691 results about "Feature point matching" patented technology

The invention discloses a close planting farmland growth vigor assessment method and system based on image processing, and relates to the field of agricultural information, and the method comprises the following steps: S1, multi-source data collection and preprocessing; s2, improving image segmentation, and extracting crop features; and S3, multi-dimensional growth vigor evaluation. According to the method, the field block level, the plant level and the whole growth period are covered through multi-source data collection, a generative adversarial network is used for repairing and shielding the plant image and restoring complete form information, the segmentation problem in a close planting scene is solved, the accuracy of close planting crop image analysis is improved, accurate registration of multi-modal data is achieved by means of feature point matching, and the accuracy of close planting crop image analysis is improved. The graph neural network optimizes image segmentation, effectively distinguishes overlapped leaves and stalks, deeply fuses multi-modal features and dynamically selects a fusion strategy, improves feature distinguishability, constructs a dynamic adaptive evaluation model, improves generalization ability and evaluation precision, identifies and intervenes abnormities in real time, and improves crop anti-risk ability and yield prediction accuracy.

The invention relates to the technical field of intelligent detection in the electronic manufacturing industry, in particular to a PCB warping degree intelligent evaluation system integrating visual positioning and multi-modal sensing, which comprises a multi-modal sensing unit, a visual positioning unit, an intelligent evaluation engine and a closed-loop control unit, the multi-modal sensing unit integrates laser displacement, infrared thermal imaging and strain sensors to acquire three-dimensional deformation, temperature and stress data; the visual positioning unit realizes sub-pixel-level positioning by using a high-resolution industrial camera and a feature point matching algorithm, and compensates vibration errors; the intelligent evaluation engine fuses data based on a time-space synchronization protocol, predicts a thermal deformation trend through an improved multi-modal convolutional neural network, and dynamically adjusts a qualified threshold value; and the closed-loop control unit executes sorting and rechecking according to an evaluation result, and optimizes warping and leveling parameters. According to the system, multi-dimensional accurate detection and intelligent control are realized, the PCB warping degree detection accuracy is effectively improved, the process can be dynamically optimized according to the production working condition, and the equipment fault risk is reduced.

The invention relates to the technical field of stamping part detection, and discloses a stamping part size and defect synchronous detection method and system. The method comprises the steps that the multi-sensor measurement module is used for collecting line laser scanning morphology data, infrared thermal image strain data and structured light projection contour data of a stamping part; multi-sensor data registration is achieved through a feature point matching algorithm, and a three-dimensional space coordinate mapping relation is generated; calculating the thermal expansion compensation amount of the material in combination with a thermal deformation correction model; filtering the structured light projection contour data, and extracting key contour feature points and defect region boundaries; inputting the related data into a multi-source data fusion model to obtain a dimensional deviation and defect fusion detection result; based on this, a measurement path is updated through a dynamic path planning algorithm, and a synchronous detection scheme is output. The device can synchronously detect the size and defect of the stamping part, improves the detection precision and efficiency, achieves the quality grade classification, and is high in adaptability.

The invention relates to a non-standard special-shaped part high-precision measurement and verification method based on three-dimensional vision, and belongs to the field of three-dimensional reconstruction and computer vision. Point cloud data of a non-standard special-shaped part are obtained through multi-angle scanning by means of multi-camera-projector collaborative layout in combination with a structured light coding technology and a phase shift code technology; the method comprises the following steps of: acquiring point cloud data, ensuring comprehensiveness and accuracy of the data, realizing high-precision alignment of the point cloud data through matching an underivative optimization algorithm with image feature points, and performing fusion, denoising and registration processing on the acquired point cloud data to ensure that point clouds acquired from a plurality of angles can be effectively fused so as to reconstruct a high-precision three-dimensional model. In the measurement and verification stage, the deviation between the point cloud data and the CAD model is measured through a standard coordinate system, multi-stage registration and a high-precision algorithm, rapid detection and size verification of the geometric deviation of the industrial workpiece are achieved, and reliable technical support is provided for quality control.

