4006 results about "Angle of view" patented technology

The invention belongs to the technical field of visual special effects, and discloses an AI visual special effect dynamic generation system fusing multi-modal perception. Through cooperative work of six core modules of multi-modal input processing, importance analysis, parameter configuration, parameterized rendering, parameter optimization and rendering and output, deep fusion of special effects and contents is realized. An image sequence, audio data and scene parameters can be processed at the same time, a multi-modal feature data set is constructed, a scene key visual area is scientifically recognized, a visual importance distribution map is generated, and the special effect parameter configuration and rendering process is guided. An improved neural radiation field technology and a physical constraint model are adopted to ensure the consistency and reality of the special effect at different visual angles; and a multi-dimensional quality evaluation and parameter automatic optimization mechanism is introduced, so that the visual expressive force and the artistic value of the special effect are guaranteed. The creation threshold is remarkably reduced, the cooperative expression ability of the special effect and the content is improved, and the special effect becomes a powerful tool for enhancing the narration and enhancing the emotion.

The invention provides a reconstruction method of a three-dimensional reconstruction model based on two-dimensional Gaussian splashing, which comprises the following steps: S1, carrying out sparse reconstruction on an input image sequence through a multi-view stereoscopic vision algorithm to generate an initial sparse three-dimensional point cloud and a corresponding camera pose parameter; s2, inputting an improved two-dimensional Gaussian radiation field by using the sparse three-dimensional point cloud and the camera pose as information; s3, dynamically screening a visible anchor point subset based on the current view angle parameter, and generating a rendered image through a differentiable rendering pipeline; s4, calculating a loss function of the rendering image of the training track and the input image to optimize a reconstruction scene; and S5, starting a special visualization tool, and inputting a rendering result. According to the method, by introducing a trimmable anchor point parameterization framework and a multi-scale feature fusion mechanism, light-weight and high-precision three-dimensional scene modeling is achieved, and the problems that traditional 2D Gaussian sputtering is insufficient in multi-view geometric consistency, storage overhead and weak texture region reconstruction and an existing 2D Gaussian splashing method is insufficient in self-adaptive mechanism are solved.

The invention discloses a historical block scene three-dimensional reconstruction method and system based on Gaussian sputtering, and the method comprises the steps: collecting a historical block video sequence through a lightweight panorama camera, extracting a multi-frame panorama, and generating an image data set through a projection converter; monocular depth and normal estimation is carried out through a pre-training visual model, and a prior depth and normal graph data set of a historical block scene is constructed; sparse reconstruction is carried out on the multi-view image data set based on the SfM technology, initial point cloud and camera pose information are acquired, and a Gaussian ellipsoid is optimized in combination with prior depth and normal information; dynamically calculating the geometric width estimation value of the street, and guiding and adjusting the adaptive density of the Gaussian ellipsoids of the vertical surfaces on the two sides; rendering the optimized Gaussian ellipsoid through an improved rasterization renderer; and designing a multi-modal loss function and a regularization mechanism to optimize reconstruction and rendering results. According to the method, high-fidelity three-dimensional reconstruction of the historical block scene is realized, and the adaptability of the Gaussian sputtering method to the complex block scene is enhanced.

The invention discloses a three-dimensional Gaussian sputtering scene reconstruction method based on structure perception refined Gaussian, and aims to solve the problems of Gaussian drift, edge blur, structure artifacts and the like of a reconstruction model due to the fact that sparse point cloud contains outliers, Gaussian morphology and normal are mismatched and a multi-dimensional optimization target is lacked in an existing three-dimensional Gaussian sputtering reconstruction method. A key frame is extracted by collecting target scene video data, sparse three-dimensional point clouds are reconstructed by using an SfM algorithm, a depth map and a normal map are generated through a Lotus model, three-dimensional Gaussian distribution is initialized after the sparse point clouds are filtered, a Gaussian covariance matrix is adjusted by using a normal consistency regular term, and the sparse point clouds are extracted. And after structure attribute analysis is carried out, a comprehensive scoring function is constructed to screen Gaussian points, and finally, a combined training framework including luminosity, normal consistency and structure continuity loss is adopted to optimize and generate a three-dimensional Gaussian scene model. The method is mainly applied to the field of three-dimensional reconstruction and multi-view rendering, and scene reconstruction precision and geometric consistency can be improved.

