53 results about "Orthophoto" patented technology

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention discloses an asphalt pavement disease identification method based on unmanned aerial vehicle scanning, and relates to the field of asphalt pavement disease identification, and the method comprises the steps: employing an unmanned aerial vehicle to carry a multispectral imager, obtaining an orthoimage of a whole road segment through a preset route, and carrying out the preliminary feature extraction and suspected region identification; a flight path is optimized according to a suspicion degree and spatial distribution by intelligently planning a recheck route for a suspicion area, and spectral reflectivity data of a specific wave band is collected for secondary verification; color features, glossiness features and texture features are explicitly extracted for a target asphalt pavement area, quantitative extraction is carried out on disease related physical features through brightness and saturation distribution, highlight area detection, texture structure analysis and other means, the accuracy and reliability of oil flooding disease recognition are improved, meanwhile, the disease degree grade is generated, and the recognition efficiency is improved. And accurate data support is provided for asphalt pavement maintenance decision making.

The invention discloses an unmanned aerial vehicle multispectral system error calibration and correction method based on a Boolean model, and belongs to the technical field of photogrammetry and remote sensing. Comprising the following steps: acquiring a multispectral image, acquiring original POS data, and performing band registration preprocessing on the image to generate a first orthoimage to confirm a system error magnitude; carrying out bundle method strict adjustment by using ground control points to obtain reference POS data, and solving seven-parameter conversion parameters; correcting the original POS data by using seven parameters and generating a high-precision orthoimage; and generating a simplified multispectral camera calibration and verification formula according to the difference value of the POS data before and after correction. Complex coordinate conversion is simplified into fixed value addition and subtraction operation, one-button calibration and batch correction of system errors of the multi-spectral camera of the unmanned aerial vehicle are achieved, the positioning precision is improved to the centimeter level from the meter level, dependence on ground control points is greatly reduced, and an efficient standardized solution is provided for large-scale engineering data processing.

The invention provides a rice leaf area index inversion method based on an unmanned aerial vehicle, and relates to the technical field of smart agriculture, and the method comprises the steps: S1, obtaining an RGB image of a to-be-detected rice canopy, a training feature set, and a corresponding rice leaf area index measured value; s2, processing the RGB image of the rice canopy to be measured to generate a digital orthoimage and a digital earth surface model; s3, generating spectral features, height features and texture features based on at least one of the RGB image, the digital orthoimage and the digital earth surface model of the rice canopy to be measured; s4, training a machine learning model by using the training feature set and the corresponding rice leaf area index measured value to obtain a rice leaf area index inversion model; and S5, inputting the spectral features, the height features and the texture features into a rice leaf area index inversion model to obtain a leaf area index prediction value of the rice canopy to be detected. And rice leaf area index inversion is carried out only by using the RGB sensor to collect images.

The invention discloses a tree positioning calculation method based on unmanned aerial vehicle remote sensing, and relates to the technical field of agriculture and forestry vegetation positioning. According to the method, firstly, a digital orthoimage and a digital earth surface model (DSM) are generated through aerial photography of an unmanned aerial vehicle, a crown mask is obtained by using an instance segmentation technology, and a trunk position is accurately positioned by calculating a fusion score in the mask by using a joint resolving mechanism of a DSM local peak domain and an iterative corrosion morphological core. The method improves the positioning precision and efficiency through a DSM highest point * iterative corrosion combined optimization strategy, and improves the positioning stability of dense crowns through the corrosion depth constraint space position. According to the method, the positioning precision and efficiency of a complex forest environment are remarkably improved, and the problems of insufficient positioning precision, difficulty in positioning trunks under dense crowns and the like in the existing unmanned aerial vehicle remote sensing technology are solved.

The present invention relates to the fields of computer vision, image processing, etc., and provides a seam line network generation method and apparatus, an electronic device, and a medium. The specific implementation solution comprises: determining respective effective areas of a plurality of orthophotos to be mosaicked; performing mosaicking processing multiple times on the basis of the respective effective areas of the plurality of orthophotos to obtain a target effective polygon group; and generating a seam line network for the plurality of orthophotos on the basis of the target effective polygon group.

