460 results about "Spatial consistency" patented technology

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

The invention relates to an underwater topographic measurement method based on laser radar and visual fusion, which comprises the following steps: synchronously acquiring underwater point cloud, images and physical environment sensing data, and endowing a unified space-time label to realize the space-time consistency of multi-source data; according to the method, the data quality of different modes is improved by means of preprocessing, denoising, scale normalization, feature enhancement and the like, an environment interference weight matrix is constructed in combination with an environment sensing model, feature extraction and matching algorithm parameters are adaptively adjusted according to different environment states, and multi-scale feature description, spatial consistency and physical constraint criteria are established, so that the multi-modal data quality is improved. According to the scheme, high-reliability feature matching between the point cloud and the image is achieved, finally, through environment-driven iterative optimization and fusion, the robustness and precision of space registration are improved, high-precision multi-modal data automatic registration can be stably achieved in the underwater dynamic environment, adaptability is high, and environment perception and space measurement quality is effectively improved.

The invention relates to the field of brain tumor analysis, and discloses a brain tumor image analysis system based on artificial intelligence, comprising: a spatial alignment unit for acquiring original image data of the brain of a subject; performing spatial alignment on the original image data according to a cross-modal registration algorithm to obtain standardized image data; the feature extraction unit is used for performing tumor region initial segmentation on the standardized image data according to a three-dimensional convolutional neural network so as to obtain a coarse segmentation probability graph; and extracting three-dimensional geometric feature parameters of the tumor candidate region according to the coarse segmentation probability graph. According to the method, the original image data is spatially aligned through the cross-modal registration algorithm, and the spatial consistency between different image sources is ensured, so that the image data under different modals can be accurately compared and analyzed, and an accurate spatial reference is provided for subsequent tumor region identification and processing.

The invention provides a semantic aerial view visual relocation method and device in a non-exposed scene, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a multi-view image sequence under a non-exposed scene (such as a tunnel, an underground pipe gallery or an underground parking lot); semantic recognition is carried out based on a pre-trained semantic target detection model, and spatial consistency semantic features are extracted through a semantic-geometric dual-channel fusion mechanism combining a semantic mask and geometric constraints; the method comprises the following steps of: realizing three-dimensional reconstruction by using a voxel micro-renderable modeling method (VGGT), and generating a dense three-dimensional semantic point cloud fusing semantics and a geometric structure; two-dimensional semantics are mapped to a three-dimensional space through a projection and back projection relation, and point cloud semantics are endowed; main structure planes such as the ground, the left wall surface and the right wall surface are extracted, and a two-dimensional semantic aerial view with semantic annotation is generated; and pose estimation is carried out based on a reciprocal matching strategy guided by a semantic mask, so that visual repositioning with high precision, high robustness and semantic interpretability is realized. The method breaks through the problems of low precision, sparse features and poor semantic consistency of traditional visual repositioning in a non-exposed environment, and can be widely applied to the fields of intelligent transportation, underground inspection and unmanned system positioning.

The invention discloses an efficient image super-resolution reconstruction method and system based on degradation region guidance, and the method comprises the following steps: S1, carrying out the region-level degradation type recognition and severity quantification of an input low-resolution image, and generating a global degradation distribution map with spatial consistency; s2, according to the global degradation distribution map and in combination with semantic-texture collaborative features, repairing a region which is judged to be seriously degraded by adopting a high-capacity branch, and repairing a region which is judged to be slightly degraded by adopting a light-weight branch; s3, fusing the output of the high-capacity branch, the output of the lightweight branch and the global detail enhanced image to generate a final high-resolution image; wherein the global detail enhanced image is obtained by enhancing the semantic-texture collaborative features.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention discloses a BIM (Building Information Modeling)-based building component attribute matching method and a BIM-based building component attribute matching system, contour line segments of a target and a reference component are segmented, curvature change values are recorded, and the local feature expression capability is enhanced. Then, dynamic adjustment parameters are introduced to optimize eigenvector weighting processing, so that the matching process adapts to different shape complexity, and mismatching is avoided; and then, identifying a local similar region by comparing the corrected matching parameter set, and generating a registration mapping relation, thereby improving the accuracy of space consistency judgment. A matching priority matrix is constructed based on the mapping relation and the shape distribution difference, high-matching-degree candidate components are quickly screened, and the matching efficiency is improved. Finally, a feedback adjustment mechanism is adopted in the attribute verification stage, it is ensured that the matching result meets the preset precision requirement, and self-adaptive optimization is achieved. The method effectively solves the problems of low precision and poor stability in non-standard geometric component matching in the prior art, and has important practical value.