The invention relates to the field of electric power monitoring, and discloses an electric power monitoring system and method fusing image recognition and data analysis, and the method comprises the steps: carrying out the visual angle coverage modeling of a target equipment group through a multi-type visual collection unit disposed at a transformer substation and a power distribution terminal; performing cross-frame fine-grained texture differential analysis on the equipment state image sequence, and constructing an image event time window in combination with synchronous disturbance characteristics of multi-source monitoring parameters; based on the high-vigilance candidate frame set, fusing the image structure variability index and the operation data multi-dimensional deviation vector by using a feature encoder, and constructing a multi-modal state coupling feature tensor; map mapping is carried out on the potential fault evolution trend, and semantic association is established between structural nodes with abnormal attributes in the image and frequently fluctuating parameter indexes in the monitoring data; and combining a node interference path in the local fault association subgraph with fault precursor distribution induced in a historical accident sample. The method has the advantage that the operation safety is improved.

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

The invention relates to an optimization method and device for sparse view angle three-dimensional Gaussian splash, and belongs to the technical field of three-dimensional reconstruction in computer vision, and the method comprises the steps: collecting a sparse view angle image; a multi-view stereoscopic vision model based on deep learning generates a geometrically consistent depth map for the sparse view image, converts the depth map into point clouds and fuses the point clouds to obtain dense point clouds; sampling dense point clouds by adopting voxel-guided farthest point sampling to obtain initialized point clouds, and constructing a three-dimensional Gaussian field; rendering the three-dimensional Gaussian field through an enhanced geometric renderer to obtain a rendering depth and a rendering normal; constructing a multi-level geometric regularization loss function, and optimizing the three-dimensional Gaussian field; and performing optimization adjustment on the three-dimensional Gaussian field based on a shape-scale constraint criterion and a two-stage adaptive opacity constraint strategy to obtain an optimized three-dimensional Gaussian field. According to the method, the problems of initialization failure, insufficient geometric supervision and element out-of-control of 3D Gaussian splashing under the sparse view angle are solved.

The invention relates to the technical field of intelligent control, in particular to a self-adaptive camera pose intelligent adjusting system which comprises a dynamic recognition module, a pose adjusting module, a mechanism control module, an information filtering module and a feedback association module. According to the method, the foreground pixel brightness gradient and the boundary continuity in the image frame are extracted, the target displacement path can be recognized, the tracking information is constructed, the target coherent perception under rapid scene change is guaranteed, the attitude parameters and the target motion direction and speed are combined, the offset correction instruction is generated, the response stability of the dynamic target is improved, and the target tracking accuracy is improved. The execution structure feedback is used for correcting control output, reducing the driving error accumulation risk, eliminating non-target disturbance through image boundary definition and motion consistency, improving data purity and combining a view angle execution state and path information to realize synchronous control of tracking and view angle adjustment; and the response efficiency and the following robustness of the system in a dynamic environment are enhanced.

The invention relates to an image processing technology, and discloses an efficient panoramic image splicing method and system based on multi-view fusion, and the method comprises the steps: collecting a plurality of images from different views; performing multi-scale feature extraction on each image, generating a feature descriptor for each extracted feature point, and matching a corresponding feature point pair; performing multi-view geometric constraint screening on the feature point pairs; according to the screened feature point pairs, estimating a homography matrix between adjacent images, and carrying out global optimization on the homography matrix; aligning all the images into the same coordinate system; determining an overlapping region between adjacent images; and according to the pixel information in the overlapping areas, fusing the overlapping areas by adopting a self-adaptive weighted fusion algorithm so as to splice the plurality of images into a panoramic image. The invention further discloses a control device and a computer readable storage medium. The invention aims to improve the efficiency and accuracy of generating the multi-view fused panoramic image.

