173 results about "Visual space" patented technology

The invention provides a zero sample anomaly detection method and device and electronic equipment, and relates to the technical field of image anomaly detection.The method comprises the steps that a to-be-detected image is acquired, and visual features are extracted through a CLIP model; performing visual enhancement on the visual features to obtain enhanced visual features; injecting the enhanced visual features into a learnable text prompt template to generate an adaptive text prompt; injecting the adaptive text prompt into a text encoder for encoding to obtain a text embedding representation; mapping the adaptive text prompt to a visual space to obtain a visual prompt, and inputting the visual prompt into the local visual features to obtain scale visual features; and based on the text embedding representation and the scale visual features, separately calculating an anomaly score and an anomaly positioning map of a preset scale, and fusing to obtain a detection result. According to the zero sample anomaly detection method and device and the electronic equipment provided by the invention, the generalization ability of the whole detection process is effectively improved, and the actual application requirements are further met.

The invention discloses a human motion posture recognition method and system based on multi-modal data fusion, and relates to the technical field of posture recognition, and the method comprises the steps: firstly, synchronously obtaining a human motion posture video frame and an IMU segment, then carrying out the feature extraction of the two kinds of heterogeneous data in a shunt parallel mode, and carrying out the feature extraction of the two kinds of heterogeneous data; and respectively capturing visual space attitude information and dynamic inertial characteristics of the IMU. Afterwards, specific features of the modals are uniformly expressed through a mixed Token and embedding mechanism, and a space-inertia collaborative attention fusion mechanism is further introduced to realize dynamic association and deep fusion of cross-modal information; and finally, classifying the multi-modal fusion representation vector obtained by fusion so as to realize accurate recognition of the human motion posture. In this way, the defect that traditional fusion is insufficient in capturing subtle action differences can be overcome, and the accuracy and stability of action recognition in a complex scene are improved.

The invention relates to the technical field of artificial intelligence, and particularly discloses a visual space description method, system and product based on depth information fusion, and the method comprises the steps: obtaining an image and a corresponding object text; performing vision-plane position feature extraction on the image to obtain a vision feature and a plane position feature, and performing depth feature extraction on the image to obtain a depth feature; performing text coding on the object text to obtain text features; performing feature fusion on the visual features, the plane position features, the depth features and the text features to obtain fusion features; performing spatial relationship classification on the fusion features to obtain a spatial relationship; and inputting the instruction language, the spatial relationship and the object text into a large language model to obtain a natural statement describing the spatial relationship of the object. According to the method, the spatial relationship between the objects in the image can be accurately described, the spatial perception capability of artificial intelligence is improved, and meanwhile, the increase of redundant manual annotation workload is avoided.

The invention belongs to the technical field of electrical equipment detection, and discloses a defect image enhancement method fusing reasoning and generation, which integrates visible light, infrared and laser radar data through a multi-modal feature fusion network, breaks through the limitation that a contrast file CN114281093A only depends on a visible light image, and improves the detection accuracy. The dynamic attention mechanism can flexibly deploy visual, spatial and semantic feature weights according to defect types, key features can still be captured in complex environments such as strong light and shielding, and meanwhile, the spatial form of the defects is analyzed by means of three-dimensional point cloud; by means of the design, missing detection caused by insufficient characteristics of tiny parts such as hardware fittings and pins is effectively avoided. Aiming at the problem of distortion of a sample generated by a traditional data enhancement method in a comparison file, the sample quality is guaranteed through double mechanisms of reasoning constraint and physical verification, defect features output by a reasoning model directly constrain feature distribution of the generated sample, and meanwhile, a material mechanics rule is introduced to verify the physical rationality of the generated sample.

