610 results about "Feature descriptor" patented technology

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

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

The invention provides a modified metal surface defect detection method and device and a medium, and the method comprises the steps: obtaining a multi-angle reflection image sequence of a modified metal surface under the irradiation of a multi-spectral light source, and generating a defect sensitive parameter set based on a preset modified metal material characteristic database and in combination with the spectral reflectivity distribution information of the multi-angle reflection image sequence; performing cooperative feature enhancement on the multi-angle reflection image sequence and the defect sensitive parameter set, and enhancing the feature contrast of a defect area and a normal area through weight distribution to obtain an enhanced defect feature set; joint anomaly detection of a spatial domain and a spectral domain is carried out on the enhanced defect feature set, a potential defect region of the modified metal surface is obtained through identification, and a defect region feature descriptor is generated; and performing defect morphological quantitative analysis according to the defect region feature descriptors, and determining the type, position and severity level of the modified metal surface defect. According to the invention, the comprehensiveness and reliability of a defect detection result can be improved.

The invention relates to the technical field of target detection, and discloses a pet target detection method and device and a camera, and the method comprises the steps: carrying out the motion triggering collection and image enhancement preprocessing of a front end region of a feeder, and obtaining an enhanced image frame sequence; performing feature extraction of dynamic receptive field adjustment on the enhanced image frame sequence to obtain pet feature descriptors and position information; behavior time sequence feature analysis is executed, and pet behavior sequence feature vectors are obtained; constructing a state transition diagram according to the pet behavior sequence feature vector, and performing time sequence consistency analysis to obtain a pet state judgment result; power management and decision execution are carried out on the feeder based on the pet state judgment result, feeding control under the low-power-consumption condition is achieved, behavior misjudgment caused by posture fluctuation is effectively avoided, the behavior recognition accuracy is improved, the accurate feeding control problem in a multi-pet family is solved, and the user experience is improved.

The invention discloses an operation and maintenance manipulator intelligent control method and system based on visual identification, and relates to the technical field of intelligent manipulator control, and the method comprises the steps: collecting RGB image data and depth image data of an operation and maintenance operation area, and obtaining a standardized image matrix and a mapping relation matrix; inputting the standardized image matrix into an improved ResNet residual network model, generating a comprehensive feature descriptor, and calculating a spatial position coordinate and an attitude angle of the target equipment based on the mapping relation matrix; based on the current joint angle state of the manipulator, an improved Jacobian matrix inverse kinematics algorithm is used for solving a target angle sequence of each joint, a preset operation mode library is matched based on the comprehensive feature descriptor, and a grabbing force parameter and a motion speed parameter are determined; and converting the target angle sequence into a control instruction, and sending the control instruction to each joint driver of the manipulator to drive the manipulator to complete action planning. According to the invention, full-process automation from environment perception to task execution is realized.

The invention provides an industrial robot welding track real-time optimization method and system based on deep learning, and relates to the technical field of industrial robots, and the method comprises the steps: collecting three-dimensional point cloud data, carrying out the multi-scale processing to generate a high-precision curved surface, constructing a welding seam feature descriptor, and carrying out the real-time optimization of the welding track of an industrial robot; and the mapping relation between the technological parameters and the welding posture is optimized on the basis of a multi-target reward function, the mapping relation is converted into an initial movement track, the track is dynamically optimized through a segmented self-adaptive optimization model and a variable structure filtering algorithm, and self-adaptive adjustment of the welding process is achieved. The welding precision and quality of the complex curved surface are improved, and the adaptability and stability of robot welding are enhanced.

The invention discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

The invention provides a brain aneurysm intelligent detection and positioning method and system based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: receiving and preprocessing a head angiography image; utilizing a multi-scale feature extraction network to obtain feature maps and fusing the feature maps; constructing anatomical candidate regions by using density clustering; extracting a tumor contour through a graph cut energy function; constructing a vascular network topological graph based on probability feature mapping, and calculating a position feature descriptor; and finally carrying out classification discrimination and marking and displaying a detection result. According to the method, various characteristics are fused, the accuracy and sensitivity of cerebral aneurysm detection are improved, and the misdiagnosis rate is reduced.