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

The invention relates to a wharf sensing method and system based on data fusion of a laser radar and a camera, and the method comprises the steps: firstly employing a fisheye camera to shoot a checkerboard calibration plate at multiple angles, employing a Zhang's calibration method to calculate an internal reference matrix and a distortion coefficient of the fisheye camera, and synchronously collecting the data of the laser radar and the camera in real time; the method comprises the following steps: performing distortion removal processing on a camera image, calculating an external parameter matrix between a laser radar and a camera through a feature extraction algorithm, a feature point matching algorithm and a nonlinear optimization algorithm, and performing data fusion through an internal parameter matrix, the external parameter matrix and a bilinear interpolation method to generate fusion point cloud data with color information; and finally, identifying a key target of the wharf by marking and training a CNN model, and calculating three-dimensional parameters of the key target by adopting a specific calculation method, so that a sailor can quickly distinguish the target in the fused point cloud data, the accuracy and efficiency of wharf environment perception are remarkably improved, and accurate environment perception and decision support are provided for ship berthing and departing operation.

The invention discloses an industrial image anomaly detection method and device, equipment and a storage medium, and relates to the field of image recognition. Obtaining a defect-free product graph, making a comparison data set based on the defect-free product graph, and performing pre-storage processing; performing feature point shape matching on the comparison data set based on the received to-be-detected image and the extracted feature point data, and determining a comparison image of the to-be-detected image; taking the to-be-detected image as a target object, correcting a product image area in the image, and aligning the product image area with the product image area of the to-be-detected image according to a pixel shift operation; and comparing the aligned product image areas, and identifying abnormal defects in the to-be-detected image. According to the scheme, the comparison data set of the defect-free product image is constructed, the feature point matching and pixel-level alignment technologies are combined, the problem of false detection caused by product position deviation or form change in a traditional method is effectively solved, the adaptability to illumination change is improved through brightness adjustment and difference matrix analysis, and the detection accuracy is improved. The method has the advantages of high detection precision, high environmental adaptability and high processing efficiency.

The invention discloses an unmanned aerial vehicle-satellite scene matching method based on two stages. Precise scene matching and positioning are completed from coarse granularity to fine granularity. An existing satellite base map retrieval task is used as a first-stage task, and a local feature point matching method is introduced in a second stage. Compared with an existing method, the method has the advantages that the matching robustness in a complex scene is remarkably improved, meter-scale accurate positioning of the unmanned aerial vehicle can be realized on an existing satellite map, and a new universal solution is provided for the field of scene matching.

The invention discloses a multi-target tracking method combining camera motion compensation and pseudo depth estimation, belongs to the field of computer vision, and is suitable for a complex automatic driving road environment. The method comprises the following steps: constructing a training set and a test set; detecting the image by using a deep learning detector and extracting features; a Kalman filter is adopted to correct a motion modeling state vector, and the target position and size prediction precision is improved; solving a homography matrix through feature point matching, performing global camera motion compensation, and reducing camera jitter and displacement interference; target pseudo depth information is calculated, hierarchical cascade matching is carried out, and association performance in dense and shielding scenes is optimized; a three-level cascade strategy is adopted to complete high confidence degree, low confidence degree and residual target matching in sequence; and finally, outputting a tracking result with a detection frame and identity information to obtain a trained model. According to the invention, accurate detection and stable tracking of multi-category targets can be realized in a complex environment, and identity switching is effectively reduced.

The invention discloses an intelligent change detection method for non-aligned two-time-phase images of an inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle image processing and change detection, and the method comprises the steps: obtaining two-phase images collected by a low-altitude unmanned aerial vehicle under a fixed route and same sensor parameters; a lightweight registration model is constructed and trained, feature point matching is utilized to predict matching point pairs, a homography matrix is calculated, and accurate registration of non-aligned images is achieved; and constructing and training a change detection model based on image pair interaction feature fusion, analyzing the aligned image after registration, and outputting a change information binary image. The method can effectively solve the problem of non-alignment caused by position and angle differences during two-time-phase image acquisition of an unmanned aerial vehicle, and the technical problems of low precision, poor robustness and insufficient calculation efficiency of a traditional method in change detection, effectively improves the automation level of low-altitude safety monitoring of ground highways and railways, reduces the maintenance cost, and improves the safety of the unmanned aerial vehicle. The important application value is realized.