The invention provides a road bridge crack detection method and system, and the method comprises the steps: collecting a bridge surface multi-view image, and constructing a training data set containing crack feature labeling through quality screening and standardized labeling; preprocessing the image by using a multi-scale feature fused deep convolutional neural network and carrying out semantic segmentation, initially identifying a suspected crack region and generating a segmentation mask; and constructing a BeNNS proxy model based on the mask, and establishing a mapping relationship between the detection result and the bridge structure topology, the stress flow field and the service function chain so as to evaluate the result reliability. And inputting an evaluation result into a hybrid evaluation mechanism, performing online real-time detection and offline batch verification to optimize precision, and outputting a verified crack region. Finally, morphological analysis is conducted on the area, geometric parameters and danger levels of cracks are extracted and integrated to a bridge health monitoring system, a crack evolution tracking algorithm and an early warning mechanism are established, and dynamic tracking early warning is achieved. The problem of low detection precision in a complex environment can be solved.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The present disclosure relates to the field of computer vision and discloses a three-dimensional dynamic scene reconstruction method and apparatus, and a storage medium. The three-dimensional dynamic scene reconstruction method comprises: acquiring synchronized videos of a plurality of viewpoints of a dynamic scene; computing matching points between video images of different viewpoints, and estimating intrinsic and extrinsic parameters of each camera; obtaining a Gaussian splatting point set {p0} on the basis of a sparse point cloud constructed according to the depth of each matching point; for the first image frame of each video, using {p0} to perform static training thereon, to obtain a Gaussian splatting point set {p}; for the remaining image frames, dividing {p} into a static point set {S} and a dynamic point set {D}, performing dynamic training on {D}, and constructing a dynamic Gaussian splatting point set {P} from {p}, {S}, and the final {D}; and, in view of the intrinsic and extrinsic parameters of each camera, rendering {P} using a Gaussian splatting rendering pipeline, to obtain rendered images at different moments from new viewpoints.

The invention relates to a self-localization and motion perception method based on deep learning, and the method comprises the following steps: multi-modal perception: collecting RGB frames and event streams through employing a DAVIS346 event camera, obtaining texture and depth information through employing an RGB-D camera, and supplementing 3D structure data through a laser radar; hybrid optical flow driven motion perception: adopting a double-branch architecture of an improved RAFT network and an event optical flow private network; multi-modal 3D detection and trajectory management: fusing multi-modal data based on VoxelNeXt-Lite to generate a 3D detection frame, filtering false detection by combining point cloud density clustering and an event density threshold, then constructing a space-time diagram associated trajectory through a Spatio-Temporal Graph Transformer, and optimizing pedestrian trajectory prediction continuity by using an LSTM (Long Short Term Memory) model perceived by a gait cycle; multi-view semantic graph matching: constructing a 3D semantic voxel map containing vertical features, calculating scene similarity in combination with top view NetVLAD features and deformable graph matching, and dynamically adjusting semantic weight by using a situation encoder and knowledge graph reasoning; and carrying out multi-sensor fusion and robust positioning.

The invention relates to the field of earth wall defect recognition, and discloses an assembled retaining wall defect recognition method and system based on image recognition, and the method comprises the steps: obtaining multi-view image data of an assembled retaining wall, and constructing a defect recognition image data set in combination with a boundary detail enhancement mechanism and a region illumination compensation strategy; performing edge guide feature extraction on the defect identification image data set, and constructing an image deep feature model based on a boundary context fusion network; judging whether the response intensity change of the image deep feature model in the crack region reaches a preset threshold value or not through an edge response enhancement mechanism, and if yes, marking that the crack region has potential defects; utilizing a multi-scale morphological structure analysis method to carry out contrast perception optimization on the corrected crack area; and based on a defect identification result, combining a component positioning mechanism and component historical operation and maintenance data to perform severity grading evaluation on the defect. The method has the advantage of improving the sensitivity to the marginal area.

The invention relates to the technical field of quality monitoring, and discloses a diamond high-strength micro-powder quality detection method and system based on artificial intelligence. The method comprises the steps of obtaining a two-dimensional projection image sequence of diamond micro-powder particles, calculating a projection matrix based on camera calibration parameters and geometric constraints, obtaining a multi-view image data set of the particles, establishing a pixel-level corresponding relation, extracting three-dimensional space coordinates of the surfaces of the particles, and reconstructing dense point cloud data of the particles. Establishing a local coordinate system based on the dense point cloud data, determining attitude parameters of particles in a three-dimensional space, if the attitude parameters deviate from a normal range, performing attitude compensation processing to obtain standardized point cloud data, and performing three-dimensional grid model construction on the standardized point cloud data; and calculating geometrical characteristic parameters of the particles based on the three-dimensional grid model, performing defect detection on the surfaces of the particles, and generating a crystal integrity evaluation report of the particles. The quality detection accuracy of the diamond high-strength micro-powder particles is improved.