The invention discloses a slope cutting house building deformation monitoring method based on unmanned aerial vehicle remote sensing, and the method comprises the steps: carrying out the periodic observation of an artificial slope through an unmanned aerial vehicle carrying a three-dimensional laser scanner, and obtaining slope three-dimensional point clouds in at least two different periods; performing denoising, down-sampling and coordinate normalization on the point cloud of each period to obtain a preprocessed data set; generating a global feature descriptor of each period by using a PointNet feature extraction unit; on the basis of a Lucas-Kanade iterative optimization algorithm, the descriptors of all periods are matched with the first period as the reference, and optimal rigid body transformation parameters are obtained; completing registration of the cross-period point clouds in a unified coordinate system according to the parameters; calculating displacement by adopting point-by-point difference and forming a differential deformation field; and comparing the displacement with a preset safety threshold, positioning an abnormal area and giving a risk level. According to the method, centimeter order registration and deformation identification of the cross-period point cloud under the unified stable reference are realized, and early warning and hidden danger assessment of a slope cutting and house building scene are supported.

The invention relates to the technical field of image recognition, in particular to an online real-time image recognition anti-counterfeiting verification method and system based on gold transaction. The method comprises the following steps: acquiring a micro texture image of the gold product; generating a unique digital fingerprint based on the physical unclonable feature of the microscopic texture image, and performing encryption in combination with a UTC timestamp to generate a dynamic encryption tag; extracting a feature descriptor of the micro texture image and performing texture comparison with a pre-stored database to obtain a texture matching degree; decrypting the dynamic encryption label to generate a dynamic code aging state; and according to the texture matching degree and the dynamic code aging state, performing anti-counterfeiting discrimination on the micro texture image of the gold product to obtain a genuine product discrimination result and a counterfeit discrimination result. According to the method, the micro texture image, the near infrared spectrum data, the dynamic encryption label and the block chain technology are combined, so that the accuracy, the safety and the efficiency of gold anti-counterfeiting are improved.

The invention discloses a BIM-based building engineering data processing method and system, and belongs to the technical field of digital construction of building engineering. The method comprises the steps that construction scanning point cloud and BIM model data are acquired, and a feature skeleton is extracted; constructing a dynamic local coordinate system to establish a skeleton-model mapping relation; carrying out space division by adopting a locality sensitive hash index and executing ICP registration calculation; reversely deriving an error propagation path to generate a compensation instruction; adjusting a feature descriptor and reconstructing a mapping relation; and finally, model difference comparison and dynamic updating are realized. According to the method, automatic calibration of the construction point cloud and the BIM model is realized, and the problems of low efficiency and large error of traditional manual calibration are solved.

The invention discloses an infrared and visible light image registration method based on an improved SIFT algorithm, and the method comprises the steps: constructing a Gaussian difference pyramid of infrared and visible light images, so as to effectively capture the multi-scale features in the images; performing local adaptive FAST key point extraction on each layer of the pyramid, and detecting significant feature points of the image on a multi-scale level; based on gradient direction distribution of images in key point neighborhoods, main direction distribution is carried out on key points, so that the key points can still keep consistent under different rotation angles; coding the key points based on the gradient information features and generating corresponding feature descriptors to be applied to subsequent registration operation; and performing initial registration by using the similarity between the descriptors, verifying and optimizing an initial registration result by using an optimized RANSAC algorithm, and eliminating mismatching points. According to the method, the number of infrared and visible light image feature point detection and matching is increased, the time complexity is reduced, and the matching precision is ensured.