The invention discloses a waste lithium battery classification processing method and system based on image segmentation identification, and relates to the technical field of waste lithium battery recycling, and the method comprises the steps: configuring a high-resolution camera, a lighting device and an ambient light sensor, collecting multi-view images of waste lithium batteries in real time, obtaining a two-dimensional image data set, and carrying out the recognition of the two-dimensional image data set; inputting the two-dimensional image data set into an SIFT algorithm to detect and match feature points, generating a three-dimensional model through an MVS technology based on the matched feature points, and obtaining geometric information of the surface of the battery by using a triangulation method; according to the method, the high-quality three-dimensional model is generated by combining high-resolution multi-view image acquisition with SIFT feature point matching and the MVS technology, and the quality of the model is optimized through denoising, curvature enhancement and dynamic parameter adjustment, so that accurate modeling of a complex geometrical shape is ensured. And the optimized three-dimensional model is analyzed by using the deep convolutional neural network, so that the robustness and accuracy of defect detection are greatly improved.

The invention relates to the field of new energy, and discloses a photovoltaic system efficiency evaluation method and system based on unmanned aerial vehicle multi-source image fusion, and the method comprises the steps: collecting a visible light orthoimage, thermal imaging data and environment parameters of a photovoltaic system, and obtaining a registration result through employing an improved feature point matching algorithm; based on a registration result, combining spatial features of a visible light image and temperature features of thermal imaging, realizing accurate segmentation of the photovoltaic panel through a deep learning model, and identifying the type, the arrangement mode and the installation angle of the photovoltaic panel at the same time; establishing a mathematical model of the relation between the photovoltaic panel temperature distribution and the power generation efficiency, distinguishing the normal working temperature difference and the fault hot spot, and analyzing and determining the efficiency attenuation degree and the fault type of the photovoltaic system through a heat distribution mode. The method is accurate in image registration, good in data fusion effect, accurate in temperature anomaly detection and comprehensive in efficiency evaluation, and provides more efficient and accurate technical support for management and maintenance of the distributed photovoltaic system.

The invention discloses a multi-modal drawing analysis technology and system, and relates to the technical field of drawing analysis, and the multi-modal drawing analysis technology comprises the following steps: recognizing a to-be-detected drawing, carrying out the preprocessing, and integrating the preprocessed data based on a feature point matching algorithm and a dynamic fusion strategy; inputting the integrated data into an improved OCR model, extracting key information in combination with semantic analysis, and performing logic correction verification; outputting the data passing the verification, and generating structured data and a visual chart; through multi-modal data preprocessing, feature point matching and dynamic fusion strategies, the OCR model and the semantic analysis technology are improved, and the problems that multi-format drawings are poor in compatibility, low in analysis precision and incomplete in information extraction are solved.

The invention provides a feature point matching method for digestive tract capsule endoscope images. The method comprises the following steps: after preprocessing, converting each image into a grayscale image, sequentially rotating each grayscale image from an initial angle according to different angles, carrying out feature point identification on the image after each rotation, and taking a subsequent feature point set with the maximum number of feature points as a feature point set; feature point matching is carried out on the feature point sets of the two adjacent frames of grayscale images, and part of matched feature points in the matched feature point sets are removed according to feature point position threshold conditions, feature point density threshold conditions and feature point brightness threshold conditions; and according to a geometric consistency condition and a confidence threshold condition, removing a part of matched feature points in the preliminary matched feature point set to obtain a final matched feature point set. According to the method, more feature points can be obtained, the distribution uniformity of the feature points is improved, and the quality of the feature points is improved.