The invention relates to the technical field of tunnel engineering construction, and discloses an intelligent material transportation and safety monitoring system and method for shield construction, and the system comprises a visual perception unit, a sensor network module, an AI analysis center module, a safety decision module and a human-computer interaction interface. According to the invention, data acquisition is carried out through the visual perception unit and the sensor network module, multi-target detection operation is carried out on image frames through the AI analysis center module after target identification and track prediction, target types, space coordinates, contour boundaries and confidence coefficients are identified and extracted, and safety judgment and early warning output are carried out. According to the invention, by integrating the multi-view camera equipment and the UWB, GNSS and other sensors and adopting a deep learning target detection algorithm, high-precision identification and continuous tracking can be carried out on construction site personnel, equipment, segments and other key objects, and accurate input is provided for subsequent risk analysis.

The invention relates to the technical field of unmanned aerial vehicle autonomous control, and discloses an unmanned aerial vehicle obstacle avoidance control method and system based on computer vision, and the method comprises the steps: collecting the multi-view image data of a flight environment in real time through a multi-view camera, and carrying out the preprocessing; using an improved YOLOv7 network to identify obstacles in the preprocessed image, and extracting position, size and motion information of the obstacles; generating a three-dimensional environment map and a plurality of candidate obstacle avoidance paths in combination with the flight parameters of the unmanned aerial vehicle and the extracted obstacle information; an optimal path is screened based on a dynamic safety evaluation model, and the attitude and power output of the unmanned aerial vehicle are adjusted in real time to complete obstacle avoidance; an obstacle avoidance effect is verified by using an optical flow method and depth information, and path planning is dynamically corrected. According to the method, the dynamic safety evaluation model is introduced, multi-dimensional factors are comprehensively considered, the safety of each path is dynamically determined, and it is ensured that the flight path of the unmanned aerial vehicle can be timely and accurately modified and optimized in a dynamic complex environment.

The invention relates to the technical field of image super-resolution enhancement, in particular to a tablet computer image super-resolution enhancement method based on a generative adversarial network. The method comprises the following steps: collecting an image through a tablet computer, and carrying out regional illumination component calculation on the image to obtain detailed illumination component data; secondly, quantizing the motion out-of-focus fuzzy degree based on the illumination data, generating track fuzzy intensity sensing data, performing 3D modeling by combining the data, and estimating the distortion trend of the image; then, a shooting error is eliminated by using rendering visual angle distortion correction, a more real visual angle effect is generated, and super-resolution enhancement is performed on the image by using a generative adversarial network, and image details are improved. And finally, designing automatic firmware based on the super-resolution enhanced data, and embedding the automatic firmware into a tablet computer control system. According to the method, the image super-resolution enhancement technology is optimized, so that the image super-resolution enhancement technology is more perfect.

Provided are a quantification method for microscopic cracks inside 3D printed concrete and a system thereof. Before measuring the microscopic crack images according to relevant standards, firstly, a denoising network, improved attention-guided denoising neural network (IADNet) is adopted. IADNet can extract features from microscopic crack images from different perspectives, perceive noise from multiple levels, and perform denoising processing, greatly improving image quality and enhancing texture details, which is beneficial for training segmentation networks. The combination of IADNet and semantic segmentation algorithm has the ability to finely recognize image information, quantify microscopic crack recognition, overcome the shortcomings of measurement and analysis of microscopic cracks inside 3D printed concrete, and improve construction efficiency and quality.