The invention relates to an unstructured environment-oriented heterogeneous data fusion sensing system for an agent with a body, which can be applied to the technical field of intelligent control. The system comprises a sensing module and a control module, the sensing module is used for acquiring multi-dimensional physical attribute data, three-dimensional space form data and dynamic mechanical response data of the intelligent body in real time; the control module is used for performing timestamp synchronous marking on the multi-dimensional physical attribute data, the three-dimensional space form data and the dynamic mechanical response data, and realizing spatial consistency mapping of the multi-dimensional physical attribute data, the three-dimensional space form data and the dynamic mechanical response data through coordinate system conversion to obtain heterogeneous data; performing feature matching on the heterogeneous data to generate a fusion perception vector; based on the fusion perception vector, generating an autonomous decision instruction corresponding to the body agent; and controlling the body agent to execute a target action corresponding to the autonomous decision instruction. By adopting the system, the control accuracy of the intelligent body can be improved.

The invention discloses a natural language and machine vision-based alignment method, which comprises the following steps of: providing a three-level alignment architecture, and respectively extracting local-global features of a visual scene and grammar-semantic features of a natural language by adopting a double-flow feature extraction network; through a space-time attention enhanced cross-modal alignment module, a dynamic gating mechanism is adopted to complete feature space adaptive projection; a joint optimization strategy is constructed based on comparative learning, and vision and language embedding space consistency is optimized by using a multi-granularity comparative loss function. And meanwhile, semantic topology constraint and visual causal reasoning are introduced, so that the calculation complexity is reduced, and the task robustness is improved. Through experimental test data, the Top-1 accuracy rate of the method in image-text retrieval reaches 92.3%, the visual question and answer F1 value is 83.4%, the model parameter quantity is reduced by 40%, the method can be widely applied to the fields of intelligent interaction systems, automatic driving scene understanding, industrial quality inspection knowledge base construction and the like, and the semantic perception and reasoning ability of a multi-modal system is remarkably improved.

The invention relates to the technical field of space-time prediction, in particular to a brucellosis space-time prediction method and system based on artificial intelligence, and the method comprises the following steps: obtaining case information, calculating an incidence relation, extracting a path sequence, screening a trend direction, and matching a new case to generate prediction data. According to the method, continuous identification of a propagation path is realized by constructing a propagation incidence relation between cases and introducing a spatial directivity index, a non-trend propagation process is screened out through an angle average value and a variance, spatial consistency of path screening is enhanced, and through joint matching of spatio-temporal characteristics of newly-added cases and an existing propagation trend, the propagation path screening efficiency is improved. The sensitivity of prediction to the trend attribution of a new case is improved, the spatial directivity of a potential disease area is enhanced through reverse projection of a trend path and area positioning drop point frequency analysis, and through linkage processing of multi-level path extraction, trend judgment and area coding, the probability of occurrence of the new case is lowered. And the capturing capability of the prediction data on the propagation and evolution characteristics of the brucellosis is improved.

The invention discloses a defect segmentation loss evaluation method based on space consistency optimization, and relates to the field of computer vision and defect segmentation. The problem that an existing loss calculation method is difficult to give consideration to defect space consistency and fair modeling of defects of different scales, and consequently loss evaluation accuracy is poor is solved. According to the method, a prediction result and a defect area in a real label are subjected to instantiation modeling, a prediction and real defect set is constructed, and one-to-one optimal matching of the prediction result and the real defect set is realized through a Hungary matching algorithm; in order to enhance the modeling capability for defects of different sizes, a Wasserstein distance is introduced to measure the spatial similarity of matched defect pairs, defect segmentation loss is calculated for successfully matched defect pairs based on the distance, a fixed loss value is directly applied to predicted defects which are not successfully matched, and the sum of the defect segmentation loss and the fixed loss value is used as a total loss value of a sample. The method is mainly applied to an industrial product surface defect segmentation task.