The invention provides a bridge stay cable video multi-target identification tracking and vibration extraction method based on an unmanned aerial vehicle. The method comprises the following steps: 1, constructing a bridge stay cable refined inclined slender target detection model based on a YOLOv11 model; 2, providing a multi-target tracking algorithm fusing the inclined slender target detection model and a StrongSORT algorithm; step 3, improving a displacement extraction method combining SIFT / ORB feature point matching and a sub-pixel refinement technology; 4, designing an unmanned aerial vehicle motion correction algorithm based on variational mode decomposition and time-frequency domain combined screening; and 5, constructing a joint working modal analysis algorithm for realizing combination of a natural excitation technology and a random subspace recognition algorithm. According to the method, high-precision extraction of the vibration signals of the stay cable and identification of modal parameters of the stay cable are realized, and technical support is provided for health monitoring of the large-span cable-stayed bridge.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention relates to the technical field of real-time positioning and mapping (SLAM), in particular to an SLAM method for tightly coupling a laser odometer and an inertial odometer, which comprises the following steps of: S1, receiving IMU (Inertial Measurement Unit) data and laser radar point cloud data, carrying out pre-integration processing on the IMU data and compensating laser frame motion distortion; s2, feature point selection is carried out on the laser point cloud based on curvature calculation, and angular points and plane points are distinguished; s3, uniform filtering is carried out on the feature points to optimize the point cloud distribution density; s4, calculating a lower pose change matrix of the laser speedometer according to the filtered feature point matching, and correcting IMU data of the inertial speedometer through the pose change matrix; and S5, adding multiple factors into the factor graph to optimize the global pose, and constructing a global map. Through a tight coupling bidirectional correction mechanism, the advantages of a laser odometer and an inertia odometer are combined, deep bidirectional compensation of sensor errors is realized, the pose estimation precision is effectively improved, and particularly, accumulative errors are remarkably inhibited in a dynamic interference scene.

The invention provides a camera pose and 3D Gaussian point cloud joint optimization method and system, and belongs to the technical field of computer vision, and the method comprises the steps: obtaining a to-be-processed image; processing the obtained image by using a pre-trained optimization model to obtain an optimized image; the optimization model comprises a feature extraction module, a matching module, a generation module and an optimization module; the feature extraction module is used for feature extraction; the matching module is used for carrying out feature point matching; the generation module is used for screening inner points through RANSAC and calculating a basic matrix to generate an initial point cloud; and the optimization module is used for performing nonlinear optimization on the initial pose and the point cloud, and outputting camera parameters and sparse Gaussian point cloud. According to the invention, synchronous high-precision estimation of the camera pose and the 3D Gaussian point cloud is realized; dependence on a pre-calculation pose or a pre-training model is abandoned, and the pose and the point cloud are directly combined and recovered from a disordered image; a large-view-angle jump scene is processed through a progressive optimization strategy, and the stability of the pose and the point cloud under sparse matching is ensured.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention relates to the technical field of image stitching, and discloses a video transmission high-definition image intelligent stitching method and system. The system comprises an image preprocessing module, a feature point matching and screening module, an image optimal splicing seam generation module and a video output module. The method comprises the following steps: firstly, acquiring a video, carrying out dynamic range equalization, carrying out filtering processing by using an adaptive noise reduction method, and carrying out image correction; secondly, extracting feature points by using a self-adaptive corner detection algorithm, matching the feature points based on a multi-dimensional spatial data index to obtain feature point matching pairs, and screening the feature points; searching an overlapping region, and introducing a dynamic search algorithm to generate an optimal image splicing path to obtain an optimal image splicing seam; and finally, dividing the image according to the dynamic grid, and realizing video output by using priority ranking. According to the method, the video images are processed and spliced, the purpose of intelligent image splicing is achieved, and the method is accurate and objective.