The invention discloses a map conversion method and apparatus. The method comprises the steps of obtaining original data of unmanned aerial vehicle patrol flight; performing conversion processing on the coordinate data to obtain target coordinate data, and performing correction processing on the original image according to the target coordinate data and the flight attitude parameters to obtain an orthoimage; performing feature preprocessing on the orthoimage to obtain target vector data and target raster data; carrying out topological relation map construction according to the target vector data and the target raster data to obtain electronic map data; performing error correction on the electronic map data according to the high-precision reference map to obtain a corrected electronic map; and carrying out data integration on the corrected electronic map and the electronic map data to obtain a target electronic map. Therefore, by integrating the corrected electronic map data and the uncorrected electronic map data, the integrity and consistency of the data can be further improved, and the precision of the generated final electronic map is ensured to be higher.

The application discloses a kind of unmanned aerial vehicle autonomous inspection orthographic image generation method, it is related to unmanned aerial vehicle surveying and mapping and autonomous navigation technical field, the path optimization strategy of the present application is fused by genetic algorithm, global optimization is realized to route in combination with the image parameter constraint of inspection area grid feature, with candidate navigation point as node, through the smoothness factor of navigation section, redundant navigation point is filtered, and the complexity of route is reduced, genetic algorithm is constrained with flight height, navigation point spacing, constructs fitness function including flight distance, number of turns, overlap rate, finds global optimal path by population iteration, optimizes and balances multi-objective demand, ensures that route meets image acquisition accuracy requirement, can also shorten flight distance, reduce number of turns, adapt to the endurance of unmanned aerial vehicle, guarantee that inspection task is efficiently promoted, simultaneously, the navigation point coordinates of simulation output are directly adapted to flight control system, ensure that actual flight parameter is consistent with planning parameter.

The invention provides a bird's-eye view orthographic image generation method and device and a computer readable storage medium. The method comprises the following steps: generating a three-dimensional ground triangulation network model according to three-dimensional ground point cloud data; penetrating dotting is conducted on the three-dimensional ground triangulation network model, multi-layer tile data are generated, the position information of tiles in the multi-layer tile data comprises row numbers, column numbers and elevation values, the row numbers and the column numbers indicate the horizontal positions of the tiles, and the elevation values indicate the vertical heights of the tiles; and according to the multi-layer tile data, generating a hierarchical aerial view orthographic image corresponding to the elevation data of the track points. Through the three-dimensional ground triangulation network model, penetrating dotting is carried out, the multi-layer tile data including the row number, the column number and the elevation value can be generated, the information capable of covering the multi-layer road surface can be generated, the aerial view orthographic image of the level corresponding to the elevation data of the track point can be generated according to the multi-layer tile data, and the aerial view orthographic image can be conveniently displayed to a user.

This invention relates to the field of data analysis technology, and more specifically, to a method for identifying forest pests and diseases based on data analysis. The method includes: Step 1: acquiring aerial orthophotos of the target forest area and counting trapping points within a set time interval, and associating the trapping counts with the nearest-nearest-assigned sample area units; Step 2: constructing a complex simplex on a forest map grid, consisting of a patch layer, a sample area layer, and a trapping layer, and outputting suspected pest and disease triggering units after filtering out negative examples using a two-stage verifier; Step 3: establishing a pest and disease map database, describing typical patterns using disease type topological fingerprints; performing similarity assessment between the topological evidence package and the disease type topological fingerprints to generate disease type, extent, and confidence level, and labeling the data in layers on the forest map grid. This invention can capture fine-grained structural changes in pests and diseases at an early stage, and improves identification accuracy and interpretability by utilizing multi-source evidence synthesis.

This invention discloses a method and system for identifying apparent damage to bridge concrete based on texture analysis, belonging to the interdisciplinary field of bridge engineering inspection and computer vision technology. The method includes: acquiring surface images of bridge components using a drone and generating orthophotos; extracting contrast, correlation, and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on local binary patterns; detecting corrosion and seepage areas based on the HSV color space; fusing multi-dimensional features and inputting them into a multi-label classifier, simultaneously outputting pixel-level segmentation masks for multiple types of damage such as cracks, spalling, exposed rebar corrosion, and seepage; generating a damage level score and maintenance recommendation report according to bridge technical condition assessment standards. This invention integrates complementary texture features to achieve simultaneous identification of multiple types of damage, outputs pixel-level segmentation results, and automatically generates an assessment report that conforms to engineering specifications.

