393 results about "Rasterisation" patented technology

The invention discloses a Gaussian representation SLAM method based on dense matching priori and factor graph constraint, which comprises the steps of inputting a current image and a key frame image, outputting a point graph corresponding to the image through a pre-trained model, returning a matching condition of two frame image points and respective point cloud information, and obtaining a point-level matching result based on a point-level matching result. The method comprises the following steps: constructing a joint optimization problem of a current frame and a key frame by taking a luminosity consistency error and a geometric projection error as targets, performing joint estimation on a camera pose and a point cloud of the current frame, realizing high-precision pose solution, generating point diagram data after Gaussian scene representation and rasterized rendering processing, and transmitting the point diagram data to a rear end for global optimization. And the rear end receives the pose and point cloud data, executes loopback detection to identify repeated key frames, and performs Gaussian rendering through an optimized key frame image to complete global dense three-dimensional reconstruction. The method effectively solves the problem of track drift and scene inconsistency caused by lack of pose priori and global geometric constraints in an existing system.

The invention provides a lake water quality monitoring method and system based on deep learning, and relates to the field of deep learning. The method comprises the following steps: firstly, collecting original sensor data, remote sensing images, hydrodynamic force and meteorological data, carrying out space-time alignment processing, and constructing a lake surface rasterization space-time diagram; the graph is input into a model composed of a spectrum attention encoder, a graph convolutional network and a time sequence unit, lake global water quality index concentration distribution is predicted, a dynamic threshold value is set based on historical data, the standard exceeding condition is recognized in real time, pollution early warning is triggered, and high-precision monitoring and intelligent management of lake water quality are achieved.

The invention relates to the technical field of unmanned aerial vehicle cooperative tracking, in particular to a multi-unmanned aerial vehicle cooperative tracking method and system based on a complex environment. Comprising the following steps: S1, acquiring image data acquired by each unmanned aerial vehicle and flight position data of each unmanned aerial vehicle, and determining a tracking target, a main tracking unmanned aerial vehicle and an auxiliary unmanned aerial vehicle; s2, establishing an appearance feature library for the tracking target according to the image data; an auxiliary unmanned aerial vehicle checks an environment shielding risk in advance through rasterizing a pre-survey point and simulating a target moving track, when shielding occurs, a stop mechanism is triggered, an auxiliary tracking position is optimized, an optimal position is calculated in combination with a direction weight and a distance threshold value, continuity of a tracking process is ensured, and the tracking efficiency is improved. Meanwhile, when the image feature proportion of the auxiliary unmanned aerial vehicle is higher than that of the main tracking machine, identity replacement is automatically triggered, it is ensured that when the view angle of the main tracking machine is limited, continuous recording of the target can still be kept through the auxiliary unmanned aerial vehicle, and the task reliability in the complex environment is improved.

The invention discloses an island reef shoreline remote sensing extraction method based on a double-branch network, and belongs to the technical field of island reef shoreline remote sensing extraction. Comprising the following steps: S1, acquiring an original satellite remote sensing image of a coral island reef, and cutting to obtain a reef remote sensing image; s2, a shoreline vector label is drawn in the island remote sensing image, and rasterization processing is carried out; s3, cutting the rasterized label and the research area image to obtain tile data; s4, dividing the tile data into a training set and a test set; s5, performing data enhancement on the training set tile data to obtain a processed image data set; and step S6, constructing an SDSPNet model based on the image data set. According to the method, the overall semantic expression ability of the broken shoreline and the lagoon face area is enhanced, and the problems of erroneous judgment of the broken shoreline of the coral island and fuzzy lagoon face boundary in an existing method are solved.

The invention belongs to the technical field of navigation, and relates to an unmanned aerial vehicle multi-target image acquisition path planning method, equipment and a storage medium. The method comprises the following steps: acquiring a city live-action three-dimensional model; rasterizing the building model to create a three-dimensional grid map; acquiring distribution information of obstacles through the live-action three-dimensional model, judging whether the corresponding grid units are occupied by the obstacles or not, and generating an obstacle map according to a judgment result; dividing and storing space nodes in the grid map based on the obstacle map; performing initial path search by calculating a cost function of an initial node; and taking the obtained initial path as the basis of initial pheromone distribution, and obtaining an optimized path by adopting an ant colony optimization algorithm. According to the method, the influence of building shielding on image quality is avoided, time and energy waste of the unmanned aerial vehicle on an unnecessary path is reduced, the target image is obtained in the shortest time and the optimal path, and a flight task is completed.