The invention provides a robot control method and device, control equipment and a storage medium, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining an input task command for a target robot, and obtaining a static scene image of a task scene collected by the target robot based on the task command; according to the static scene image and the task command, adopting a preset visual space reasoning model to generate a task action sequence corresponding to the task command and object space state information corresponding to each action in the task action sequence; according to the task action sequence and the object space state information corresponding to each action, adopting a preset task generator to generate an action sequence instruction corresponding to the task command; and according to each action instruction in the action sequence instruction, sequentially controlling the target robot to execute corresponding actions. The control efficiency and the control accuracy of the robot are improved, so that the control requirement in a complex scene is met.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention relates to a vision-semantic collaborative perception method and system for an automatic driving vehicle. Comprising a visual-semantic feature coding module and a visual-semantic feature fusion module. The visual-semantic feature encoding module comprises a text semantic encoder and a visual space-time encoder; a visual space-time encoder extracts single-frame semantic and structural features based on a 2D visual encoding model, and a 3D visual encoding model is adopted for modeling to form 3D frame-level fusion visual perception features; and the visual-semantic feature fusion module comprises a time converter, a cross converter and a context enrichment converter, and is used for adaptively adjusting a fusion weight based on a dynamic weight fusion strategy to generate required visual-semantic feature fusion information. According to the invention, the multi-modal weight is automatically adjusted, the environment perception error and decision delay are reduced, the adaptability and response precision of the automatic driving vehicle to the trunk line logistics complex traffic scene are improved, and the safe and stable operation of the intelligent driving system in the complex road environment is ensured.

The invention discloses a character interaction detection method and system based on large language model cross-modal fusion. The method comprises the following steps: 1) extracting image features; 2) multi-task feature extraction; 3) interaction behavior feature enhancement; 4) generating a large language model text; and 5) performing cross-modal fusion. According to the method, multiple groups of features are extracted from the image based on query to adapt to feature attention of different tasks, and feature representation of interaction behaviors is improved through multi-branch feature fusion and interaction behavior feature enhancement, so that the accuracy and reliability of character interaction detection are improved; and meanwhile, rich feature description texts are obtained by utilizing the powerful language expression capability of a large language model, and a good expression effect of character interaction detection under the condition of few samples is realized by combining cross-modal fusion training and aligning a good text-visual space.

The embodiment of the invention provides a visual space science experiment data analysis assembly line method. The technical problem that space science experiment data analysis is poor in convenience can be solved. The method comprises the following steps: in response to input data selection operation, algorithm model selection operation and visual component selection operation, generating data nodes, model nodes and visual nodes; performing parameter configuration on the data nodes, the model nodes and the visual nodes, and performing assembly line connection operation to generate a flow chart; based on the flow chart, receiving an input job submission operation; analyzing the flow chart to obtain an analysis result; obtaining a job scheduling result based on the analysis result; based on the job scheduling result, an algorithm mirror image deployment package corresponding to the algorithm model selection operation and file data corresponding to the data selection operation are called; performing algorithm analysis on the file data according to the algorithm mirror image deployment package to obtain an analysis result; and performing visual display. And multi-process, interactive and quantitative data analysis is realized.

The invention relates to a multi-mode human body activity identification method based on WIFI and videos, and the method comprises the steps: S1A, processing WIFI data, and obtaining WIFI space features; s1B, extracting human skeleton information in the video image to obtain visual features; s2A, performing spatial processing on the visual spatial features and the WIFI spatial features to obtain fused spatial features; s2B, performing time processing on the visual spatial features and the WIFI spatial features to obtain fusion time features; and S3, analyzing the fusion space feature and the fusion time feature to obtain a human body activity type. The multi-mode human body activity identification method based on the WIFI and the video has the advantages of being real-time and low in power consumption.

The invention relates to the technical field of medical data processing, and discloses a cognitive impairment auxiliary evaluation method, system, device and medium, evaluation is carried out on subjects in the aspects of instantaneous memory, dementia screening and delayed memory, the instantaneous memory comprises instant retelling and the like, the dementia screening comprises time calculation, reading, mathematical calculation, action simulation and the like, and the dementia screening and delayed memory evaluation can be carried out on the subjects. The delayed memory comprises delayed memory repeating of unprompted clues, delayed memory repeating of prompted clues and the like, the overall evaluation process covers memory, execution, visual space, language, calculation, concept reasoning cognitive domain and the like, Chinese language features are integrated, doctors are effectively assisted in comprehensive and accurate cognitive impairment evaluation on subjects, and the evaluation efficiency is improved. And the working efficiency of medical staff is greatly improved.