The invention relates to the technical field of distributed storage verification, and discloses a storage information verification system based on a consensus mechanism. The system comprises a consensus network communication module, a storage feature analysis module, a verification decision core module, a time sequence prediction module, a decision compensation module and a verification execution engine module. The consensus network communication module is connected with a distributed storage node and receives a storage verification request; the storage feature analysis module extracts multi-dimensional feature vectors of the target data to form a feature descriptor set; the verification decision core module is combined with an embedded consensus verification space and dynamic confidence coefficient constraint condition operation to output an initial verification decision scheme; the time sequence prediction module divides a time window and generates a prediction feature descriptor set; the decision compensation module carries out deviation compensation according to the result to obtain an optimization scheme; and the verification execution engine module sends an instruction to trigger the distributed nodes to execute verification operation. A dynamic verification system constructed by the system adapts to dynamic characteristics and heterogeneous environments of distributed storage.

The invention discloses a traffic monitoring video rapid target extraction method for edge equipment, and relates to the technical field of intelligent traffic video processing and edge calculation target detection, and the method comprises the steps: carrying out the adaptive downsampling processing of original video frame data, and generating downsampling video frame data; extracting a foreground target candidate region, and constructing a traffic region-of-interest mask in combination with a lane line detection result; carrying out pixel AND operation on the traffic region-of-interest mask and the foreground target candidate region to generate accurate candidate target region data, and extracting a target feature vector; carrying out weighted fusion on the target feature vector through a lightweight attention mechanism, generating a fusion feature descriptor, and calculating a target confidence score; and carrying out screening and duplicate removal processing on the accurate candidate target area data, and outputting traffic target extraction result data. According to the invention, the target in the traffic video can be rapidly and accurately extracted and processed in a low-delay manner on the edge equipment with limited computing resources.

The invention provides a robot production line article grabbing method based on visual localization, which comprises the following steps: preprocessing a multi-modal image to obtain an original image; performing grid mapping on the original image to obtain a multi-level feature descriptor; based on the multi-level feature descriptors, a dynamic mapping relation among the three coordinate systems is established through a non-rigid coordinate system alignment algorithm, and coordinate offset of movement of the conveyor belt is compensated in real time; performing hierarchical feature matching on the multi-level feature descriptors and an article template library, identifying article categories and extracting contour geometric features; based on the contour geometric features and the surface curvature distribution, the three-dimensional pose and candidate grabbing points of the object are obtained through a geometric constraint optimization algorithm; and generating a robot obstacle avoidance track according to the candidate grabbing points and the robot motion model. According to the method, accurate coordinate compensation is realized through multi-modal data fusion and dynamic adaptive grid mapping, and the article positioning accuracy is improved in combination with hierarchical feature matching.

The invention discloses a gynaecology and obstetrics emergency medical data security sharing system based on a block chain, and relates to the technical field of medical information processing. The method is used for solving the security and timeliness problems of multi-mechanism data sharing in an emergency scene. The method comprises the following steps: firstly, performing grading processing and desensitization on personal identifiers and medical data of patients through a data grading and desensitization module, and outputting data which can be safely shared; then, an on-chain evidence storage module performs windowing processing on the continuous monitoring data flow, extracts key physiological features, generates feature value Hash, constructs data feature descriptors and submits the data feature descriptors to a block chain network; the emergency access token management module realizes dynamic authorization through a smart contract, and generates a temporary access token associated with the data feature descriptor; and finally, the secure decryption and data sharing module verifies the authority based on the hierarchical decryption key, decrypts the data and compares the eigenvalue hash, and shares and records an operation log with an authorization party after ensuring the data integrity, thereby realizing efficient, secure and traceable data sharing.

Methods, systems, and computer-readable storage media for providing a set of column pairs, each column pair including a column of a bank statement table, and a column of a super invoice table, each column pair corresponding to a modality, the super invoice table including at least one row including data associated with multiple invoices, for each column pair, determining a feature descriptor based on an operator, a feature vector being provided based on feature descriptors of the set of column pairs, inputting the feature vector to a ML model that processes the feature vector to determine a probability of a match between the bank statement, and a super invoice represented by the super invoice table, and outputting a binary output representing one of a match and no match between the bank statement, and the super invoice based on the probability.