The invention provides a path planning method, electronic equipment and a computer program product. The intelligent agent realizes path planning based on a pulse neural network of width, and the path planning method comprises the steps that a target area is divided based on environment information of the target area and an optimization target, a rasterized map composed of different sub-areas is determined, the optimization target comprises calculation efficiency and environment adaptability of the intelligent agent, and the rasterized map is determined based on the calculation efficiency and the environment adaptability of the intelligent agent. Each sub-region corresponds to one pulse neural network sub-node, and the rasterized map comprises a starting point position, an ending point position and obstacle information; based on the environment feature vectors of the sub-regions of the rasterized map and the position information of the intelligent agent, the current state of the intelligent agent is determined, and the current state is determined based on the Markov decision process; and based on the current state of the intelligent agent and a path planning algorithm, performing path planning on the rasterized map, and determining a planned path.

The invention discloses a forest pest and disease development trend prediction method and system combined with remote sensing monitoring, and relates to the related field of forestry monitoring data processing, and the method comprises the steps: carrying out the remote sensing monitoring of a target forest, building a remote sensing image data set, calling ground monitoring data, and carrying out the rasterization based on a time step after the interpolation; performing multi-feature joint extraction on the remote sensing image data set, and superposing extraction results to a unified grid to form a multi-layer feature map; constructing a joint feature matrix by using the rasterized data and the multi-layer feature map, and performing pest distribution identification through a classification model; performing pest development trend prediction by taking the pest distribution diagram as a basic feature and taking the combined feature matrix as an additional feature; and according to a development trend prediction result, carrying out pest abnormity early warning. The technical problem of insufficient prediction accuracy caused by insufficient monitoring efficiency and accuracy in existing forest pest development trend prediction is solved, and the technical effects of improving the monitoring efficiency and accuracy and improving the pest prediction accuracy are achieved.

The invention relates to a path planning method and system for navigation guidance of a low-altitude aircraft, and the method comprises the steps: calculating a probability density value of pre-processed prior flight data based on kernel density estimation, mapping the normalized probability density value to a grid map of a rasterized environment model, generating a flight path safety experience distribution map, and carrying out the navigation guidance of the low-altitude aircraft. And expanding the boundary of the obstacle area based on the safety distance constraint matrix, generating a restricted area matrix, generating an initial path according to the bidirectional A *-KDE algorithm cost function, the flight line safety experience distribution map and the restricted area matrix, removing redundant nodes from the initial path, and carrying out smoothing processing to obtain a flight route. The flight route is obtained by using the prior flight data, the deviation between the flight route and the actual navigation path can be reduced, the obstacle area boundary is expanded based on the safety distance constraint matrix, the obstacle safety distance is fully considered, and the risk that the low-altitude aircraft collides with the obstacle can be reduced.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a photovoltaic power station remote sensing identification method and system based on time constraint, and the method comprises the steps: reading image data, and obtaining projection parameters and geographical transformation parameters; processing the image data by a minimum-maximum normalization method; selecting a reference year, acquiring reference year photovoltaic power station vector data processed by a normalization method, converting image data and vector data processed by the reference year normalization method into the same coordinate system according to projection parameters and / or geographical transformation parameters, rasterizing the vector data, and generating a binary mask consistent with the resolution of the image data; and by taking the binary mask as a label, designing a deep learning network structure to train image data, and outputting a predicted binary mask. According to the method, the uncertainty of single-phase image classification is remarkably reduced, and the stability and reliability of a multi-phase classification result are ensured.

The invention relates to a vehicle driving control method, a vehicle and a computing platform, and the method comprises the steps: carrying out the three-dimensional point cloud data collection and all-round view image data collection of an environment where the vehicle is located, so as to obtain a three-dimensional point cloud frame and an aerial view image feature; rasterizing point clouds in the three-dimensional point cloud frame to obtain grid point clouds corresponding to a plurality of grids; determining a target deep learning model according to the distance between the grid and the vehicle; performing feature extraction on the grid point clouds in the grids by adopting a target deep learning model to obtain point cloud features of the grid point clouds; projecting the aerial view image features into a plurality of grids; fusing the aerial view image features and the point cloud features in the same grid to obtain fused features; identifying the fusion feature to obtain a target detection result of the three-dimensional point cloud frame and a classification detection result of the plurality of grids; and controlling the vehicle to run according to the target detection result of the three-dimensional point cloud frame and the classification detection result of the plurality of grids. And the accuracy of target detection is improved.