The invention discloses a mathematical formula identification coding method, and particularly relates to the technical field of formula coding. The method comprises the following steps: acquiring mathematical formula image data to be identified, and performing symbol boundary extraction and preprocessing to generate symbol feature expression data; and performing visual spatial layout analysis and symbol type semantic classification based on the symbol feature expression data to generate formula layout structure data and symbol semantic classification data. And through graph structure analysis based on a topological relation, determining an inter-symbol topological relation of the mathematical formula, and generating symbol topological relation data. And deriving a dimension constraint relationship and an operator dependency relationship between symbols through a mathematical meta-knowledge mining technology, and generating mathematical meta-knowledge constraint data. Initial mathematical formula structure expression data is generated through decoding of structure and semantic constraint fusion, and a formula coding sequence is generated through formula consistency verification and semantic constraint reconstruction. According to the invention, the accuracy and efficiency of mathematical formula identification can be effectively improved.

The invention discloses an SAM-based mirror surface detection method, which constructs a MirrorSAM based on segmentation of all models and is specially used for mirror surface detection. Specifically, due to the fact that reflections generated by a mirror at different positions are different, and a complex visual space can interfere with positioning, HMDE (Hierarchical Mixture of Direct Experts) is designed in a low-rank space, so that bias of SAM on an entity is reduced, and experts are dynamically adjusted according to input. Meanwhile, it is observed that a depth difference exists between a mirror surface and an adjacent area, depth mark calibration (DTC) is provided, learnable depth marks are introduced to generate a depth map and serve as error correction factors, in addition, selective pixel-prototype contrast (SPPC) loss is further formulated, and the error correction factors are used as error correction factors. Partially confusable samples are selected to facilitate decoupling of specular and non-specular representations. Wide experiments on four mirror reference data sets and three settings show that the method exceeds the existing most advanced method under the condition of a small number of trainable parameters and FLOPs.

The invention relates to the technical field of computer vision, and discloses a figure interaction detection method based on spatial fine-grained context interaction feature fusion, which comprises the following steps of: firstly, performing target detection on an input image to obtain a target detection result set and a person-object pairing feature; performing gridding projection on the image to obtain image global features, and inputting the image global features into a spatial fine-grained feature learning module to obtain spatial fine-grained features; inputting the spatial fine-grained features and the human-object pairing features into a spatial context interaction feature fusion module to obtain spatial context interaction features, and inputting the spatial context interaction features and the spatial fine-grained features into a visual encoder to obtain enhanced human-object pairing features; obtaining an interaction category score according to the feature and a text embedding feature of a character interaction category; and iteratively optimizing the character interaction detection model until convergence. According to the method, the description of local details of a visual space is enhanced, the context relationship between a person and an object is enhanced, and the accuracy of person interaction detection is improved.

The present invention relates to the field of digital measurement technology, in particular to an uncertainty calculation method for a visual measurement system. First, a visual space is defined as a plurality of sections along a central axis of a visual measurement system and divided into grid nodes. Then, in a system measurement coordinate system, a standard device is used to evaluate the standard uncertainty component of the position of the grid node. Finally, according to the geometric relationship between the observation point and the adjacent sections and the grid node, the uncertainty of the observation point is predicted and calculated in combination with a set weight distribution and an interpolation algorithm. The calculation method is simple, efficient and highly reliable, and avoids the deviation between theory and practice caused by factors such as incomplete consideration of error sources caused by theoretical analysis.

