181 results about "Vision Disparity" patented technology

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

The invention relates to the technical field of image data processing and intelligent control, and discloses a patch board spot welding track intelligent control method and system based on visual perception. According to the method, synchronous image streams are collected through a binocular vision sensor, and epipolar correction image pairs are generated through timestamp synchronization, ROI extraction and epipolar correction processing; calculating a disparity map by adopting a stereo matching algorithm, and constructing a compensated three-dimensional point cloud model based on deformation vector field fusion multi-frame point cloud data of a radial basis kernel function; welding spot position error vectors are generated by extracting welding spot feature points and performing spatial filtering optimization; and carrying out inverse kinematics calculation on the mechanical arm by adopting a damping least square method, carrying out safety constraint optimization in combination with prospective collision risk assessment and real-time pose data, and generating a trajectory compensation instruction. According to the method, the problems of dynamic deformation compensation and motion safety in patch plate welding are solved, the submillimeter welding spot positioning precision is achieved, and the welding quality stability and the system robustness are improved.

The invention provides a robust stereo matching method fusing monocular semantic prior and multi-dimensional expert aggregation, relates to the technical field of computer vision and stereo matching, establishes a robust stereo matching framework fusing monocular semantic prior and multi-dimensional expert aggregation, and comprises a monocular branch and a binocular branch, inputting a stereo image into a robust stereo matching framework based on fusion of monocular semantic prior and multi-dimensional expert aggregation for matching calculation, wherein the matching calculation comprises the following steps: extracting semantic prior features of the input image by using a monocular branch; the method comprises the following steps: extracting a geometric enhancement feature map of a stereo image by using binocular branches, generating a matching cost body based on the geometric enhancement feature map, and carrying out refined iterative updating on the matching cost body to obtain a matched disparity map. According to the method, monocular and binocular depth estimation is cooperatively supported in a unified network architecture, and a parallel monocular depth prior path is utilized to actively guide and strengthen a core binocular matching process.

The invention relates to the technical field of computer vision, and particularly provides a tunnel video stream three-dimensional modeling system based on binocular stereo matching and SLAM (Simultaneous Localization and Mapping), which comprises a multi-modal image acquisition unit, a binocular infrared camera and an RGB (Red, Green and Blue) camera are configured to synchronously acquire an infrared image pair, an RGB image pair and inertial measurement unit data of a tunnel environment, the multi-source data is uploaded to the cloud processing platform in real time through the wireless transmission module; based on a dynamic switching mechanism of environmental perception, performing adaptive fusion pose estimation of a feature point method and a direct method on input multi-modal image data; the stereo matching and dense reconstruction unit is used for calculating a disparity map by adopting an improved self-adaptive window stereo matching algorithm, generating a three-dimensional point cloud in combination with the pose information output by the pose estimation unit, and constructing a global consistency point cloud model of the tunnel scene through a time sequence point cloud registration and splicing algorithm; and a multi-algorithm target detection and semantic fusion unit. The method can meet the requirements of precision and robustness of tunnel reconstruction.

The invention relates to the technical field of computer vision, in particular to a three-dimensional reconstruction method and device based on optical polarization and stereoscopic vision principles. The three-dimensional reconstruction method sequentially comprises the steps of image preprocessing, polarization parameter extraction, binocular stereo matching, disparity map generation and optimization, normal vector correction, depth information fusion and three-dimensional point cloud reconstruction and modeling generation. A polarization normal vector field is constrained and corrected as a'skeleton ', and the azimuth angle pi ambiguity problem which puzzles polarization three-dimensional reconstruction for a long time is solved; high-frequency surface normal details contained in corrected polarization information are used as textures to enhance and fill up depth information loss of binocular vision in weak texture and repeated texture areas, the inherent limitation of a single sensing technology in a three-dimensional reconstruction task is overcome, and the three-dimensional reconstruction of the surface of an object, especially a diffuse reflection object, is realized. And high-precision and high-integrity three-dimensional shape recovery is realized.

