radius parameters of each navel orange as geometric data of the navel oranges; and collision detection and visibility analysis are conducted on the grabbing pose candidate set, collision cost and visibility cost are generated, comprehensive optimization is conducted on the collision cost and the visibility cost through a multi-target optimization function, the optimal grabbing pose is generated, and the mechanical arm is driven to execute grabbing operation based on the optimal grabbing pose. According to the method, the multi-feature segmentation and the visual hedgehog algorithm are combined, so that accurate calculation of the navel orange grabbing pose is realized.

The invention relates to the technical field of industrial automation, in particular to an industrial robot posture recognition method. Aiming at the problems of weak workpiece texture, strong surface reflection, serious stacking and shielding and the like in an industrial production line, a pixel-level dense feature fusion and self-attention mechanism is introduced, and semantic texture information of a color image and spatial geometric information of a depth image are integrated in a feature extraction stage; the problem that the posture of a rotationally symmetrical workpiece is fuzzy is solved through an asymmetric loss function, meanwhile, a comprehensive grabbing scoring model containing force sealing stability, environment collision risk probability, mechanical arm kinematics reachability and visual uncertainty is further established, and through the mode of combining off-line grabbing candidate construction and on-line real-time evaluation, the grabbing accuracy of the mechanical arm is improved. And the globally optimal grabbing pose is screened out. According to the method, the recognition precision and robustness of the industrial robot in the unstructured environment are remarkably improved, and the success rate and safety of grabbing operation are improved.

The invention relates to the technical field of measurement and detection, in particular to a test sieve calibration method based on machine vision. Comprising the following steps: acquiring a test screen image by a microscope, firstly performing graying processing on an original image, and converting a color image into a grayscale image; the method comprises the following steps: carrying out binarization processing on a grey-scale image, carrying out binarization on the image, setting a grey-scale value of a pixel point on the image to be 0 or 255, enabling the whole image to present an obvious visual effect which is only black and white, and better analyzing the shape and the contour of an object through binarization; and performing connected domain analysis on the binary image, finding a pixel point to which each sieve hole belongs, endowing each pixel point with a label through the connected domain analysis, and forming a connected domain by the pixel points with the same label value so as to realize segmentation of the region of interest. The method is applied to the measurement calibration work of the test sieve, the working efficiency of verification and calibration personnel can be greatly improved by using the method, and human resources are saved.

Disclosed in the present invention is a road-scene depth completion method based on the guidance of semantic information and a color image. The method comprises: designing a dual-branch network consisting of a color-image-guided branch and a semantic-guided branch, and introducing semantic information into the network, so as to perform road-scene depth completion. The idea of multi-task learning is used, and a backbone is shared with a depth map prediction layer in the color-image-guided branch, such that the semantic information can be obtained simply by adding a semantic segmentation layer, without the need to add an entire network. Moreover, semantic labels generated by the semantic segmentation layer are fed into the semantic-guided branch as input, such that parameters of the layer can also be adaptively adjusted during network training. By integrating high-precision semantic information and an RGB image, the present invention completes a sparse depth map provided by LiDAR, thereby improving the accuracy and real-time performance of scene understanding in applications such as digital twin, virtual reality, digital infrastructure and intelligent transportation.

The invention relates to the technical field of artificial intelligence, and discloses a disordered workpiece grabbing method based on three-dimensional visual perception and intelligent decision, and the method comprises the steps: collecting the color image data and depth image data of a scene, and generating the three-dimensional point cloud data of the scene based on the internal reference of a camera; performing target detection and instance segmentation processing based on the color image data to generate target segmentation mask data, calculating an outlier degree score and a top degree score of a target in combination with depth data, and selecting an optimal capture target according to a comprehensive score; through an intelligent target selection strategy based on YOLOv11-SAMV3 fusion, in combination with an outlier degree score and a top degree score, a grabbing target is comprehensively evaluated, an isolated workpiece located at the top of a stack is preferentially selected, the shielding and collision risks are reduced, intelligent and automatic grabbing target selection is guaranteed, and the grabbing efficiency and the system adaptability are improved.

The invention relates to a color image restoration method, terminal equipment and a storage medium. The method comprises the following steps: modeling a to-be-restored image into a three-order observation tensor; performing TR decomposition on the observation tensor to obtain a core tensor; on the basis of the core tensor, the to-be-restored image modeling is optimized through an optimization model, and a target tensor is obtained; and obtaining an image restoration result of the to-be-restored image based on the target tensor, the optimization model being used for calculating the target tensor so that the target tensor satisfies a low-rank constraint term of model expansion of a minimized TR factor, a fitting error of an approximate tensor and a data driving prior realized based on FFDNet. The image restoration effect of the optimization model provided by the invention is superior to that of an existing image completion model.