This invention relates to a method and medium for detecting building changes based on UAV imagery. By using UAV images of the same target area acquired at different times, the method incorporates UAV ground control points and aerial POS information into an aerial triangulation system to jointly calculate the camera's exterior orientation elements and imaging geometry. Orthophotos of the preceding and following times are generated under a unified coordinate reference, and building probability maps of the preceding and following times are obtained through processing. The merged orthophoto obtained after combining the preceding and following times UAV images is used to construct multiple object units based on cross-temporal consistency. Furthermore, the average first and second semantic confidence scores corresponding to the preceding and following times orthophotos are obtained. Based on whether the two average confidence scores meet three preset judgment conditions, each object unit is determined and labeled as belonging to a specific type of building change area. Thus, stable and accurate detection of building changes based on UAV imagery is achieved.

This invention discloses a method, system, and storage medium for LOD2 building reconstruction from single-view orthophotos. The method inputs a single-view orthophoto remote sensing image into a shared backbone network to generate a footprint mask probability map; constructs a first modulation factor, performs element-wise gating on the multi-scale feature map, and inputs it into a height decoder to output a height field; normalizes the height field to obtain a normalized height map and constructs a second modulation factor; fuses the second modulation factor with the first modulation factor to form a composite modulation factor, performs dual element-wise gating on the multi-scale feature map, and inputs it into a structure line decoder to output a distance field and orientation field; recovers the roof structure line based on the distance field and orientation field; and constructs the roof surface, walls, and ground by combining the roof structure line, height field, and footprint boundary to generate a LOD2 3D model of the building. This invention can automatically generate a topologically complete and geometrically accurate LOD2 3D model relying solely on a single orthophoto.

The invention relates to the technical field of building detection, and provides a house indoor defect intelligent detection and graphic file output method and system. The method comprises the following steps: acquiring millimeter-level three-dimensional point cloud data through handheld SLAM laser scanning, generating an orthoimage after preprocessing, carrying out defect identification and classification by adopting an unsupervised-weak supervised neural network in which a MobileNetV3-SOM and a stacked auto-encoder are connected in series, calculating three-dimensional coordinates and quantization parameters of a defect center through pixel-point cloud back projection, and carrying out defect identification and classification on the defect center. And finally generating a CAD vector file containing defect types, three-dimensional coordinates and quantization parameters. According to the method, only normal wall surface sample training is needed, the problem of insufficient model generalization caused by defect sample scarcity is solved, meanwhile, accurate three-dimensional positioning and complete parameter description of defects are achieved, the output standardized CAD file can be directly used for repairing design, the detection efficiency is improved, and the application range is widened.

A method for classifying disturbing plants using drone orthophotos is disclosed, and the method for classifying disturbing plants using drone orthophotos may include: (a) forming a drone orthophoto in which a target area is shown during a preset period; and (b) determining a candidate object of a disturbing plant from the drone orthophoto as the disturbing plant.

This application discloses a multi-mode fusion method and system for calculating canopy height in fields and orchards, belonging to the field of agricultural remote sensing and precision agriculture monitoring technology. It utilizes an unmanned aerial vehicle (UAV) platform equipped with a lidar sensor, a multispectral sensor, and a pose acquisition module to simultaneously collect lidar point cloud data, multispectral image data, and timestamped pose data. Based on the pose data, spatial registration and stitching are performed on the point cloud data to generate a 3D point cloud model of the crop; geometric correction and stitching are performed on the multispectral image data to generate a multispectral orthophoto. The two are unified to the same geographic coordinate system to establish a spatial mapping relationship; spectral indices are calculated based on the multispectral orthophoto to identify land cover categories and regions; corresponding subsets of the point cloud are extracted based on the spatial mapping relationship to generate a digital surface model and a digital ground model; and canopy height is calculated by the difference. This application integrates multi-source data to achieve automated and high-precision calculation of canopy height for crops such as corn and fruit trees, and has strong applicability.