The invention provides a three-dimensional calibration method using a speckle image as a calibration object and combining weight parameters. The method comprises the following steps: acquiring a standard grid image and the speckle image through a camera; determining angular point positions in the standard grid image through an angular point detection algorithm; calibrating the stereoscopic vision system by using the obtained angular points to obtain an initial calibration result; calculating 3D coordinates of the camera calibration board in the world by using the disparity map; determining a subset of the reference image corresponding to the checkerboard angular points in the target image through a digital image cross-correlation algorithm; calculating a weight parameter of each calibration point based on the re-projection error; solving internal and external parameters of the camera by using the weight parameters, radial alignment constraint and a least square method; optimizing the main point of the camera by using the weight parameters; and if the re-projection error or the number of iterations does not meet the requirement, repeatedly calculating the weight parameter of the calibration point and the subsequent steps, otherwise, obtaining a camera calibration result, and obtaining the distortion curved surface of the camera lens.

The invention provides a super-resolution binocular image generation method and system based on geometric structure consistency, and the method comprises the steps: extracting the deep features of a low-resolution binocular image through employing a convolutional neural network or a Transform model, achieving the information interaction of a left image and a right image in combination with a cross attention module, and constructing a pixel incidence matrix of the left image and the right image; acquiring a pixel corresponding relation of the left image and the right image by using the pixel incidence matrix, and constructing a continuous parallax field; performing spatial warping on the deep features based on a continuous parallax field to obtain warping features, and aligning the deep features of the left and right images; and merging the deep features and the warping features after spatial alignment, inputting the merged features into a feature up-sampling module based on implicit two-dimensional expression, and outputting a high-resolution binocular image of the same scene. The invention provides a binocular image super-resolution technology comprising binocular image feature extraction, continuous parallax field construction based on implicit two-dimensional expression, left and right image feature space alignment and feature upsampling based on implicit two-dimensional expression.

The invention discloses an underwater fish body length measurement method, system and device based on binocular vision and a medium, and relates to the technical field of underwater measurement. The method comprises the following steps: performing fish body tracking on a collected fish school video, and determining an ID of each fish and corresponding position information; judging the straightening state based on the tracking frame corresponding to the ID of each fish, and when the judgment result is that the fish body is straightened, selecting a frame with the longest center line as a fish body straightening reference frame; carrying out ROI cutting and masking on the basis of the fish body straightening reference frame to obtain an ROI area map; inputting the left view and the right view in the ROI into a GMFlow model for parallax matching to obtain a parallax image which only contains the characteristics of the whole body of the target fish and the periphery of which forms a black frame; and calculating the length of the fish body according to the coordinates of the central points of the four corners of the fish body in the disparity map and calibration parameters of the binocular camera. According to the invention, efficient and accurate fish body measurement can be realized through step-by-step processing of the fish school image sequence.

The invention provides a road three-dimensional lane line detection method and system based on binocular vision, and relates to the technical field of automatic driving, and the method comprises the steps: carrying out the online calibration of internal and external parameters of a camera and the time sequence alignment of the binocular image and IMU attitude data through collecting the forward binocular image and IMU attitude data of a vehicle; processing the binocular image after time sequence alignment, and determining a dense disparity map; generating a road point cloud based on the dense disparity map and camera parameters, and constructing an adaptive terrain model; constructing a depth enhanced BEV feature map according to the depth feature of the road point cloud and the texture feature of the binocular image, predicting candidate parameters of a three-dimensional lane line, and screening through various constraints; and then executing extended Kalman filtering and trajectory optimization to obtain three-dimensional lane line parameters. According to the invention, rapid and accurate detection of the three-dimensional lane line can be realized, the accuracy of three-dimensional lane line detection under a complex terrain is improved, and the deployment cost is reduced.

The application provides a three-dimensional reconstruction method based on binocular stereo matching, and relates to the technical field of binocular stereo vision. The method combines binocular stereo matching algorithm, triangulation algorithm and surface texture mapping, and can realize surface reconstruction of scene objects in different environments. After stereo calibration is performed on a binocular camera, the internal and external parameters of the binocular camera are obtained, stereo correction is performed, then an image disparity map of the corresponding scene object is generated according to an optimized semi-global stereo matching algorithm, after the disparity information is obtained, the point cloud information converted by the object disparity is triangulated on the surface according to the triangulation algorithm, and finally three-dimensional surface reconstruction of the object is realized through the texture mapping technology. The application can reduce the influence of noise in the disparity map generation process, i.e. the semi-global stereo matching algorithm, improve the accuracy of the disparity information, and complete the three-dimensional surface reconstruction task of the object with little sacrifice of time performance.