The invention relates to the technical field of urban planning and spatial information, in particular to an urban landscape style assessment method and system based on three-dimensional reconstruction, and the method comprises the following steps: S1, generating a live-action three-dimensional model of a target urban area; s2, dividing the target urban area into a plurality of space grid units on a horizontal plane; s3, extracting three-dimensional visual feature parameters in each vision field range; s4, calculating a visual space oppression index according to the geometrical relationship of the buildings in the visual field range; s5, calculating a landscape style and feature comprehensive evaluation value of the space grid unit; and S6, mapping the comprehensive evaluation value of the landscape style to the geographic position of the landscape style, and generating an urban landscape style evaluation thermodynamic diagram. According to the method, through three-dimensional modeling, visual feature extraction and comprehensive quantitative evaluation, three-dimensional, standardized and visual evaluation of urban landscape styles and features is realized, and scientific support is provided for urban planning and styles and features management.

The invention discloses a low-cost robot imitation learning method and system based on a human video. The method comprises the following steps: S1, data acquisition; s2, data extraction and physical alignment are carried out to eliminate man-machine physical differences; the step is divided into two parallel processing modules of action space alignment and visual space alignment; s3, data set construction: mixing the aligned human data with real robot teleoperation data, carrying out balanced sampling, and constructing a mixed data set Dmix; and S4, cooperative training: constructing a strategy network based on diffusion Transform for training. According to the method, data can be acquired only through the monocular RGB camera, expensive robot teleoperation data are replaced with cheap and easily available human videos, and the data acquisition threshold is greatly reduced. Through a visual alignment strategy of random color grid rendering, a network can learn neglect skin color textures and pay attention to geometric structures without a complex generative model, so that the robot can be seamlessly migrated to robots in different forms.

The invention discloses a vehicle-mounted audio augmented reality system and method based on virtual-real fusion space anchoring, and relates to the technical field of intelligent cabin man-machine interaction, and the system comprises a controller, an augmented reality display device, a distributed loudspeaker array, and a sensor group. The controller receives the virtual image pixel coordinates, the eyeball position coordinates and the vehicle state semantic identifier, and stores a cabin three-dimensional digital model. The system converts a dynamic pixel coordinate into a three-dimensional virtual sound source coordinate by utilizing perspective inverse projection through vision-space mapping logic; the retrieval model anchors the semantic identifier to the physical component coordinates through the semantic-space mapping logic. The audio rendering module calculates a speaker drive gain based on the virtual sound source coordinates to synthesize a virtual sound source. By constructing a unified cabin three-dimensional digital model and a double-channel mapping mechanism, spatial alignment of an audio sound image, an AR visual track and a physical part of a vehicle body is realized, and audio-visual perception splitting is eliminated.

The invention relates to the technical field of photovoltaic safety monitoring, and discloses a photovoltaic power station fire hazard monitoring system based on computer vision. The system comprises an image acquisition unit, a visual feature extraction unit, a feature fusion unit, a hidden danger weight distribution unit and a hidden danger tracing unit. The image acquisition unit acquires real-time visible light image data and infrared thermal imaging data; a visual feature extraction unit extracts a visual space feature vector and a heat distribution feature vector; the feature fusion unit performs multi-modal fusion on the two types of vectors to generate a fusion monitoring feature set; the hidden danger weight distribution unit calculates a hidden danger association degree score set through a pre-trained convolutional neural network model, wherein the set comprises association strength of abnormal heat distribution and visual abnormality of different regions; and the hidden danger tracing unit generates a fire hidden danger positioning result based on association intensity distribution joint positioning. The system can comprehensively capture the state of the photovoltaic power station, improves the accuracy and comprehensiveness of fire hazard monitoring, and assists the safe operation of the photovoltaic power station.

The invention discloses a double-branch zero-sample remote sensing scene classification method based on a knowledge graph, which relates to the technical field of scene recognition, and automatically constructs a'scene-landscape-ground feature 'three-level knowledge graph by analyzing ground feature composition and spatial distribution information in a local area; on this basis, the designed double-branch supervision zero sample remote sensing scene classification network DBSS supervises mapping from the semantic vector to the visual space from a global branch and a local branch, so that the visual space can fully reflect a correlation structure contained in the semantic space; a large number of experiments carried out on UCM, AID and NWPU data sets show that the class average accuracy and the overall precision of classification of invisible class scenes by AKG-DBSS can reach 98% and 59.56% at most, the standard deviation is smaller than 6.91%, the method is remarkably superior to other methods in the prior art, and good expandability is achieved.