The invention discloses a multilevel feature fusion adaptive point cloud registration method, and relates to the field of point cloud processing technology and industrial visual inspection technology, and the method comprises the steps: carrying out the preprocessing of a point cloud pair; multi-scale feature extraction is carried out on the preprocessed point cloud pair, a feature pyramid is constructed through the extracted multi-scale features, and a top-down guide mechanism is adopted between layers of the feature pyramid; performing preliminary feature fusion on feature points of the point cloud pair based on the extracted multi-scale features; performing multi-level registration on the point cloud pair; the multiple layers comprise first-layer registration, second-layer registration and third-layer registration, a transformation matrix of each layer is obtained after each layer of registration is completed, and registration is carried out on the to-be-registered point cloud through the transformation matrix of each layer; according to the method, a multi-scale feature extraction strategy is adopted, three feature descriptors with different scales, namely ESF, SHOT and FPFH feature descriptors, are selected for the degradation problem in point cloud registration, and feature extraction and parameter adaptive adjustment from macroscopic to microscopic are achieved.

The invention relates to the technical field of mineral processing visual detection, and discloses a visual analysis system for detecting the grade of a phosphorite flotation froth layer. The system comprises an image acquisition and decomposition module, a parallel feature extraction module, a dynamic feature fusion module, a foam evolution analysis module and a grade decision output module. The system performs multi-scale decomposition on a foam image, extracts physical and semantic features in parallel, constructs a dynamic fusion network based on bidirectional mapping to perform iterative interaction, and generates a multi-modal feature descriptor. Therefore, self-organizing growth of a foam evolution graph is driven, an evolution track of a key foam primitive is positioned and tracked, a foam grade state vector is formed, and finally, a regulation and control decision is output in combination with external control parameters. According to the system, deep fusion of physical and semantic features and deep analysis of the foam dynamic evolution process are achieved, the accuracy and predictability of foam grade state sensing are improved, and an effective means is provided for accurate control over the flotation process.

The invention relates to the technical field of touch screens, and discloses a touch screen virtual key feedback method, device and equipment, and the method comprises the steps: carrying out the normalization processing of touch data collected by a touch screen, obtaining a standardized position matrix and a standardized pressure matrix, and constructing a touch response matrix; performing singular value decomposition and reconstruction operation on the touch response matrix to obtain a reconstruction matrix; classifying the touch points according to the reconstruction matrix to obtain a touch track, and performing region area calculation and pressure distribution center calculation to obtain a touch feature descriptor; establishing a target touch response equation according to the touch feature descriptors; performing signal linearization processing and interference compensation to obtain a compensated touch signal; according to the method, feedback parameters are calculated, stability constraint calculation is conducted on the feedback parameters, virtual key feedback signals are output, accurate control over the virtual key feedback intensity is achieved, and the sense of reality and operation experience of tactile feedback are improved.

The invention provides a tobacco leaf grade intelligent classification and quality evaluation method and system based on machine vision, and relates to the technical field of grade classification, and the method comprises the steps: collecting a tobacco leaf multi-angle image to obtain three-dimensional point cloud data, calculating the size, shape contour, surface fluctuation, color distribution and texture trend information of a leaf, and constructing a multi-scale feature descriptor; recursive feature screening is adopted to obtain an optimal weight coefficient, and a comprehensive evaluation index is generated; and determining the tobacco quality grade based on a preset grading threshold. According to the method, objective quantitative evaluation of the tobacco leaf quality is realized, the classification precision is improved, and the subjective difference of manual discrimination is reduced.