The embodiment of the invention discloses an aircraft height measurement method based on binocular vision, and the method comprises the steps: obtaining internal parameters and distortion parameters of a first camera and a second camera, and obtaining a first image and a second image collected by the first camera and the second camera; calibrating the first image and the second image according to the internal reference and the distortion parameter of the first camera and the second camera to obtain a first calibration image and a second calibration image; binocular matching is carried out on the first calibration image and the second calibration image, and a full-image disparity map is obtained based on the first calibration image; performing airport runway semantic segmentation based on the first calibration image to obtain a semantic segmentation map of the airport runway; performing 3D coordinate reduction operation on the 2D pixels according to the full-image disparity map and the semantic segmentation map to obtain a 3D point cloud of the airport runway; fitting a runway plane equation according to the 3D point cloud, converting the runway plane equation to an airframe coordinate system according to the relative position of the first camera and the airframe, and obtaining an aircraft height measurement result according to the runway plane equation under the airframe coordinate system.

The invention relates to the field of binocular stereo vision, and discloses a binocular stereo matching method and system based on multi-scale iterative optimization and related equipment. The method comprises the steps of performing semantic structure feature extraction on a corrected left view and a corrected right view through a feature extraction network; determining a cost space pyramid and a probability matrix according to the left view feature map and the right view feature map output by the feature extraction network; extracting a multi-scale context feature of the left view through a context sensing network, and initializing the multi-scale context feature to obtain a hidden state and input of a recursive network; during recursive network iteration, according to the probability matrix, a multi-range search strategy is adopted to index local cost from the cost space pyramid; and inputting the local cost into the recursive network, combining the hidden state obtained by the context features and the input, and carrying out iterative updating on the parallax value to obtain a parallax map, thereby improving the accuracy of parallax calculation.

The invention discloses a two-dimensional detection and SGBM three-dimensional ranging method based on improved YOLOv13, belongs to the field of computer vision, and particularly relates to the two-dimensional detection and SGBM three-dimensional ranging method based on the improved YOLOv13. The objective of the invention is to solve the problem of low cross-view-angle distance measurement precision in a three-dimensional space perception level of the existing method. The method comprises the following steps of: obtaining a trained YOLOv13-SHSA network model; the binocular camera obtains a left view and a right view; inputting the left view into the trained network model, and outputting three detection results by three detection heads of the trained network model; processing the three detection results to obtain detection frames and confidence coefficients of a plurality of targets in the left view; obtaining a disparity map of the left and right views; calculating a depth value corresponding to each pixel point in the disparity map according to the disparity map and the binocular camera parameters; and calculating the distance of the target object based on the depth map, the detection frames of the multiple targets in the left view and the confidence coefficient.

The invention belongs to the technical field of underwater target space positioning, and discloses an underwater benthos rapid positioning method and system based on binocular vision, and the system comprises an underwater environment data binocular camera collection device which carries out the image enhancement processing of collected image data; inputting the enhanced image into a lightweight Slim-RT-DETR target detection network, and carrying out the detection and recognition of a fishing object; cutting target areas of left and right views of the binocular camera based on a target detection and recognition result, and calculating a parallax value by adopting an anti-noise optimization method of random sampling and neighborhood interpolation; and calculating three-dimensional space coordinates of the fishing object according to the binocular parallax and the re-projection matrix, and positioning the underwater benthos. According to the invention, a rapid image enhancement algorithm and a target detection model suitable for underwater fishing are designed, a normal form method suitable for underwater biological positioning is provided, and high-precision and high-real-time underwater fishing object positioning can be realized.

The invention discloses a three-dimensional reconstruction and pose estimation system and method based on binocular structured light, and the method comprises the steps: obtaining binocular structured light image pairs and attitude angles of a detected part at different preset attitude angles, and synchronously binding the attitude angles with the binocular structured light images; reconstructing dense three-dimensional point clouds of the surface of the measured part in each attitude by utilizing parallax information between binocular images and combining structured light coding and triangulation principles; performing initial pose estimation by using an efficient multi-view algorithm, and performing registration on the dense three-dimensional point cloud under each pose by using a multi-scale geometric constraint ICP algorithm based on an initial pose estimation result and the pose angle to obtain a registered complete point cloud; and geometric completion is carried out on sparse point clouds in the obtained registered complete point clouds through a PCN model, and a global complete three-dimensional point cloud model of the measured part is generated. The method has good precision and robustness in reconstruction of the complex special-shaped part, and the system is light, rapid in deployment and stable in measurement.