The invention relates to the field of pipeline three-dimensional reconstruction and intelligent detection, in particular to a pipeline three-dimensional reconstruction and intelligent detection method based on panoramic stereoscopic vision. According to the technical scheme, a panoramic camera and a radar sensor are used for collecting panoramic images and depth data in a pipeline; carrying out denoising, distortion correction and illumination compensation processing on the collected panoramic image, and carrying out image optimization; stable feature points in the panoramic image are extracted, and feature point matching is carried out; a multi-view stereoscopic vision algorithm and a synchronous positioning and mapping technology are combined, and pose estimation and three-dimensional point cloud generation are carried out by using a panoramic image and depth data; optimizing the generated point cloud data, and reconstructing a smooth and continuous three-dimensional surface; and based on the processed three-dimensional data, intelligently detecting cracks and corrosion defects in the pipeline by using a convolutional neural network, and automatically generating defect types, positions and repair suggestions. The method is suitable for pipeline detection.

The invention discloses a method for automatically generating an ancient building elevation by using a multi-modal neural network, and the method comprises the steps: obtaining and preprocessing a three-dimensional inclined point cloud model and a three-dimensional laser point cloud model of a target ancient building, and obtaining the indoor and outdoor point cloud data of the ancient building; performing feature point matching and rigid body transformation fusion on the ancient building indoor and outdoor point cloud data to obtain a three-dimensional ancient building point cloud model; performing semantic segmentation on the three-dimensional ancient building point cloud model through projection and a multi-modal neural network to obtain each structure area of the ancient building; and according to each structural region of the ancient building, extracting each component contour from the three-dimensional point cloud model to generate an ancient building elevation.

The invention relates to an intelligent ship berthing and leaving self-positioning method and system based on texture point clouds, and the method comprises the steps: firstly employing a fisheye camera to shoot a checkerboard calibration plate at multiple angles, employing a Zhang's calibration method to calculate an internal reference matrix and a distortion coefficient of the fisheye camera, and synchronously collecting the data of a laser radar and the camera in real time; the method comprises the following steps: carrying out orthodontic processing on a camera image, calculating an external parameter matrix between a laser radar and a camera through a feature extraction algorithm, a feature point matching algorithm and a nonlinear optimization algorithm, and carrying out data fusion through an internal parameter matrix, the external parameter matrix and a bilinear interpolation method to generate texture point cloud data with color information; the method comprises the following steps: performing texture point cloud registration through an improved NDT-color algorithm in cooperation with open-loop mileage estimation to preliminarily determine the position change of a ship, optimizing a graph model by using a closed-loop detection and graph optimization technology, generating an optimized global displacement vector, and further generating a wharf high-precision map containing color texture information. And real-time self-positioning of the ship in a wharf high-precision map is realized.

The invention discloses a gas leakage detection method. The method comprises the following steps: S10, carrying out denoising and image enhancement on an acquired visible light image and an acquired thermal infrared image; s20, carrying out image feature point matching; s30, fusing the visible light image and the thermal infrared image after registration; and S40, gas leakage detection based on the improved YOLOv8 framework. According to the invention, thermal imaging, multispectral and visible light imaging multi-source fusion technology is adopted, so that the problems of efficient detection and accurate identification of multiple types of gases in a broadband range are solved.

The invention relates to the technical field of video processing, and discloses a three-dimensional video fusion method and application thereof in a video security system. The method comprises the following steps: processing a static scene and a dynamic scene, constructing a three-dimensional scene model, obtaining an optical flow field corresponding to each layer of image of an image pyramid, obtaining multi-scale optical flow information, and correspondingly matching image feature points of a video frame with image feature points of the three-dimensional scene model to generate a feature point matching result, the corresponding relation between the video frame and the three-dimensional scene model is found according to the feature point matching result, accurate position information is provided for subsequent fusion, the fusion weight is calculated according to the feature point matching result, the fused three-dimensional video is obtained through processing according to the fusion weight, and the fused three-dimensional video can be used in a video security and protection system. More comprehensive and accurate scene information can be provided, the precision and real-time performance of security and protection monitoring can be improved, and the requirement of a video security and protection system for high reliability is met.