This invention discloses a method and related apparatus for identifying and calculating the excavation progress of cable trenches in photovoltaic power plants, belonging to the field of photovoltaic power generation technology. The method acquires orthophotos of the photovoltaic power plant and locates the cable trench; obtains point cloud data of the cable trench based on its location; performs plane fitting using the point cloud data to obtain the target plane of the cable trench; and compares the target plane with the original design drawings to obtain the excavation progress data of the cable trench. This invention can acquire cable trench excavation progress data in real time, providing timely and accurate information support for the construction and management of photovoltaic power plants. Furthermore, this method has a certain degree of flexibility and can adapt to the needs of cable trench excavation progress identification in photovoltaic power plants of different scales and types.

This invention discloses an intelligent monitoring and early warning method for small urban water bodies based on visible light drones, belonging to the field of water quality prediction and management technology. The method acquires water body images using a drone equipped with a visible light sensor, automatically stitches them together to generate orthophotos, automatically segments water body areas and outputs boundary vector layers, extracts band DN values ​​and constructs a water quality parameter feature dataset through normalization, band combination, and chromaticity angle calculation, uses a machine learning model to invert chlorophyll a concentration, turbidity, and phycocyanin concentration, and optimizes the model accuracy by combining measured data, performs statistical analysis and future trend prediction on the inversion results, and generates thematic maps, completes water quality evaluation according to standards, and automatically outputs reports and pushes them to relevant units when early warning conditions are met. This invention uses low-cost visible light equipment to achieve fully automated intelligent monitoring, effectively improving inversion accuracy and regulatory efficiency, and adapting to the needs of large-scale, routine monitoring and early warning of small urban water bodies.

This invention discloses a method, device, and medium for drone flight path planning, relating to the field of photovoltaic cleaning. The method includes: first, acquiring a single image of the area of ​​the component to be cleaned and a LiDAR point cloud, then stitching them together to output an orthophoto; rasterizing the point cloud to generate a two-dimensional elevation mean model, and extracting component edges and identifying corner points from the image; using geometric fusion to project the corner points onto the model to obtain preliminary three-dimensional coordinates of the corner points, which are then optimized through principal component analysis; next, clustering and fusing the corner points from multiple images, overlaying them with the optimized coordinates onto the orthophoto for verification, forming a set of regional corner point coordinates and grouping them; finally, generating a two-dimensional flight path based on this and the flight path spacing, explicitly specifying the absolute elevation to obtain the target cleaning flight path. This method integrates multi-source data, utilizes single images for precise corner point identification, employs LiDAR point clouds to improve positioning accuracy, and relies on orthophotos to ensure complete global analysis. The generated flight path has high accuracy and strong adaptability, accurately guiding drone cleaning and improving efficiency and quality.

The invention discloses a fruit tree canopy extraction method used in an orchard full grass covering scene. The method comprises the following steps: S1, processing a collected orchard remote sensing image to obtain a digital orthoimage and a digital surface model image; s2, cutting and unifying the resolution; s3, screening based on the measured fruit tree height threshold to obtain a digital surface model image with effective pixels reserved; s4, performing interactive separation on the digital surface model image which is provided in the step S3 and retains the effective pixels and the digital orthoimage provided in the step S2 to obtain a preliminarily extracted fruit tree canopy and a digital orthoimage after interactive separation; s5, converting R, G and B channels of the digital orthoimage after interactive segmentation into L *, a * and b * channels by using a CIELAB color space, and determining a fruit tree color threshold value; and S6, screening based on a fruit tree color threshold value to obtain an accurately extracted fruit tree canopy. The method can improve the extraction precision in the orchard full grass covering scene, and has universality.