The invention relates to the technical field of image processing, and discloses an AI image super-resolution reconstruction method based on a multi-scale fusion mechanism. The AI image super-resolution reconstruction method based on the multi-scale fusion mechanism comprises the following steps: establishing a binocular image system; establishing a double-end collaborative model architecture; establishing a front-end model optimization mechanism; according to the method, through a scene adaptive feature labeling decision model, three-dimensional position driven quantization and spectral response calibration replace manual presetting, and parallax-pose linkage correction is combined, so that subjective interference is thoroughly eliminated, and the misalign error suppression effect is improved by more than 40%; scale intelligent selection and cross-scale feature interaction are introduced into a dynamic interactive multi-scale attention convolutional neural network (DI-MSCNN), and dynamic definition of convolution kernel parameters is matched, so that feature extraction efficiency is improved by 30%-40%, and texture density differences can be accurately adapted; a detail hierarchical perception GAN (DLP-GAN) generates a strategy through hierarchical discrimination and detail partitioning.

Embodiments of the present application relate to a method and system for three-dimensional modeling of a train based on a binocular disparity prediction model, the method comprising: setting installation, acquisition and splicing rules of double parallel linear array cameras; setting a binocular disparity prediction model; acquiring first data set based on the installation, acquisition and splicing rules; training the binocular disparity prediction model based on the first data set; installing the double parallel linear array cameras after the training; when a train passes through the two installed cameras on the current train track, acquiring and splicing images to obtain images I1 and I2, inputting the images I1 and I2 into the binocular disparity prediction model for prediction to obtain a disparity map D 1‑2 , and based on I1, I2 and D 1‑2 , the present application can improve prediction accuracy in high-speed scenes and complex lighting environments, effectively overcome the limitations of a single perspective, and improve the integrity of three-dimensional reconstruction of edge and occluded areas.

A camera parameter automatic optimization system for binocular depth estimation, a true value acquisition module acquires a depth true value map in a simulation environment; an Auto-CAM learning module adopts a deep reinforcement learning framework based on an actor-critic, learns in reverse propagation according to a current camera configuration, a state vector generated by left and right view images and a depth error, so as to optimize camera parameters such as focal length, baseline distance and distortion coefficient; a deployment module outputs an optimal camera parameter configuration under a specific scene, a camera calibration module acquires accurate camera parameters through corner scanning, an image acquisition module acquires RGB binocular images, a depth map prediction module outputs a predicted disparity map by using a stereo matching network algorithm and converts the predicted disparity map into a depth map, and finally, a visual rendering module performs filtering and color processing. The application not only improves the accuracy of depth estimation and the adaptability of the system, but also improves the accuracy and efficiency of camera calibration, optimizes the user experience and visual effect.

The application discloses a kind of environment simulation method and system based on binocular stereo vision measurement, it is related to scene simulation technical field, and its technical solution points are: the method includes by constructing the physical imaging model in left and right optical elements in binocular stereo vision camera, and left and right view is obtained based on the physical imaging model;Determine the disparity map between the left and right view, determine scene depth image based on the disparity map;Three and more scene depth images generated by three and more different binocular stereo vision cameras corresponding to the scene are acquired, and the three and more scene depth images are fused, to construct the three-dimensional simulation model corresponding to the scene based on the depth image after fusion.The application can accurately reflect the depth level and spatial relationship of scene, and the three-dimensional simulation model finally constructed is more realistic, and can strongly support virtual reality, three-dimensional measurement and other applications.

The embodiment of the invention provides a sub-pixel offset optimization parallax calculation method, a sub-pixel offset optimization parallax calculation device and electronic equipment. The sub-pixel offset optimization parallax calculation method comprises the following steps: acquiring a binocular image; labeling a label of a target area of the binocular image based on a preset semantic segmentation network; based on the label, extracting visual features of the target area; establishing a matching relation of the target area according to the visual features; respectively calculating a first sub-pixel offset of a left eye image in the binocular image and a second sub-pixel offset of a right eye image in the binocular image according to the matching relationship of the target areas; and calculating the parallax of the binocular image according to the first sub-pixel offset and the second sub-pixel offset. According to the method, the target area is accurately marked by introducing the semantic segmentation network, and visual feature extraction and sub-pixel offset calculation are combined, so that the precision and robustness of binocular image parallax calculation are remarkably improved.