The invention provides a cross-platform collaborative loading method for CAD drawings, and relates to the technical field of data processing.The cross-platform collaborative loading method comprises the steps that rasterization layering processing is conducted on an original CAD file on a central platform, and a multi-stage grid tile set is obtained; storing to a temporary storage unit by adopting a pyramid hierarchical index; acquiring an updated view state by monitoring a user interaction event in real time, and comparing the updated view state with the original CAD file to obtain view parameters; extracting a target grid tile set; converting into a neutral exchange format set; according to the format support features, scheduling the container format conversion service, and compatibly distributing the container format conversion service to M edge platforms; and performing synchronous rendering replacement of the cross-platform tile file according to the M role permissions. The technical problems that in the prior art, due to the fact that effective CAD file format compatibility processing is lacked, tedious data conversion needs to be carried out among all platforms, and then the accuracy and reliability of data are reduced are solved.

The invention relates to the field of PCB detection, in particular to an AOI detection data processing system and method for a PCB, and the system comprises a visual processing module, a contour recognition module, a PCB alignment module, a region aggregation module and a defect detection module, the visual processing module is used for rasterizing each image pixel point, the contour recognition module is used for detecting a communication contour of the PCB, and the PCB alignment module is used for aligning the PCB alignment module. The PCB alignment module is used for aligning a PCB image with a standard image, the area aggregation module is used for identifying a defect view field and judging a qualified state, and the defect detection module is used for establishing a reinspection path and outputting a block defect rate. Production defects can be found in time, the probability of missing detection and false detection is reduced, the production quality of the PCB is improved, stable operation of an AOI system in a production environment is guaranteed, and the detection efficiency and accuracy are improved.

The invention discloses a large-scale building group layout optimization method based on deep learning. The method comprises the following steps: S1, obtaining a three-dimensional rasterized matrix and a meteorological environment vector of a to-be-optimized building group; s2, on the basis of a pre-trained three-dimensional building layout-wind field mapping network, generating a global wind environment distribution map according to the three-dimensional rasterized matrix and the meteorological environment vector; s3, based on the global wind environment distribution diagram, calculating a building layout parameter gradient through a gradient back propagation optimizer, and generating a layout optimization scheme; and S4, based on the layout optimization scheme, outputting optimized building group space layout parameters. Intelligent optimization of large-scale building group layout is realized through a deep learning technology, and urban wind environment quality and planning and design efficiency are remarkably improved.

The invention discloses a concrete strength detection method based on image recognition. The method comprises the following steps: generating a corrected concrete image data set; outputting a shared feature map set; obtaining a semantic mask set by adopting a rasterized pyramid decoder and a channel attention mechanism; performing microstructure feature statistics based on the semantic mask set to obtain a microstructure index set; outputting a concrete strength predicted value set through a transfer learning and fine tuning regression mechanism; performing incremental updating on the strength prediction network by using a pseudo label-active learning loop mechanism to obtain an updated strength prediction network; and reasoning the corrected concrete image data set by using the updated strength prediction network to generate a concrete strength visualization result map set. According to the method, the interference of a non-intensity correlation noise region on a segmentation result is effectively suppressed, and the accuracy of a microstructure index is remarkably improved.

The invention provides an electric field analysis method. Firstly, a target model is generated through simulation modeling, continuous electric field distribution data is obtained, a first data set and a second data set are established, rasterized image feature extraction is performed on the first data set and the second data set through a graph neural network, corresponding first graph features and second graph features are obtained, and a cascade grid processing network is used to process the electric field distribution data. Firstly, a target power line is predicted according to the first graph feature, then the target power line is converted into a target electric field mode according to the second graph feature, and finally an electric field prediction result is generated and a preset analysis process is executed. According to the method, through the combination of the graph neural network and the cascade grid processing network, the continuity characteristic of electric field distribution is effectively reserved, and meanwhile, the noise problem caused by discretization is avoided. And through rasterized feature data processing, the data demand and the calculation complexity are greatly reduced, and the efficiency and the accuracy of modeling analysis are improved.