The invention relates to the technical field of slope monitoring, and discloses a slope slippage identification method and system for unmanned aerial vehicle inspection. According to the method, an unmanned aerial vehicle carries a laser radar to collect three-dimensional point cloud data of a slope, and an initial spatial distribution field is generated through spatial gridding preprocessing; establishing a slope space reference field model, and performing space consistency correction on the initial space distribution field to obtain a corrected displacement field at multiple time points; the unmanned aerial vehicle is controlled to periodically inspect according to the displacement safety threshold, and actual displacement is obtained by combining a ground displacement sensor; when the deviation between the grid unit displacement amount and the actual displacement amount exceeds a preset tolerance, extracting a historical displacement feature vector and dynamically adjusting a displacement value, and generating an updated displacement field; and inputting the updated displacement field into the space reference field model for repeated routing inspection until all the deviations are smaller than a preset tolerance. The method improves the accuracy and reliability of slope slippage identification, and is suitable for various slope monitoring scenes.

The invention discloses a three-dimensional terrain construction method based on multi-modal semantic constraints, and relates to the technical field of three-dimensional terrain modelling, and the method comprises the steps: obtaining an optical remote sensing image and a digital earth surface model, constructing unified space reference data, and carrying out the joint semantic analysis of ground features under the guidance of the multi-modal semantic constraints; generating a semantic mask set with structural consistency; further performing regional geometric reconstruction on the digital earth surface model on the basis of a mapping relation between semantic categories and earth surface physical attributes to form a three-dimensional terrain entity conforming to ground feature space expression characteristics; meanwhile, by analyzing the project cost document and extracting the project cost data with spatial directivity, under the constraint of spatial consistency and semantic consistency, the project cost information and the three-dimensional topographic entity are subjected to association mapping, and finally a three-dimensional topographic data result fusing topographic geometric information and project economic attributes is formed.

The invention discloses an energy digital management method and system, and the method comprises the steps: receiving a multi-source heterogeneous energy data collection signal, and generating an initial energy characteristic spectrum based on a space-time correlation feature extraction algorithm; performing graph neural network coding on the initial energy feature graph, constructing an energy knowledge graph vector set, and decomposing a high-dimensional vector in the knowledge graph vector set into a plurality of subspace vectors; performing real-time scheduling decision on the subspace vectors by using an energy flow optimization model driven by reinforcement learning to generate an energy distribution strategy set; a digital twinborn verification algorithm is applied to the energy distribution strategy set, physical-digital space consistency verification is carried out, and a trusted energy scheduling scheme is generated; and performing block chain evidence storage and intelligent contract execution on the credible energy scheduling scheme to generate a non-tampering energy transaction record. By utilizing the embodiment of the invention, the scheduling precision and the response speed of the energy system can be improved, and full-link credible management from decision-making to execution is realized.

The invention discloses an artificial intelligence-based equipment security and protection monitoring method, equipment and a medium, and relates to the technical field of equipment security and protection monitoring, and the method comprises the steps: constructing a deep learning optical flow estimation model based on image frame sequences, carrying out the optical flow motion vector calculation of adjacent image frames through employing the model, and extracting the motion characteristics of smoke diffusion; textural features are extracted from the image frames, normalization and time alignment are carried out on the textural features and smoke diffusion characterization features, and a space-time joint feature vector sequence is constructed; performing smoke event classification prediction on the sequence through a time sequence classification model containing an attention mechanism to obtain an event type and confidence; and triggering a security response according to the smoke event type and the confidence coefficient. According to the invention, the dynamic characteristics of smoke diffusion are effectively extracted, and the detection precision is improved; the capturing capability of time sequence continuity and space consistency in the smoke diffusion process is enhanced through spatio-temporal joint modeling, and missing report and false report are reduced.