The invention discloses a pipeline defect detection method and device, a storage medium and computer equipment, and relates to the technical field of defect identification. The method comprises the steps of collecting a magnetic flux leakage signal of a pipeline, performing data structuring processing on the magnetic flux leakage signal to obtain an original magnetic flux leakage signal vector, and converting the original magnetic flux leakage signal vector into a two-dimensional gray matrix; based on a pseudo-color mapping function, mapping the two-dimensional gray matrix into a plurality of pseudo-color images, the image sizes of the pseudo-color images being different; and inputting the plurality of pseudo-color images into a pre-trained defect identification model to obtain defect features of the defect identification model based on the pseudo-color images of different sizes, and outputting a detection result of the pipeline. According to the scheme, the defect detection precision of the pipeline can be improved.

The invention discloses an underground engineering structure total space deformation analysis method based on a multi-depth camera array, and relates to the field of space deformation, and the method comprises the steps: employing the multi-depth camera array to collect a depth image and a color image of an underground engineering structure, and generating an alignment depth image of each depth camera; performing internal reference calibration on the depth camera, and converting an aligned depth image of the depth camera into three-dimensional color point cloud data of the depth camera; performing external parameter calibration on each depth camera to obtain an optimized transformation matrix from each slave camera to the master camera; transforming the three-dimensional color point cloud data of each subordinate camera to a coordinate system of the main camera by adopting an optimized transformation matrix to obtain a three-dimensional model of the underground engineering structure; and determining corresponding point pairs in the three-dimensional model obtained in different periods, and calculating the Euclidean distance between the corresponding point pairs to realize the full-space deformation analysis of the underground engineering structure. According to the invention, low-cost and high-precision underground engineering deformation monitoring is realized.

The invention discloses a young poultry counting method based on deep learning, and relates to the field of image processing, and the method comprises the steps: information collection and feature extraction: collecting a young poultry image through a hardware platform, and carrying out the preprocessing of the image, and obtaining a continuous color image of a target image; data annotation: carrying out label annotation on the preprocessed image, carrying out accurate target detection annotation on stacked young poultry in the image to obtain an annotated data set, and providing a high-quality data set for model training; inputting the collected real-time image into a young poultry detection system constructed based on a YOLO architecture, wherein the system is used for performing target detection, target tracking and target counting on the input image; and multi-target tracking: continuously positioning spatial positions of a plurality of targets through frame-by-frame analysis of the video sequence, and maintaining a unique identity (ID) of each target. Precise counting and high-speed real-time processing of the young poultry in a high-density scene are realized.

The invention relates to the technical field of sanitation, and discloses a hand cleaning process evaluation method, device and equipment and a storage medium, the method comprises the following steps: obtaining target information collected by a multi-modal sensor, the multi-modal sensor comprises at least two of a color image sensor, a depth sensor, a sound sensor and an infrared image sensor, the target information correspondingly comprises color image information, depth information, sound information and infrared image information; the target information is information acquired by the multi-mode sensor in the hand cleaning process of a target object; performing hand cleaning process evaluation based on the target information by using a deep learning model to obtain a score value corresponding to each evaluation index; wherein the evaluation indexes comprise an action sequence score, an action amplitude score, an angle score and / or a posture score. According to the method, the accuracy of evaluating the hand cleaning process is improved by integrating the multi-modal data.

The invention relates to the technical field of agricultural intelligent detection, in particular to a blueberry tree pest and disease damage detection method based on machine vision. The method comprises the following steps: acquiring an ultraviolet gray image and a visible light color image of the same blueberry target, extracting a local gray extreme point of a fruit powder layer in the ultraviolet image as an anchoring node, and constructing a cluster manifold topology network for describing fruit space distribution; and taking the topological network as a deformation control skeleton, and carrying out pixel-level non-rigid registration on the visible light image by utilizing a thin-plate spline interpolation function. And then, performing weighted difference operation on the registered image and the ultraviolet image to generate a spectral residual saliency map which inhibits a healthy fruit powder background and highlights an abnormal region, and extracting a pest and disease damage target through threshold segmentation. According to the method, the problem of cross-modal registration of dense small fruit clusters in a dynamic wind blowing environment is effectively solved, fruit powder interference is accurately eliminated by utilizing spectral physical characteristics, and the detection robustness is remarkably improved.

The invention provides a natural image matting method and system based on text and boundary information aggregation, and relates to the technical field of image processing. Splicing the original color image and the corresponding ternary image, extracting initial features, and obtaining enhanced fusion features through multi-scale Laplacian high-frequency extraction and cosine similarity weighted fusion; based on the enhanced fusion feature and the ternary image, generating a gated ternary fusion feature fused with priori knowledge through a trans-attention mechanism; global coding modeling is carried out on the gated three-value fusion features to obtain deep features; text prompt and multi-scale boundary information are introduced based on deep features, adaptive up-sampling is guided through a cross-attention mechanism, semantic difference consistency constraint is adopted between decoding layers, consistency constraint is implemented from pixel appearance, high-level semantics and color component dimensions, and finally a transparency image is output through a prediction header to obtain an image matting result. And the fidelity and the boundary accuracy of high-frequency details in a matting result are effectively improved.