According to the intelligent navigation method and device for the inspection robot, a map fusion system is innovatively designed, and the environment is effectively expressed through multi-source data and rasterization processing. And an environment perception mechanism is constructed, and a reliable obstacle recognition model is established in combination with semantic analysis and trajectory prediction. Dynamic planning is introduced, and the accuracy of obstacle avoidance is ensured through interference analysis and state optimization. According to the method, the defects of the traditional technology in the aspects of map fusion, environment perception, dynamic planning and the like are effectively overcome, and technical guarantee is provided for the inspection robot.

The invention provides a document image-text integrated intelligent understanding and processing method and system, and relates to the technical field of artificial intelligence. Comprising the following steps: separating image-text elements and establishing context association; calling a graph vectorization engine, and executing intelligent vector conversion processing on the rasterized illustration; performing hierarchical classification, attribute endowing and structured reconstruction on the vector primitives in combination with text semantic analysis to generate vector entity objects with complete attributes; the text elements and the vector entity objects are jointly input into a pre-trained multi-mode large language model, unified knowledge expression containing vectors, topology, attributes and document contexts is generated, and applications such as question answering, editing, abstracting and reporting of image-text integration are supported. According to the method, the problems of graph semantic loss, difficulty in fine analysis and the like caused by vector-to-grid illustration in papers, reports and other types of documents are solved, the overall understanding depth and interpretability of AI for complex image-text documents are remarkably improved, and high-performance semantic understanding capacity is provided for document-based training and intelligent processing.

The invention relates to the technical field of numerical control machining measurement and correction, and discloses a machining surface correction method for a numerical control milling center. The method comprises the steps of establishing an original point cloud of a processing surface and extracting a feature structure; analyzing each feature structure to form a structured description list; inputting the list into a geometric growth model, and generating a virtual feature structure set according to rule iterative evolution; extracting and rasterizing the contour and the height field, carrying out registration fusion with a reference plane equation, and carrying out variation calculation to obtain a smooth middle geometric surface; performing adaptive resampling on the intermediate surface to generate an intermediate point cloud; and finally, according to the spatial frequency distribution of a topological residual field formed by the difference between the original point cloud and the intermediate point cloud, adaptively determining a weight, and carrying out weighted mixing on the original point cloud and the intermediate point cloud to generate an optimized surface point cloud. According to the method, intelligent evolution of the machined surface features and frequency domain self-adaptive data fusion are achieved, and the correction machining precision and the surface forming quality are improved.

The invention provides a road boundary detection method and a false target identification method and system, and the method comprises the steps: detecting the operation information of a vehicle in real time, and updating the historically accumulated point cloud information based on the motion information of the vehicle; performing screening processing on the point cloud information based on a preset screening rule to obtain final point cloud information; performing rasterized indexing on the final point cloud information based on a preset rasterized indexing rule to obtain a preliminary road boundary point; and based on the self-vehicle motion information and the final point cloud information, carrying out smoothing processing on the initial road boundary point to obtain a final road boundary. According to the method provided by the invention, the technical problem that the road edge identification technology cannot accurately identify the road boundary in special-shaped road boundaries or irregular road scenes, such as forks, roundabout, small roads in villages and the like, is effectively solved.

The invention discloses a new energy station operation index multi-dimensional comparative analysis display method and system, and belongs to the field of new energy station operation management and data analysis. The method comprises the following steps: fusing and standardizing multi-source heterogeneous data, collecting and cleaning multi-station data, and classifying and storing the multi-station data as a standardized data pool; rasterized multi-dimensional indexes are calculated, grids are divided, data are mapped, and grid-level and hierarchical multi-dimensional indexes are generated; performing dynamic weight and TOPSIS evaluation, constructing an evaluation model to calculate the score of a single station, extracting a core index intersection, and transversely comparing the ranking of the stations; the evaluation result is subjected to instantiation storage and hierarchical storage to guarantee data query and tracing; and performing multi-dimensional visual display, displaying the data by multiple terminals, and generating a decision report. The system comprises corresponding function modules. According to the method, the problems of data fragmentation, evaluation system deficiency, non-visual display and the like are solved, multi-station data integration, quantitative evaluation and visual decision making are realized, and the operation management efficiency is improved.