The invention provides a vegetation index spatio-temporal fusion method considering farmland spatio-temporal heterogeneity, which comprises the following steps: simultaneously acquiring high-resolution (fine-resolution) and low-resolution (coarse-resolution) satellite remote sensing data of a target area, preprocessing the satellite remote sensing data, carrying out field parcel segmentation on the high-resolution remote sensing data, and carrying out field parcel segmentation on the low-resolution remote sensing data; optimizing a selection strategy of similar pixels by taking the field parcel as a unit; vegetation index time series data of the high-quality coarse-resolution image are constructed through data reconstruction; fitting a crop vegetation index growth curve of the fine-resolution image by adopting a Double Logistic function, and generating a fine-resolution pixel initial value at a prediction moment; constructing an optimization model and optimizing a fine pixel vegetation index value in combination with coarse and fine resolution pixel data; weighted distribution is carried out based on the spatial consistency in the field, and a prediction image is optimized in combination with spatial filtering. According to the method, the problems of unreasonable assumption of a traditional space-time fusion model on crop spectrum linear growth in a high-heterogeneity farmland scene and inaccurate similar pixel matching are effectively solved, the fusion precision and robustness are improved, and the method has a wide application prospect.

The invention discloses a plane 2D point cloud map updating method based on dynamic value evaluation, which belongs to the technical field of intelligent navigation, and comprises the following steps: acquiring sensor data of a current frame, and matching the sensor data with a currently maintained global map to obtain a matching result; based on the matching result, updating and evaluating the target unit in the map, and fusing an instant cleaning criterion based on space consistency and a long-term value criterion based on historical state information; according to a result of the update evaluation, determining an update action on the target unit, the update action at least comprising a deletion operation; an update action is performed to update the global map. According to the method, the accurate map updating decision is realized through intelligent fusion of instant and long-term double criteria, and the global scoring and fault-tolerant control mechanism is combined, so that the long-term consistency and high precision of the map are ensured, the operation efficiency and stability of the system are remarkably improved, and reliable and adaptive navigation support is provided for the robot in a dynamic environment.

The invention discloses a multi-modal condition-driven closed-loop sensing and generation optimization method and device and a medium, which are applied to an automatic driving long-tail scene, firstly, multi-modal conditions such as text description, a semantic map and a 3D layout are uniformly represented, a causal inference network is introduced, a causal structure between scene elements is explicitly inferred from multi-modal input, and a multi-modal condition-driven closed-loop sensing and generation optimization model is obtained. Generating structured causal embedding to realize causal perception enhancement of generation conditions; in a diffusion generation stage, a causal consistency mechanism is deeply fused into a condition control and denoising process, and a diffusion model is guided to effectively inhibit generation of unreasonable or common sense violating scenes in a generation process through an anti-fact condition constructed by a causal inference network, so that semantic reasonability, spatial consistency and dynamic credibility of long-tail data are remarkably improved. According to the method, the problem that long-tail scene data is deficient is solved, causal constraints are introduced to a generation source, and the reliability, robustness and cross-domain generalization ability of an automatic driving system in an extreme scene are remarkably improved.

The invention discloses an automatic driving obstacle recognition method for a complex scenic spot road scene. The method comprises the following steps: acquiring an original image of a scenic spot road; inputting a multi-scale feature extraction network to generate a multi-scale feature map; inputting the image into a multi-branch decoder to respectively obtain an obstacle region segmentation result, an obstacle position bounding box coordinate and an obstacle type classification probability; in combination with surrounding environment information of road geometrical morphology, weather and signal lamp states, feature weighting is carried out on the surrounding environment information by adopting an attention mechanism, and an initial obstacle saliency map is generated; according to a preset obstacle type priority level, detecting and processing an identification result of spatial overlapping in the image to obtain an obstacle identification result after conflict resolution; and performing spatial consistency proofreading on the road path segmentation map and the road path segmentation map, eliminating inter-frame jump based on time sequence smoothing, and finally outputting an identification result. According to the invention, the problem of high-precision and real-time obstacle recognition for complex scenic spot road scenes is effectively solved.

The invention relates to the technical field of multi-camera image processing and computational photography, and discloses an image processing method and system based on four-camera cross-focal-length continuous zooming fusion. The image processing method comprises the steps of system initialization, image acquisition, zoom routing, automatic ROI extraction and tracking, field-of-view cutting and geometric alignment, image fusion and binocular depth recognition. Through systematized multi-camera collaborative design, an innovative mechanism is introduced in key links such as zoom routing, geometric alignment, image fusion and depth recognition, smooth zoom, space consistency, detail fidelity, power consumption optimization and high-quality 3D perception are realized, the imaging quality is improved through the effects, the application scene is expanded, and the application prospect is wide. And a comprehensive solution is provided for mobile photography, AR and intelligent visual systems.