The invention discloses a landscape object identification and explanation method and system, a medium and a product, and relates to the technical field of augmented reality. The method comprises the following steps: when the head motion state of a user is stable, triggering a camera to collect and recognize a single-frame image, matching a recognition result with a regional knowledge base, and generating a candidate object list and coarse-grained poses which are sorted according to spatial probability and historical popularity; performing feature point matching on a current frame and an ideal frame in the video stream, and calculating a fine-grained pose of a target landscape in the candidate object list relative to a camera coordinate system according to a matched target feature point; according to the fine-grained pose and the real-time eye movement data, determining a drop point part of the sight line of the user on the target landscape, forming a multi-dimensional query vector according to the drop point part, the intention of the user and the environment context parameters, and generating multimedia explanation content; and carrying out fusion rendering on the multimedia explanation content and the real-time video stream, and outputting and displaying. By implementing the technical scheme, the personalized multimedia explanation content can be generated.

The invention provides a multi-camera-based AI glasses FOV extension method and system, and the method comprises the steps: transmitting a synchronization signal to a plurality of cameras, enabling the plurality of cameras to carry out the exposure at the same time, and obtaining a plurality of frames of original images at the same time; pre-processing the multiple frames of original images to obtain pre-processed images; key feature points of the preprocessed image are extracted through a feature point detection algorithm, and feature descriptors are generated; through a feature matching algorithm, feature point matching is carried out on the adjacent preprocessed images by utilizing the feature descriptors, and a local transformation matrix between the adjacent preprocessed images is calculated; based on the local transformation matrix, fusing the adjacent preprocessed images to obtain a fused image of the adjacent preprocessed images; and all the fused images are spliced to obtain a large-view-field-angle image covering the target view angle, so that the method has the advantages of effectively expanding the shooting view angle, eliminating image distortion and improving the image splicing naturalness.

An image processing device includes: a region dividing unit that divides each of a plurality of captured images captured by an in-vehicle camera into a plurality of divided regions; a feature point detection unit that detects, as a feature point, a portion exceeding a threshold value for each of the divided regions; a threshold value setting unit that sets the threshold value for each of the divided regions so as to evenly distribute the feature points detected in each of the divided regions; a feature point matching unit that performs matching by associating the feature points detected in the plurality of captured images with each other between the plurality of captured images; and a relative parameter calculation unit that calculates a relative parameter on the basis of the feature points associated between the plurality of captured images.

The invention discloses a multi-unmanned aerial vehicle target tracking method based on cross-view collaboration, which is suitable for robust target monitoring in a dynamic scene. The method comprises the following steps: firstly, establishing a spatial mapping relation among visual angles of multiple unmanned aerial vehicles through feature point matching and projection transformation; after each unmanned aerial vehicle node independently executes deep learning-based target detection, converting a detection frame center point coordinate of an auxiliary view angle into a main view angle coordinate system, and calculating an Euclidean distance between the detection frame center point coordinate and a main view angle detection result; a main visual angle potential leak detection area is identified by setting a dynamic distance threshold value, and a local high-sensitivity re-detection module is triggered based on leak detection confidence; and finally, realizing space-time tracking of observation data by adopting a single-view target tracking algorithm. According to the invention, the problem of leak detection caused by single-view-angle shielding is effectively solved through a cooperative verification mechanism of a cross-view-angle detection frame center point space relationship, and the continuity and reliability of target tracking in an unmanned aerial vehicle view angle complex environment are remarkably improved while the real-time performance is ensured.

The invention provides a digital pathological image high-color accurate splicing method and device based on feature point matching. The method comprises the following steps: obtaining a target splicing region in a target splicing image and a to-be-spliced region in a to-be-spliced image based on a scanning sequence of digital pathological images; performing affine transformation on the to-be-spliced region by using the affine transformation matrix to obtain an initial correction region; constructing a global color mapping function, and mapping the initial correction image by using the global color mapping function to obtain a color correction image; and splicing the color correction image and the target splicing image based on the scanning sequence of the pathological images to obtain a digital pathological image splicing result. According to the scheme, only the target splicing area and the to-be-spliced area are subjected to feature point matching, the cumulative distribution function of the effective feature points is calculated according to the RGB channels, and the global color mapping function is constructed and optimized, so that accurate color correction is realized, and the visual integrity and consistency of the spliced image are improved.