The invention relates to the technical field of artificial intelligence and finance, and provides an artificial intelligence-based text wordart method, device, equipment and medium, on one hand, a target text is rasterized based on a text wordart model to obtain a target raster image text, a vector text layer is converted into a pixel layer, and the pixel layer is converted into a pixel layer; the text is not limited by language types, the problem that the Chinese language structure is complex is avoided, and the text-to-graph problem is converted into the graph-to-graph problem; on one hand, a large language model is used for recognizing regional coordinates used for semantization in raster image characters and expanding style cue words, and the controllability degree of text art generation is improved; and on the other hand, the discriminator is utilized to guide the diffusion model to generate a font which can be obviously recognized, and meanwhile, through an adversarial training mechanism, the generated wordart has the advantages of clear font recognition and artistic semantic change, so that the style and the font are highly fused and balanced, and the problems of low efficiency and poor quality of the text wordart are solved.

The invention discloses a rural residential area automatic layout method based on shape grammar and multi-objective optimization, and the method comprises the steps: firstly constructing a rasterized construction cost surface based on the slope data of a research area, a basic farmland protection area vector and an ecological protection red line vector, and then constructing a rasterized construction cost surface based on a traditional residential building sample library and survey data; a shape grammar rule base including a building foundation, courtyard topology, residence number configuration, style and feature control and public service facility coverage is constructed, rule parameters are iteratively optimized by taking a land intensification degree, an ecological interference degree, a style and feature coordination degree and a service coverage degree as optimization targets, an optimal solution set is obtained, and finally, a construction cost surface is taken as a guidance, so that the construction cost is optimized. Performing layout generation in the low-cost constructable area according to the optimal solution generation; according to the technical scheme provided by the invention, a high-implementability rural residential area layout scheme with intensive land utilization, ecological protection and regional style and appearance coordination can be generated under various natural and policy constraint conditions, and the layout generation efficiency is improved.

The present invention provides an adaptive texture atlas compression system and method based on the Unity engine, which relates to the field of graphics processing. It includes a texture resource import module that converts PNG to DYN files and generates configuration data. The texture resource setting module adopts a double-buffer configuration management mechanism, detects hash value differences through an improved CityHash algorithm, and asynchronously synchronizes changed fields. The atlas construction module performs edge detection, polygon generation, and rasterization processing, and outputs pixel data using the active edge table algorithm. The layout unit realizes mutually exclusive layout of pixel data based on multi-threaded parallel computing and outputs a UV coordinate mapping table. The atlas setting module dynamically modifies configuration parameters and triggers pipeline updates. Geometric accuracy is ensured by verifying the number of vertices and texture area, and the expansion offset and step parameters are dynamically adjusted to adapt to different resolutions. An integrated breakpoint resumption mechanism is used to ensure the integrity of transaction logs.

The invention discloses a method for planning a road between an upper reservoir and a lower reservoir of a pumped storage power station based on an improved A * algorithm, and the method comprises the steps: S1, carrying out the rasterization of three-dimensional terrain point cloud data of a target region of the pumped storage power station, and generating a digital elevation model (DEM); s2, identifying a natural obstacle area in the target area, and generating an impassable area matrix map; s3, constructing a heuristic function comprehensively considering distance and gradient, an enhanced cost function and a multilayer neighborhood search strategy, performing path search based on an improved A * algorithm, and constructing an initial path; s4, performing optimization processing on the initial path to obtain a final path; and S5, superposing the optimized final path to an original terrain grid map for visualization, calculating and displaying key design indexes of the path, and assisting a designer to perform scheme comparison and selection and engineering decision. The method is high in intelligent degree, high in engineering adaptability and suitable for rapid line selection and optimization design of the upper and lower reservoir roads of the pumped storage power station in the complex mountainous area.

Adaptive rastering is disclosed. In one example, compositing data is received via a network from a surrogate browser, at an endpoint browser. At least a portion of a display list of the compositing data is transformed into a DOM tree comprising at least one semantic DOM element. The semantic DOM element is induced to be rendered by the endpoint browser. In another example, at least a portion of the display list is transformed into at least one of a plurality of rasterization targets. The at least one of the plurality of rasterization targets is induced to be rendered by the endpoint browser.