The invention relates to the technical field of data processing, and discloses a scheduling method and system for realizing cloud terminal call center data based on edge computing, and the method comprises the steps: carrying out the feature extraction of a data scheduling request, obtaining a feature descriptor, and calling request association data from a database of a cloud terminal call center; calculating the adaptation degree between the multimode integrated semantics and the edge computing nodes, and screening out data execution nodes corresponding to the request associated data from the edge computing nodes; calculating a channel noise ratio between the data scheduling equipment and the node server, and calculating a communication throughput rate between the data scheduling equipment and the node server; calculating the total load of task data when the request associated data is transmitted, and constructing a data transmission path between the data scheduling equipment and the node server; and executing scheduling processing on the request associated data by using the data scheduling equipment and the node server to obtain a scheduling result. The scheduling efficiency of the cloud terminal call center data can be improved.

The invention relates to an X corner detection method based on multilevel matching and feature optimization, and belongs to the technical field of computer vision and image processing. The method comprises the following steps: making a high-precision template corresponding to a predetermined X corner mark, so that the high-precision template can be accurately focused on a corner area; carrying out noise reduction and filtering operations on the image before identifying the mark so as to remove noise interference; carrying out template matching on the preprocessed image by adopting a self-adaptive threshold value and a multi-scale search strategy; and performing multi-level detection on the basis of template matching, including ORB feature extraction, Harris corner optimization and sub-pixel level positioning, and outputting the accurate position of the corner and a feature descriptor. The method can still maintain high precision and stability in a complex scene, is suitable for the fields of visual positioning, three-dimensional reconstruction and the like, and has the advantages of high detection efficiency, strong anti-interference capability, wide applicability and the like.

The invention provides a federated learning-based model fusion method and system, and the method comprises the steps: extracting key points and descriptors thereof, generating an initial corresponding relation set, and carrying out the coordinate normalization processing, thereby obtaining a robust basic feature; initializing a federated learning architecture client, performing normalization and cutting preprocessing on local image data, extracting feature descriptors, and training a local model based on a convolutional neural network; aggregating client parameters to obtain a global model, carrying out weighted summation or feature splicing fusion on client feature descriptors, carrying out deep mining on fusion features, and optimizing feature expression in combination with a global context attention mechanism; calculating the inner point probability of the matching pair based on the fusion features, screening a high-probability candidate set, and solving a basic matrix; according to the technical scheme, feature information from different clients can be integrated, the updated global model is used for identifying the matching points of the clients, wrong matching is removed through geometric consistency check, and the matching accuracy is improved.

The invention relates to a multi-modal image matching method and system based on learning features and epipolar geometric constraints. The method comprises the following steps: carrying out edge enhancement processing on an input image through wavelet transform; extracting a multi-scale dense feature map based on the transformed convolutional neural network, and generating a feature descriptor with rotation and scale invariance in combination with principal direction normalization; adopting an FLANN algorithm and dynamic distance constraint to realize preliminary feature matching; and introducing a basic matrix construction and epipolar geometric consistency verification mechanism, and eliminating mismatching point pairs in combination with an RANSAC affine constraint model. According to the method, image enhancement, deep learning and geometric verification strategies are fused, the problems of radiation nonlinearity and geometric distortion caused by imaging mechanism differences among multi-modal images are effectively solved, the matching precision and robustness are improved, and the method is suitable for remote sensing application scenes such as optical-SAR registration, multi-source image fusion and earth surface change detection.

The embodiment of the invention discloses a building engineering crack detection method and system based on image recognition, and the method comprises the steps: obtaining a building surface image, carrying out the preprocessing of the image, obtaining a standardized image, and carrying out the multi-scale decomposition extraction and integration of various features, and forming a multi-dimensional feature descriptor set; after feature importance is evaluated, a compact feature vector is generated through dimension reduction, quantization coding and compression, and then a multi-level feature index mechanism for optimized compression is constructed. A query feature vector is extracted from a newly collected image, searching and screening are completed by means of an index mechanism and a tolerance threshold, and a crack matching result is obtained; and based on the result, positioning cracks, classifying types, measuring parameters and evaluating severity, and generating a crack state report. The crack trend is analyzed in combination with the historical data time sequence, a multi-stage early warning mechanism is designed, maintenance suggestions are provided, and a real-time monitoring and early warning system is formed. According to the embodiment of the invention, the technical problems of high storage pressure and low real-time detection efficiency in the prior art can be effectively solved.