The invention relates to the technical field of power transmission and transformation construction, in particular to a virtual visual space safety management and control system for power transmission and transformation construction, which is used for solving the problems that the physical condition of a constructor cannot be supervised in real time to judge whether the constructor is dangerous or not, the safety of the constructor during construction is difficult to guarantee, and the safety of the constructor is influenced in the prior art. And safe operation of the power transmission and transformation project cannot be ensured. The physical condition of the constructor is supervised and analyzed in real time through the constructor supervision module so as to judge whether the constructor is in danger during construction, if the constructor is in danger, a manager is warned and reminded in time, the constructor is immediately arranged to stop working and send to a doctor for examination, the safety of the constructor during construction is effectively guaranteed, and the construction efficiency is improved. Personnel safety accidents in a power transmission and transformation project are avoided, and safe proceeding of the power transmission and transformation project is ensured.

The invention discloses a trajectory generation method and system based on joint measurement of pedestrian visual spatio-temporal information, and relates to the technical field of image processing, and the method comprises the following steps: constructing a video monitoring base library, extracting visual features of all data in the video monitoring base library, and constructing a historical trajectory sequence at the same time; cutting and coding the historical trajectory sequence by a time window of set time to obtain a plurality of historical trajectory features, and inputting the historical trajectory features into a multi-modal recurrent neural network model to output the next hop space-time transition probability of each camera; generating a track sequence point at the next moment by combining the measurement with the visual feature similarity, and updating a historical track sequence; according to the trajectory generation method and system, the trajectory sequence of single travel of pedestrians is generated.

The invention discloses a laboratory safety assessment method based on machine vision, and relates to the technical field of laboratory safety monitoring, and the method comprises the steps: carrying out the standard processing of collected laboratory comprehensive data, obtaining a standard monitoring image, carrying out the feature element setting of the standard monitoring image, and obtaining a feature recognition matrix; constructing a virtual visual space, performing supplementary positioning on the virtual visual space to obtain a virtual laboratory model and an image acquisition node, and performing feature acquisition on the standard monitoring image through a feature recognition matrix based on the virtual visual space to obtain a monitoring feature picture; performing periodic comparison on the monitoring feature pictures to obtain a difference comparison result, and performing safety judgment on the laboratory according to the difference comparison result to obtain an anomaly evaluation result; laboratory safety is greatly improved, accident loss is reduced, and the safety management level of a laboratory is improved.

The invention discloses unilateral vision space perception parameter acquisition equipment based on picture description and reading test, and relates to the technical field of man-machine interaction, and the equipment comprises a picture description test device, a reading test device and a parameter acquisition device; the picture description testing device is used for carrying out picture description testing and obtaining picture description parameters according to an eyeball focus point movement track and accumulated fixation time; the reading test device is used for performing reading test and obtaining reading parameters according to gaze, gestures and voice input; and the visual space perception parameter acquisition device is used for acquiring a target single-side visual space perception parameter according to the picture description parameter and the reading parameter. According to the method, a data basis for comprehensively evaluating the unilateral visual spatial perception ability of the subject is provided, the visual spatial perception data are accurately acquired, and the comprehensive data can be used for accurately identifying the deviation or deficiency or unilateral neglect of the visual spatial perception ability.

Implementations for robot control are provided. A method involves, based on vision data depicting an environment of a robot and a natural language instruction for the robot, determining an affordance plan for performing a task. The affordance plan comprises a sequence of intermediate representations of the robot in visual space, such as end effector poses. An action input prompt is assembled with data indicative of the vision data, the natural language instruction, and the affordance plan. The action input prompt is processed using one or more generative models to generate action output indicative of one or more actions to be performed by the robot. Subsequently, a robot control signal is generated based on the one or more actions. This provides a spatially precise and dimensionally concise form of guidance for robot manipulation tasks, which can improve performance and generalization.