The invention discloses a visual display method and system for a three-dimensional simulation scene and a storage medium, and belongs to the technical field of computer simulation, and the simulation method comprises the steps: carrying out ground object mapping on GIS data and the three-dimensional scene; a three-dimensional simulation scene is constructed according to the mapped GIS data, and the three-dimensional simulation scene comprises a rasterized three-dimensional geographic scene and simulation objects constructed in a multi-level mode; observation starting points are selected in the three-dimensional simulation scene, visible spaces corresponding to different observation starting points are obtained, and the visible spaces comprise visible areas and invisible areas; carrying out dynamic visual display on a visual area and an invisible area of the selected observation starting point, and outputting a visual analysis result containing a shielding analysis result; and analyzing communication transmission attenuation based on the invisible area, and outputting a communication analysis result, thereby solving the problems of large resource consumption, poor display continuity and insufficient occlusion processing capability in a large-calculation-amount scene in the prior art.

The invention relates to the technical field of computer aided design, in particular to a BIM family library construction method and system for tile construction parameters, and the method comprises the following steps: dividing a two-dimensional paving region into discrete grid units based on the geometric shape and obstacle coordinates of a to-be-paved region input by a user, and distributing a unique position identifier for each grid unit; according to the method, discrete grid division is carried out in the two-dimensional paving area according to the geometrical shape and the obstacle coordinate, and the unique position identifier is distributed, so that the original abstract paving area can be converted into a rasterized paving map capable of being positioned and analyzed; and binding probability distribution characteristic values of ceramic tile sizes, material unit prices, labor efficiency and material loss rates with the grids to realize a visual corresponding relationship between construction environment parameters and spatial positions. In the path generation process, through iterative calculation from the starting grid to the ending grid and total cost minimization selection, the construction sequence and time prediction are more accurate.

The invention discloses an indoor point cloud wall face segmentation method based on density constraint super voxels, and belongs to the technical field of point cloud data processing and three-dimensional space data analysis, and the method comprises the following steps: collecting point clouds, removing invalid points, dividing structural point clouds through a normal vector and a gravity direction pair included angle, and obtaining an included angle; the method comprises the following steps: obtaining a primary wall point cloud set through rasterization and combined constraint of density constraint and height constraint, optimizing super voxel segmentation by adopting an energy function, completing initialization, seed screening and adjacency relation construction, performing iterative growth and traversal screening by taking a super voxel with the minimum curvature as a seed to obtain a super voxel cluster, and obtaining an initial wall point cloud set; and finally, outputting a complete wall point cloud through binary mask expansion, label updating and point cloud elimination. According to the method, the phenomena of wrong segmentation and missing segmentation can be effectively inhibited in a complex indoor scene, and the method is suitable for point cloud wall segmentation of a multi-wall and multi-scale structure.

The invention provides a low-light road perception and topological reasoning method and system based on track prior, and the method carries out the enhancement processing of a low-light image through an image pre-training model, thereby restoring the details of the image, improving the brightness of the image, and reducing the distortion phenomenon of the image in the enhancement process. Moreover, crowdsourcing trajectory data is effectively utilized as prior information, and the rasterized thermodynamic diagram and vectorized trajectory information are constructed in combination with the trajectory prior data, so that the accuracy and robustness of the map construction process are enhanced, and the difficulty and cost of obtaining the prior information are reduced. According to the scheme, the capability of dealing with an automatic driving scene under the conditions of low illumination and complex illumination can be effectively improved, and the road perception and topological reasoning precision is improved.

The embodiment of the invention provides a target detection method and device, a controller and automatic driving equipment, and the method comprises the steps: obtaining a multi-view image of a target region where the automatic driving equipment is located, and corresponding camera parameters, and the target region comprises a to-be-detected target object; performing feature extraction on the multi-view image, fusing camera parameters to obtain a three-dimensional feature image of the target area, performing rasterized semantic segmentation and offset prediction on the three-dimensional feature image to respectively obtain a grid mask image and offset information, and performing image segmentation on the grid mask image and the offset information; the grid mask image is used for indicating information of a target grid occupied by the target object in each grid of the three-dimensional feature image, and the offset information is used for indicating position offset information of the target object in the target grid; and determining the position information of the target object according to the offset information and the information of the target grid in the grid mask image. By adopting the scheme, the obtained position information error of the target object is smaller, and the position detection result of the target object is more accurate.