The invention relates to an electromagnetic environment measurement method and device for a complex terrain crossing area. The method comprises the following steps: acquiring a source point cloud and a target point cloud of a to-be-detected area, and acquiring a filtering point cloud set corresponding to the source point cloud; performing region growth segmentation on the filtering point cloud set, and determining region geometric information corresponding to each filtering point cloud in the filtering point cloud set; obtaining a matching pair between the first feature descriptor and the second feature descriptor to construct a feature matching relation set; determining a target rigid transformation matrix according to the feature matching relation set and the regional geometric information corresponding to the filtering point cloud, and performing point cloud registration on the filtering point cloud through the target rigid transformation matrix; and combining the filtered point clouds after point cloud registration to generate a three-dimensional point cloud database of the to-be-detected area, and modeling an electromagnetic environment of the to-be-detected area according to the three-dimensional point cloud database. By adopting the method, the measurement accuracy of the electromagnetic environment of the crossing area of the power transmission line can be improved.

The invention discloses a large-scale deformation compensation method, device, equipment and medium, and relates to the field of image processing, and the method regards a salient point in an image as a leader in a group, is similar to a leading animal in a migration sheep flock, and plays a key role in guiding the whole registration process. In order to estimate the aggregation manifold, large-scale deformation registration is divided into three parts according to an animal migration mode: route decision based on aggregation quantization, route execution with aggregation maintenance and cascade migration with aggregation inheritance. The three parts are integrated into an aggregation cascade migration (CCM) framework, and large-scale deformation of the image is effectively compensated. According to the method, the registration precision and robustness during large-scale deformation compensation can be greatly improved. The method is a universal method, is not limited to specific feature descriptors or deformation models, and can be easily expanded to other image registration tasks.

The invention provides a method for identifying forest tree species by using laser point cloud data, and the method comprises the following steps: collecting three-dimensional laser point cloud data of a forest region, setting an elevation threshold, filtering ground points, and extracting a point cloud sample object; respectively extracting VFH, CVFH and ESF feature descriptors from the sample point cloud, and constructing three types of geometric feature vectors; performing supervised classification on the features by adopting a random forest and a support vector machine learning classifier; the output of each classifier is fused through strategies such as weighted voting, an average method or a stacking method, and a final tree species identification result is obtained; according to the method, three types of global or semi-global feature descriptors of VFH, CVFH and ESF are extracted for a point cloud sample object of a single tree, feature modeling is carried out on tree species from three dimensions of spatial attitude, local scale structure and global shape distribution, the advantage of real restoration of a target structure by using point cloud data is utilized, and the feature modeling efficiency is improved. And the problem of projection distortion of image features under multiple view angles is avoided.

The present invention provides an adaptive drone image matching pair selection method and system, comprising: S1: acquiring drone images, performing feature extraction on the drone images using the SIFT algorithm, and obtaining a local feature set of the drone images; S2: constructing a vector codebook, performing feature aggregation on the local feature set using the vector codebook, and obtaining a feature matrix set; vectorizing the feature matrix set to obtain a global feature descriptor vector set; S3: performing image retrieval on the global feature descriptor vector set based on a graph index structure to obtain a drone image matching pair set; S4: performing three-dimensional reconstruction on the drone image matching pair set to obtain a three-dimensional reconstruction model. The present invention uses global feature descriptor vectors to replace word frequency calculation based on local features, and provides a graph-based indexing strategy to achieve efficient overlapping image search, making drone image matching pairs more efficient and accurate.