The application discloses a kind of coal mine underground image stereo matching methods based on threshold and weight Census transformation, comprising the following steps: S1, image information is collected by two monocular camera modules binocular holder;S2, the gray value of all pixels in support window is thresholded;S3, improved center point pixel calculation method obtains matching generation value;S4, improved dynamic cross-domain obtains cost aggregation value;S5, disparity value is obtained using WTA strategy, S6, the overall process is verified on the visual system of underground unmanned auxiliary transport vehicle above-mentioned.The application applies threshold and weight Census transformation method to coal mine underground perception, realizes the autonomous obstacle avoidance and visual reconnaissance function of coal mine underground unmanned auxiliary transport vehicle, reduces the influence of factors such as dust, unstable illumination conditions on stereo matching, improves the accuracy of stereo matching.

This application provides a binocular image matching method, apparatus, device, and medium. The method includes acquiring a binocular image; performing feature extraction processing on the binocular image to obtain multiple feature maps of different scales; fusing the multiple feature maps to obtain multiple fused feature maps; generating multiple cost bodies of different types based on the multiple fused feature maps; aggregating the cost bodies using an hourglass module to obtain a target cost body; merging the multiple cost bodies to obtain a source cost body; generating a first target cost body using an attention module based on the source cost body and the target cost body; aggregating the first target cost body using an hourglass module to obtain a second target cost body; and performing visual regression processing based on the second target cost body to obtain a disparity map. This method can focus on useful information from the cost bodies during binocular image matching, improving the efficiency and quality of disparity map generation.

The invention belongs to the technical field of image processing, and particularly relates to a stereoscopic vision parallax prediction method based on an energy function and an attention fusion network. The objective of the invention is to solve the problem that a real-time algorithm is difficult to obtain high precision in stereoscopic vision parallax prediction. The method comprises the following specific steps: acquiring a data set: training a network model by adopting two public virtual and real stereo matching data sets; the method comprises the following steps of: constructing a network model: constructing an Entry Function and Attention Interference network, and optimizing cost body construction and cost aggregation by utilizing an attention mechanism; designing a minimum loss function; training a network model: inputting the acquired data set into the network model for training; finely adjusting model parameters; and storing model parameters. According to the method, an end-to-end supervised method is adopted, a UNet-like network architecture is integrally used, cost body construction and cost body aggregation are improved, and the efficiency of the model is improved by using an attention mechanism, so that the real-time stereoscopic vision parallax prediction method based on deep learning is realized.

The utility model discloses a passenger car tire wear degree inspection device based on binocular vision, which comprises a tire and an arc-shaped plate, the arc-shaped plate is a dust blocking plate arranged below a fender, an avoiding space is reserved between the arc-shaped plate and the tire, and a binocular vision sensor is arranged between the arc-shaped plate and the tire as shown in the figure. The binocular vision sensor is arranged between the arc-shaped plate and the tire, a gap is reserved between the binocular vision sensor and the tire, abrasion with the tire is avoided, meanwhile, interference with the arc-shaped plate is avoided in the process of damping along with the tire, and after the binocular vision sensor is synchronously fixed to the outer side of the tire and the tire is abraded through the parallax of the binocular vision sensor, the tire can be prevented from being damaged. When the tire is worn, the thickness of the outer side wall of the tire is reduced, the distance between the tire and the binocular vision sensor is increased, the vision difference is reduced at the moment, the wear degree of the tire can be judged according to the value of the vision difference, and corresponding processing is carried out.

The invention relates to the technical field of binocular vision, and discloses a binocular vision system, a soft synchronous acquisition method, computer equipment and a medium. A master camera acquires images at an aligned frame rate, records a timestamp of a current frame and broadcasts the timestamp to a slave camera; and after receiving the timestamp transmitted by the master camera, the slave camera calculates the time difference with the own image acquisition moment, and adjusts the acquisition delay according to the time difference. According to the invention, through the configuration of the master camera and the slave camera, the master camera is responsible for determining the collection frame rate and the time reference, the slave camera is responsible for monitoring the time signal of the master camera, obtaining the frame rate and the timestamp deviation of the master camera and the slave camera, adjusting the collection delay according to the time difference, and then assisting to finely adjust the time in a stepping manner and calculating the binocular vision disparity map. By calculating a Laplace operator and estimating the definition of a disparity map, an effect similar to focusing is achieved, so that a more accurate time difference is obtained, and soft synchronous acquisition of binocular vision is realized.