The invention relates to a building block pavement disease detection method and equipment based on an RGB-D image, and the method comprises the steps: collecting a pavement color image and a depth image, inputting a pre-constructed semantic segmentation model, and obtaining a segmentation result of a building block and a thin and narrow seam; based on the depth image, mapping a segmentation result of the building block and the thin and narrow seam to a three-dimensional point cloud space; performing plane fitting on the point cloud data by adopting a random sampling consistency algorithm, extracting a point cloud contour, and calculating horizontal displacement between adjacent building blocks; respectively fitting the point clouds of the adjacent building blocks as independent planes by adopting a random sampling consistency algorithm, and calculating the vertical displacement between the adjacent building blocks; and based on the horizontal displacement and the vertical displacement between the adjacent building blocks, generating quantitative data of horizontal displacement and vertical displacement of the building block pavement of the target road section, marking disease positions exceeding a preset limit value, and completing disease detection of the building block pavement. Compared with the prior art, the method realizes accurate quantification of horizontal and vertical displacement between the building blocks of the building block pavement.

An inkjet printing apparatus comprises: a first printing unit that prints a specific color image by applying a specific color ink having a specific color on a printing medium; a second printing unit that prints a color image by applying a color ink onto the specific color image printed on the printing medium; and a control unit that controls an application amount of the specific color ink by the first printing unit. The control unit determines an application amount per unit area of the specific color ink in accordance with a viewing condition in a case where an observer views a printing medium on which the color image is printed.

The invention relates to the technical field of vehicle safety, and discloses an integrated motorcycle auxiliary driving system and method. The method comprises the following steps: synchronously acquiring vehicle kinematics parameters and environment three-dimensional point cloud data through a multi-source sensing system of a motorcycle; executing a dynamic risk mapping operation by using the collected data to generate a risk probability distribution diagram; acquiring a color image and a depth image of a road scene through a stereoscopic vision camera, inputting the color image and the depth image into the multi-scale feature extraction network for analysis in combination with the risk probability distribution map, and outputting a comprehensive risk score; performing obstacle trajectory prediction according to the comprehensive risk score to obtain an obstacle prediction trajectory, scanning a target area by using an infrared sensor, processing point cloud data by using a point cloud segmentation algorithm based on the prediction trajectory, and extracting actual obstacle attributes; and inputting the comprehensive risk score and the actual obstacle attribute into a fuzzy logic controller for data fusion, and finally generating an integrated motorcycle aided driving control instruction.

The invention discloses an AR scene rapid deployment method based on a semantic map, and relates to the technical field of AR scene deployment, and the method comprises the steps: S1, semantic map construction: collecting three-dimensional point cloud data, color image data and depth information of a real scene through a laser radar and an RGB-D camera, inputting a deep semantic segmentation network to identify semantic categories of objects and areas in the scene; the scene is monitored and analyzed in real time through the deep semantic segmentation network, dynamic changes in the scene can be captured in time, the space labeling strategy is automatically adjusted according to the semantic understanding result, it is ensured that AR virtual information is always matched with the real environment, the accuracy of the functions such as AR navigation is ensured, and by recognizing semantic requirements of different AR applications, the real-time performance of the system is improved. According to the technical scheme, the annotation resources and key points are intelligently allocated according to the requirements, the adaptive space annotation scheme is generated, the pertinence and efficiency of AR scene deployment are remarkably improved, and the AR scene can quickly respond to the application requirements of different fields.

The invention discloses a cement-based material phase identification and quantitative analysis method combining artificial intelligence and expert knowledge. The method comprises the following steps: acquiring a BSE image, a qualitative element surface spectrum and a quantitative element surface spectrum of a cement-based cementing material sample; synthesizing a color EDS image based on the qualitative element surface spectrum, and performing superpixel division on the image; based on the quantitative element surface spectrum, extracting an average value of element relative contents of all pixels in each superpixel range as an element feature; performing two-dimensional visualization processing on all the element features by adopting a PHATE dimension reduction algorithm to generate derivative element features; performing manual correction and phase label labeling on a clustering result by utilizing a Glue multi-view interaction platform in combination with expert priori knowledge; and fusing the corrected clustering result with the superpixel division result, and regenerating a new phase classification mask with a phase identifier. According to the method, intelligent identification and high-precision analysis of the cement-based material multiphase system can be realized, and the accuracy, reliability and interpretability of phase classification are improved.