The invention discloses a self-adaptive three-dimensional scene reconstruction method and system based on a single panorama, and belongs to the field of computer vision processing. The method comprises the following steps: firstly, generating a depth map through an indoor scene panorama and constructing an initial three-dimensional grid; then generating a multi-view image and a shielding mask thereof based on view conversion, forming a training sample pair for fine tuning of the diffusion completion model, and injecting scene prior information for the model; then extracting a grid boundary contour and calculating a central axis, and adaptively constructing a camera pose set; after the integrity of the grid is optimized through iteration completion, the grid is converted into a 3D Gaussian sputtering field, and Gaussian point parameters are optimized through multi-view data; in the optimization process, an up-sampling strategy of rendering error feedback and edge Gaussian points is adopted, and finally a scene model with complete geometric textures is output. According to the method, a high-quality three-dimensional scene is reconstructed from a single panorama through a structure self-adaptive completion and Gaussian refining technology, and the method is particularly suitable for immersive roaming reconstruction of indoor scenes.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle intelligent inspection method for construction site operation safety, which comprises the following steps: step 1, deploying an unmanned aerial vehicle, personnel positioning equipment and a base station, and establishing a time synchronization network and a space coordinate conversion model through the base station; step 2, when the unmanned aerial vehicle executes an inspection task, detecting a shielding area in an image in real time based on a preset route, and generating a dynamic obstacle avoidance path according to the three-dimensional model to control the unmanned aerial vehicle to perform multi-view supplementary shooting; and step 3, carrying out space-time alignment on the multi-view supplementary shooting image and positioning data acquired by the personnel positioning equipment. According to the invention, the technical scheme of dynamic shielding perception and multi-view adaptive supplementary shooting is adopted, a high-precision space coordinate system constructed by a reference station and a real-time shielding detection mechanism are combined with dynamic obstacle avoidance path planning, and comprehensive coverage inspection of a high-altitude and hidden operation area is realized.

A generalizable neural radiation field reconstruction method based on multi-modal information fusion, including: Step 1, constructing photometric features and geometric features based on unstructured multi-views, and constructing a multi-modal neural encoder by performing incrementally complementary fusion on the photometric features and the geometric features; Step 2, converting the multi-modal neural encoder and raw RGB pixel bodies of the unstructured multi-views into a volume density and radiation brightness; Step 3, sampling light on the basis of the constructed multi-modal neural encoder, aggregating context features of the sampled light based on a transformer network to obtain light context features; and Step 4, decoding, using the light context features, the volume density and the radiation brightness; rendering, based on the decoded volume density and the radiation brightness, to generate a free-view RGB-D image; and guiding dense reconstruction of a low-texture scene by combining photometric supervision and sparse geometric supervision.

The invention discloses a pump shell welding seam quality detection method based on image segmentation. The method comprises the following steps: generating a steady-state pump shell welding seam image flow under the driving of motion compensation; obtaining a domain adaptive DINOv2 visual embedded feature map; performing adaptive pyramid fusion and cross-scale attention operation on the domain adaptive DINOv2 visual embedded feature map to generate a semantic form segmentation map; generating a semantic-texture fusion mask; performing uncertainty weighted optimization on the semantic-texture fusion mask in combination with the pixel-level confidence map to obtain a weld defect instance map; generating an interpretable texture anomaly heat map; and through multi-view supplementary shooting or manual auditing, supplementary pump shell welding seam image data is obtained, and the steady-state pump shell welding seam image flow is updated. According to the method, the system can continuously keep accurate positioning of the pixel-level segmentation boundary in a weak-label or even non-label migration scene, and boundary drift and area missing detection of a segmentation result are effectively avoided.

The invention discloses a charging robot automobile charging port pose vision measurement method comprising the following steps: 1, controlling a mechanical arm to drive a monocular camera to collect a charging port image, and constructing a charging port image data set; 2, extracting a charging hole contour in each image by adopting an improved Mask R-CNN instance segmentation model; step 3, performing robust ellipse fitting based on each charging hole contour to obtain center coordinates of each charging hole in the two-dimensional image; step 4, establishing a world coordinate system based on charging hole space distribution defined by the automobile charging port standard model, and determining three-dimensional coordinates of each charging hole; and 5, the multi-view two-dimensional center coordinates and the corresponding three-dimensional coordinates are input into the PnP graph optimization model, and the pose of the charging port in the mechanical arm base coordinate system is solved by fusing the re-projection error constraint, the structure prior constraint and the kinematics chain constraint. The invention further discloses a charging robot automobile charging port pose vision measurement charging method and a charging system.