This invention discloses a method for constructing urban pipeline simulation data based on intelligent remote sensing interpretation, relating to the technical fields of intelligent remote sensing image interpretation and rapid generation of basic urban geographic information data. The method includes: acquiring original orthophotos of the city; preprocessing the original orthophotos; annotating the preprocessed orthophotos according to road contours using image annotation tools, and pre-creating sample training and validation sets; optimizing the encoding and decoding structures in the original U-Net model to establish an urban pipeline simulation data construction model and completing model training; extracting road lines using the trained model; segmenting the road lines; and assigning attributes to the segmented points and lines to complete the generation of pipeline data. This invention achieves rapid generation of urban pipeline network data by intelligently and automatically extracting roads from remote sensing images and constructing pipeline simulation data based on the extracted road data, providing pipeline simulation data under specific conditions.

This invention provides a method for identifying the severity of walnut leaf scorch based on UAV multispectral imagery, comprising the following steps: S1. Acquiring UAV multispectral orthophotos of the walnut canopy in the target area; S2. Extracting vegetation index features based on the multispectral orthophotos, and filtering the extracted vegetation index features using a minimum redundancy maximum correlation algorithm; S3. Inputting the filtered vegetation index features into a pre-trained model for identifying the severity of walnut leaf scorch, and outputting the severity level of walnut leaf scorch in the target area; wherein, the pre-trained model for identifying the severity of walnut leaf scorch is based on a sample dataset containing labels for the severity level of walnut leaf scorch and trained using a random forest algorithm. This invention solves the technical problems in existing technologies for walnut leaf scorch, such as the lack of available remote sensing data sources and insufficient classification accuracy and generalization ability of monitoring models due to weak correlation or redundancy of feature variables.

This invention discloses a method, system, platform, and storage medium for generating and processing tunnel orthophotos based on global constraint optimization and contour refinement distance determination. It generates and acquires first data corresponding to the tunnel; based on a longitudinal geometric reference with known physical spacing, it uses a global constraint optimization method to longitudinally calibrate the first data, generating corresponding second data; it then refines the contour distance determination for each section in the second data, generating corresponding third data. This method overcomes the limitations of local correction in dynamic filtering, achieving global compensation for longitudinal mileage errors and improving the geometric consistency of long-distance tunnel images. It accurately extracts the main lining contour in complex sections, effectively eliminating interference from auxiliary structures such as pipelines and cable troughs, ensuring that the distance division is based on the actual tunnel wall. Based on the distance division, it achieves adaptive grayscale equalization, compensating for intensity unevenness caused by changes in laser incident angle, point density, and curvature, thus improving image visual quality and detail recognition.

The invention relates to the technical field of iconography, in particular to an orthophoto map generation method and system. The method comprises the following steps: preprocessing an acquired initial orthoimage, and constructing a preprocessed orthoimage set; according to the method, an overlapping updating formula is provided, and pixel value updating is carried out on pixel points with overlapping areas in a pre-processing orthoimage set; meanwhile, the missing area is collected again, and a to-be-spliced orthoimage set is obtained; the method comprises the following steps: splicing each orthoimage in a to-be-spliced orthoimage set according to position coordinates to generate a regional orthoimage map; thirdly, inputting the regional orthoimage into a forest segmentation detection model to generate a forest regional image set; and finally, extracting a forest coverage feature vector of each image in the forest region image set, and generating a region forest coverage detection value according to the forest coverage feature vectors. According to the ortho-image map generation method and system, the accuracy of forest coverage detection can be improved.

The utility model discloses a TBM rock slag catalog image automatic acquisition device based on high-speed camera shooting. The TBM rock slag catalog image automatic acquisition device comprises a TBM cutter head, a TBM slag discharging belt conveyor, a high-speed camera and a camera fixing support. A TBM cutterhead capable of achieving rotary cutting is installed at the end of the TBM deslagging belt conveyor, the camera fixing support is installed on the side, close to the TBM cutterhead, of the TBM deslagging belt conveyor, and the high-speed camera is installed on the camera fixing support, arranged above the starting end of the TBM deslagging belt conveyor and used for collecting orthoimages of rock slag on the TBM deslagging belt conveyor in real time. Rock slag image data are collected in real time through the high-speed camera, manual intervention is not needed, the data collecting and processing time is greatly shortened, meanwhile, the device can work continuously, the idle time of manual operation is effectively shortened, massive training samples are accumulated for rock slag image recognition and geological analysis, the precision of a machine learning model is improved, and the working efficiency is improved. And intelligence and automation of geological logging of the rock slag are promoted.