The present application belongs to the technical field of power transmission line engineering cableway design and construction, and particularly relates to an engineering cableway stockyard point spatial data clustering method and system based on road feature enhancement. The method comprises the following steps: S1, road feature extraction: extracting road center line feature data, road average width feature data, and road unit direction feature data; S2, road feature enhancement processing: calculating boundary minimum distance feature data, transverse offset feature data based on cross product, and longitudinal projection position feature data; S3, spatial data clustering: based on the enhanced road features, using the K-means clustering algorithm, the stockyard points are automatically divided into left stockyard point set and right stockyard point set; S4, spatial consistency optimization: based on the principle of geographical spatial continuity, the outlier point label is corrected through the neighbor point voting mechanism. The accuracy and generalization of the spatial data clustering of the stockyard points of the power transmission line engineering cableway are improved.

The invention belongs to the technical field of optical three-dimensional scanning, and relates to a real-time registration method for high-density point clouds, which comprises the following steps: acquiring two groups of sparse point cloud data generated by three-dimensional scanning in an oral cavity, inputting the point cloud data into a constructed efficient registration model, extracting multi-dimensional feature descriptions of a source point cloud and a target point cloud, and constructing a registration model; the point cloud registration method comprises the following steps of: obtaining a point pair set, calculating a feature distance to form an initial point pair set, performing spatial consistency analysis on the initial point pair set, rejecting outlier pairs to obtain a cluster point pair subset, and finally performing point cloud registration calculation on the cluster point pair subset to complete point cloud registration. The invention further relates to a high-density point cloud real-time registration device, scanning equipment and a storage medium. According to the technical scheme provided by the invention, the registration precision and stability can be improved, and meanwhile, the requirement of the intraoral scanner on the real-time performance is ensured.

The invention provides a handheld product six-surface completion state judgment method based on cross view angle image fusion, and relates to the technical field of data processing, and the method comprises the steps: calculating a sight line vector of a point O pointing to a pupil center based on pupil coordinates and spatial consistency coordinates; calculating a product body axial vector of the point O pointing to each datum point based on the coordinates of the points X, Y and Z; on the basis of the sight line vector and the axial vector, azimuth angle and pitch angle characteristic quantities are calculated, quadrant segmentation is carried out through a space cutting algorithm, and a detected surface result is obtained in combination with a preset space azimuth mapping rule; activating a corresponding accumulator based on a detected surface result, accumulating continuous detected duration and comparing the accumulated duration with a preset threshold value; if the duration reaches the threshold value, generating a completion signal, otherwise, triggering an audible and visual alarm. According to the method, the detection reliability and the result credibility in a complex operation scene are improved.

The invention relates to a mapping system and method for flight inspection of indoor equipment, the system comprises a data acquisition end and an offline data processing end, the data acquisition end comprises an unmanned aerial vehicle and a sensing module, and the sensing module comprises a laser radar module, a binocular camera module, an inertial measurement module and an airborne recording module; the method comprises the following steps: synchronously acquiring multi-source data through layered flight; performing space-time alignment and preprocessing; self-adaptive sampling is carried out based on point cloud geometric prior guide image features, and laser-vision joint features are generated; fusing point cloud registration, joint features and inertial data, and carrying out joint estimation on adjacent frame pose increments; performing loopback detection based on the key frame to generate a closed-loop constraint; constructing and optimizing a pose map to eliminate cumulative drift; and finally fusing to generate a global point cloud map. According to the method, the space consistency and geometric accuracy of the map can be effectively improved, and a reliable data basis is provided for digital modeling and intelligent operation and maintenance of indoor equipment inspection.