The invention discloses a power transmission image defect detection and defect duplicate removal method and system based on a deep learning image segmentation algorithm, and belongs to the technical field of intelligent inspection of power equipment. According to the method, real-time tower identification and adaptive shooting are realized through a lightweight YOLO model deployed at the edge end of an unmanned aerial vehicle; pixel-level segmentation is carried out on the infrared image by using an MSAN-Net network, the network integrates a ResNet encoder, a cross-scale attention mechanism and a multi-level feature pyramid, and boundary learning is enhanced by using a composite loss function; based on a multi-view three-dimensional reconstruction technology, two-dimensional defects are mapped into space rays through feature point matching and pose estimation, and defect de-weighting is achieved through ray intersection judgment. Through the MSAN-Net network, the segmentation precision of the infrared component under a complex background is remarkably improved through an attention mechanism and multi-scale feature fusion, and the problem of repeated defect detection in multi-view inspection is effectively solved in combination with a three-dimensional space mapping method.

The invention discloses a three-dimensional scene synthesis method fusing depth estimation and a double-view video, and relates to the technical field of image fusion. The invention discloses three-dimensional scene synthesis integrating depth estimation and a double-view video. Comprising the following steps of: acquiring a left image sequence and a right image sequence which are synchronous; generating an initial depth map through a depth estimation model; generating a mask of a sheltered region through region sheltering detection; positioning the sheltered region in an image of a current frame; and performing weighted fusion on the updated depth map through the depth confidence map, and outputting a three-dimensional scene of the current frame through the fused depth map. According to the method, a shielding detection mechanism and a shielding area mask are constructed, space alignment is carried out on a fusion depth map of a previous frame and an image of a current frame, and depth completion of a shielding area is realized in combination with a residual fusion strategy.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention relates to the technical field of unmanned aerial vehicle control, provides a visual environment generation method, system and device based on a neural radiation field and a storage medium, and solves the problem of poor visual environment generation effect. The method comprises the steps that semantic segmentation is carried out on a corrected image, image data with semantic tags are generated, and the semantic tags are used for marking categories of objects in an outdoor scene; updating the neural radiation field model based on the image data with the semantic tag, mapping the semantic tag to a voxel feature space corresponding to the neural radiation field model, and constructing a semantic point cloud; identifying a non-key area in the multi-view image, performing redundant voxel cutting on the non-key area, and generating a cut semantic point cloud; and utilizing the clipped semantic point cloud to drive a neural radiation field model, and generating any visual angle scene of the outdoor scene. According to the technical scheme, lightweight semantic modeling and efficient free viewpoint rendering of the outdoor visual environment are achieved, and the new visual angle generation efficiency and quality of the complex environment are improved.

The invention relates to a multi-modal sparse fusion three-dimensional target detection method in an unstructured environment, and the method is based on a complexity perception candidate region dynamic sampling strategy, and through the cross-domain self-compensation of point cloud geometric features and image spectral features, and the fusion of multi-view sparse features, the detection of a multi-modal sparse fusion three-dimensional target is realized. The perception capability and the recognition precision of the obstacle area, and the remote dynamic target detection performance and the real-time performance are improved; an anti-deformation convolution group and a terrain gradient perception attention module are introduced into a ParScaleNet image backbone network, so that the extraction of edge features of a complex scene is enhanced, and the cross-scale characterization capability of the features is improved; according to the method, a ContraSpaceOpt module with semantic maintenance capability is deployed behind a Transform, excessive smoothness of obstacle features is inhibited through class comparison loss functions and feature space anisotropy constraints, the space separability of obstacles is maintained, and the problems of point cloud fracture and texture misalignment caused by the obstacles are effectively solved.

The invention discloses a multi-modal ship target individual identification method and system, and relates to the technical field of ship target identification, and the method comprises the steps: obtaining multi-modal data of a target region, and constructing a training set and a test set; each training sample in the training set is input into a target individual prediction model, a multi-granularity adaptive loss function is adopted as a loss function to carry out model training, and the target individual prediction model adopts an image feature extraction method based on cross-modal guide enhancement to carry out feature extraction and splicing fusion on each training sample; and a target individual identification result of each training sample is obtained by adopting a visual angle self-adaptive fusion method of embedded type label information. And after training is completed, a trained target individual prediction model is obtained, each test sample in the test set is input into the trained target individual prediction model, and a final target individual identification result is obtained. According to the invention, the generalization ability of model learning individual features can be improved, and the accuracy of ship target individual identification is improved.