The application discloses a kind of urban orthographic image map identification fusion methods, including the following steps, first, initial identification, form starting seed, second, multiple overlap area fusion, form fusion seed, third, inflation fusion processing, form secondary fusion seed, fourth, form target seed. The embodiment of the application can identify the complete target object.

This invention discloses a remote sensing-based method for estimating farmland slope, relating to the field of farmland slope technology. The method includes the following steps: acquiring image data and coarse-resolution digital elevation model (DEM) data; processing the image data to obtain a digital terrain model and orthophotos; visually interpreting the orthophotos to extract farmland distribution and shape indices; extracting topographic factors based on the DEM and filtering key factors using the variance inflation factor method; weighting the key factors using the entropy weighting method to obtain a comprehensive membership degree, combining a threshold to filter target slope points and overlaying farmland to obtain a preliminary farmland slope; performing a weighted average of the coarse-resolution DEM data to obtain the topographic slope; calculating the main slope aspect correction coefficient using the K-means clustering method on the DEM; fitting the preliminary farmland slope, topographic slope, main slope aspect correction coefficient, and shape index using a power function model to obtain a farmland slope estimation model, thus achieving the estimation of farmland slope.

The invention discloses a forest wild animal habitat evaluation and ecological corridor planning method and a forest wild animal habitat evaluation and ecological corridor planning system. Relates to the technical field of ecological protection and forestry information. The method comprises the following steps: acquiring a forest point cloud and a multispectral image by using an unmanned aerial vehicle carrying a laser radar and a multispectral camera; preprocessing to generate a digital elevation model, a vegetation canopy height model and an orthoimage; generating a land utilization classification map based on the image and a canopy height model, and calculating a landscape index; generating a habitat suitability grid map by using a pre-trained multi-modal deep neural network; identifying core habitat plaques through spatial overlay analysis; constructing a landscape map taking the habitat plaques as nodes, performing connectivity analysis by using a pre-trained map attention network, constructing a dynamic resistance surface, and determining a key ecological corridor; and combining the high-resolution image to identify the breaking point, and outputting an ecological corridor planning scheme. According to the invention, high-precision habitat evaluation and data-driven corridor planning can be realized.

This invention provides a method for extracting surface cracks in mining areas based on multi-scale global fusion, relating to the field of surface crack extraction. Specifically, it includes the following steps: acquiring orthophotos of the mining area from a UAV; constructing a multi-scale feature perception module to capture multi-scale crack textures and suppress complex background interference; constructing a token selection context module to perform channel linear modulation on deep features and fuse local details with global semantics; designing a feature fusion module based on Fourier transform to reduce background noise and enhance edge contrast through linear combination in the frequency domain; and using an image segmentation network model to infer from the UAV imagery and output segmentation results containing crack regions. The technical solution of this invention overcomes the problems existing in the prior art when identifying cracks in high-resolution orthophotos of complex mining scenes from UAVs, such as inaccurate segmentation due to similar textures to cracks, discontinuous edges of slender cracks, and incomplete extraction of connected structures.

The application discloses a kind of mountain pipeline digital twin system construction method, system, equipment and medium, it is related to pipeline engineering construction technical field, the method includes: obtaining digital orthophoto map data, digital elevation model data and pipeline asset data, the pipeline asset data includes line asset data and station yard asset data;According to the digital orthophoto map data and the digital elevation model data, mountain model is constructed;According to the pipeline asset data, line model is constructed;According to the station yard asset data, station yard model is constructed;The mountain model, the line model and the station yard model are rendered in three dimensions, and mountain pipeline digital twin system is obtained.The mountain model and line model constructed by the application are easy to modify and replace, and the mountain pipeline digital twin system rendered has high precision, can simulate and simulate the position, direction and surrounding mountain information of mountain pipeline in all directions, has short construction period and low investment cost.