The invention discloses a method for automatically correcting radial distortion of a wide-angle lens, and relates to the technical field of computer vision and image processing, and the method comprises the steps: collecting an original image, calculating the distance from a pixel to an imaging principal point, and extracting a multi-scale image and edge features; analyzing the edge direction and radial change and linear extension of the structure, extracting a linear structure candidate region, obtaining a distortion compensation parameter, generating a multi-scale candidate linear parameter set, and calculating a multi-scale linear consistency index for adaptive adjustment; extracting a point set from the candidate straight line parameter set, executing forward and reverse mapping to calculate a dual-space consistency index, and optimizing local parameters and the point set; calculating a minimum linear complexity index based on the line segment curvature, the curvature change rate and the length, and implementing local optimization; and performing weighted fusion on each index to construct a comprehensive objective function, performing step-by-step shrinkage optimization to obtain an optimal compensation parameter, performing global geometric correction on an original image, generating a distortion correction image, and realizing improvement of the structure recognition precision and the geometric correction effect.

The invention discloses a thin coal seam working face monitoring method and system based on computer vision, and relates to the field of thin coal seam working face clamping plate monitoring. According to the method, continuous tracking and dynamic feature extraction of the three-dimensional form of the splint are realized by combining multi-source visual data fusion with a time sequence form analysis modeling method. Specifically, a structured light depth camera, an RGB camera and a thermal infrared imager are utilized to obtain morphological characteristics of a clamping plate in different dimensions, and a three-dimensional model with spatial consistency is constructed through fusion; and a morphological risk function is constructed in combination with curvature change, top height difference and time continuity characteristics, so that quantitative, dynamic and real-time evaluation of the clamping plate disengagement risk is realized, and the system can judge potential risks in advance in the early deformation stage of the clamping plate.

The invention discloses a power distribution network inspection robot system and method, and relates to the technical field of intelligent inspection, and the system comprises a data collection module, a data registration module, an anomaly detection module, a space verification module, a time sequence analysis module and a comprehensive judgment module. The data acquisition module controls the robot to acquire multi-modal data and record space-time positioning information; the data registration module registers the multi-modal data to an equipment coordinate system; the anomaly detection module extracts anomaly features and calculates confidence; the space verification module carries out space consistency verification and physical logic consistency verification according to the physical rule base and evaluates the abnormal level; the time sequence analysis module evaluates a defect evolution state; and the comprehensive judgment module performs defect judgment and generates a processing strategy. According to the method, single-mode false detection and environmental interference are effectively eliminated through a dual verification mechanism, comprehensive evaluation of the current state and the development trend of the defect is realized in combination with time sequence evolution analysis, and the accuracy and the reliability of defect detection are remarkably improved.

The invention relates to a video diffusion model-based camera condition-guided viewpoint synthesis method, which belongs to the technical field of computer vision, and comprises the following steps of: taking a stable video diffusion model SVD as a basic generator, and jointly inputting Gaussian noise and encoded image potential representation to obtain a video diffusion model SVD-based viewpoint synthesis model; meanwhile, a pose encoder based on a time attention mechanism is used for encoding camera parameters represented by Plcarbon coordinates, and then the camera parameters are used as pose conditions to be embedded into a time attention layer in a video diffusion model de-noising U-Net; pixel-level features of an input single image are extracted through an image coding embedding network and serve as image feature conditions to be embedded into a space attention layer in a de-noised U-Net of a video diffusion model, and a pre-trained video diffusion model is finely adjusted; the space consistency of the synthetic image and the input image and the track consistency of the synthetic image and the camera parameters are improved, and the generation diversity is increased while the generation quality is improved.

The invention discloses a multi-view image Gaussian reconstruction method and system guided by LiDAR point cloud. The method comprises the following steps: step S1, realizing accurate registration of LiDAR point cloud and a multi-view image in the same earth-centered earth-fixed coordinate system through GNSS constrained aerial triangulation, and uniformly carrying out center-of-gravity normalization to ensure space consistency and numerical stability; s2, estimating surface normal and scale parameters based on the local geometric features of the LiDAR point cloud, constructing a Gaussian ellipsoid consistent with the local geometry, and realizing Gaussian ellipsoid initialization of geometric perception; and S3, generating a sub Gaussian ellipsoid according to the Gaussian gradient and the tangent plane direction, and improving the modeling density and geometric continuity of the detail region through local encryption and parameter inheritance. According to the method, the fusion of LiDAR point cloud high-precision geometry and multi-view image texture information is realized, the geometric precision, the structural integrity and the detail expression capability of three-dimensional reconstruction are effectively improved, and the method is particularly suitable for high-precision live-action three-dimensional modeling of unmanned aerial vehicle multi-view images.