The application discloses a kind of double-branch zero sample remote sensing scene classification method based on knowledge graph, it is related to scene identification technical field, by analyzing the spatial distribution information of local area ground object composition, automatically constructs a kind of " scene-landscape-ground object " three-level knowledge graph;On this basis, the double-branch supervision zero sample remote sensing scene classification network DBSS designed from global and local two branches respectively to the mapping of semantic vector to visual space is supervised, to promote visual space can fully reflect the correlation structure contained in semantic space;Through a large number of experiments on UCM, AID and NWPU data set, it shows that the class average accuracy and overall accuracy of AKG-DBSS for the classification of invisible class scene can reach 98% and 59.56% respectively, the standard deviation is less than 6.91%, significantly better than other prior art methods, with good scalability.

The embodiments of the present disclosure provide an image processing method, apparatus, device and a storage medium. The method includes: obtaining a target object detected in a current key frame and a rendered virtual object corresponding to the current key frame, wherein the key frame is a frame which triggers object detection; determining, based on the detected target object and a vision space queue, a virtual object to be newly added; determining, based on the rendered virtual object and the vision space queue, a virtual object to be deleted; and updating, based on the virtual object to be newly added and the virtual object to be deleted, a virtual object corresponding to the current key frame.

The invention provides an intraoperative gauze counting and tracking method based on visual perception, and relates to the technical field of visual pattern recognition. A collaborative perception framework integrating multispectral physical fingerprint recognition, visual space-time tracking and adaptive entangled state filtering is constructed; the core problem of target identity confirmation and persistent tracking in the prior art is solved. High-robustness fluorescent fingerprints are introduced to serve as identity anchor points, deep coupling and mutual correction of physical identities and spatial-temporal trajectories are achieved through an adaptive fusion algorithm, and finally absolute identity confirmation and high-precision and high-robustness continuous tracking of each target are achieved. According to the method, the recognition accuracy in a seriously polluted and shielded environment is improved, error accumulation in a long-term tracking process is effectively inhibited, and the self-adaptive capability and reliability of the whole system in a dynamic complex scene are enhanced.

The present invention relates to a multi-scale image matching method and system. The method comprises the following steps: S1, obtaining a data set for image matching training; S2, constructing an image matching model, which includes a visual space cue extraction module, a visual space fusion module and a visual space latent map transformation module. The visual space cue extraction module extracts visual cues and spatial cues from the input image pair. The visual space fusion module fuses the extracted visual cues and spatial cues into the same space. The visual space latent map transformation module extracts local features from the visual space fusion features by constructing a feature map and fuses the local features with global features, so as to effectively capture multi-scale context information corresponding to the visual space; training the image matching model through the data set; S3, performing image matching on the image pair to be matched through the trained image matching model. The method and system can improve the speed and accuracy of image matching.

This invention discloses a dynamic monitoring and alarm system for electrical fire hazards based on big data analysis, belonging to the field of electrical safety monitoring and intelligent early warning technology. It includes modules for visual space modeling and interference prediction, temporal thermal anomaly extraction, structural contour recognition and consistency judgment, temperature disturbance assessment and credibility grading, composite credibility modeling and hotspot correction, and dynamic scoring and identification model update. The visual space modeling and interference prediction module acquires the field-of-view projection model of the thermal imaging monitoring area and establishes a high-risk pixel distribution model for reflection interference based on camera installation parameters and spatial geometric features. This invention constructs an intelligent identification closed-loop system from perception to decision-making, integrating spatial modeling, thermal behavior analysis, and dynamic credibility assessment to achieve accurate identification and continuous adjustment of false hotspots, effectively avoiding false alarms and missed alarms, and significantly improving the accuracy and system stability of electrical fire early warning.