The present application belongs to the technical field of underwater detection, and particularly relates to a method and device for detecting and grabbing underwater cultured objects. The method comprises: acquiring a left view and a right view photographed by two cameras installed in a parallel binocular form; restoring the left view and the right view; detecting the cultured objects in the restored left view and right view to obtain a target frame of the cultured objects; performing stereo matching on the target frame region of the cultured objects to obtain a disparity of the left view; calculating the size and position of the cultured objects according to the disparity of the left view; and grabbing the cultured objects according to the position and size of the cultured objects. The present application can automatically acquire optical binocular images of underwater scenes, determine the distance and size of various underwater cultured object targets, and implement grabbing, thereby improving the efficiency and intelligent level of aquatic product fishing.

The application discloses a binocular frame generation method and system based on central feature flow, comprising: extracting multi-source features from a current frame monocular low-resolution color image, current frame geometry buffer data and historical prediction image respectively; performing disparity offset prediction and consistency adjustment on left and right eye generation results through a centering operator to align the multi-source features to a central feature coordinate system, forming disparity-consistent central feature flow; inputting the aligned central feature flow of the current frame and the refined central feature flow cached from the previous frame into a recurrent feature network for time series modeling and feature fusion, and outputting the refined central feature flow of the current frame; performing inverse transformation on the refined central feature flow through the centering operator to generate high-resolution left and right eye output frames respectively, and caching the central feature flow of the current frame for next frame processing. The application can significantly reduce rendering calculation amount, improve stereoscopic vision quality and immersion, and is especially suitable for real-time stereoscopic display scenes such as VR / AR.

The invention discloses a binocular stereoscopic vision feature point matching method, a binocular stereoscopic vision feature point matching device, a medium and equipment, and belongs to the field of feature point matching. And then applying a template matching algorithm on the left camera image to obtain a matching result of the feature points. The initial depth is calculated based on the actual length of the feature point, the pixel size and the focal length of the binocular camera, redundancy adjustment is carried out in combination with the minimum bounding ball diameter to obtain a depth range, and then the parallax range is calculated. And determining a cut region of interest of the feature points in the right camera image by using the parallax range, performing template matching in the region, and finally determining the three-dimensional coordinates of the feature points by combining the matching result of the left camera and the right camera and the re-projection matrix. The problem that feature points cannot be accurately and efficiently matched in the prior art is effectively solved.

This application relates to a method for identifying wildfire hazards based on a spatial-temporal correlation operator using visual language priors. The aim is to address the problems of high false alarm rates and difficulty in early identification of concealed fires in vision-based power transmission line wildfire monitoring methods under complex backgrounds. The method constructs a multimodal fusion tensor for the current frame based on visible light and infrared images of the target area. It then uses a visual language prior module to extract features from the multimodal fusion tensor and meteorological data to obtain semantic feature vectors and semantic credibility. Finally, it uses a spatial-temporal correlation module to obtain a spatial-temporal evolution feature vector based on the multimodal fusion tensor, historical time-series cache queue, binocular disparity map, and the semantic credibility. Finally, it uses a spatial-temporal correlation operator to fuse the semantic feature vector, the semantic credibility, and the spatial-temporal evolution feature vector to obtain the probability of wildfire hazard risk.

The embodiment of the invention provides a task execution method and device, electronic equipment and a storage medium, and relates to the technical field of robotics.The method comprises the steps that a view pair collected through a binocular camera is obtained, and a first monocular geometric feature of a left view of the view pair and a second monocular geometric feature of a right view of the view pair are extracted; determining similarity characterization values of the first monocular geometric feature and the second monocular geometric feature under the plurality of different parallax characterization values, and obtaining binocular parallax features including the plurality of different parallax characterization values and the similarity characterization values; taking at least one view in the view pair as a target image, and extracting visual semantic features from the target image; performing feature fusion based on the binocular parallax features and the visual semantic features to obtain fused features; and controlling the robot to execute the task for the task object based on the fusion feature and the task description instruction. According to the scheme provided by the embodiment of the invention, the task execution accuracy can be improved.