834 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 steel structure component virtual pre-assembly method based on three-dimensional laser scanning. Designing a self-adaptive hierarchical scanning strategy, and dynamically adjusting parameters of scanning equipment to obtain data of each region of the component; abnormal points are detected and eliminated by fusing scanning data of different levels; datum points are extracted, coordinate calibration is carried out, data of all levels are spliced, and a full-component three-dimensional point cloud model is formed; classifying the point clouds by using a multi-dimensional data analysis algorithm; establishing a closed-loop data verification system; designing a multi-feature point matching algorithm; performing iterative docking and position calibration through an algorithm; quantitative analysis is carried out on spatial deviation between the components in the assembling process; after the deviation is detected, a fine adjustment scheme is generated, and the position of the component is adjusted; a virtual pre-assembly path is generated according to the shapes, the connection modes and the installation sequence of the components, and the assembly sequence and the layout of spatial positions are controlled; and the assembly state of each component is monitored in real time, and the path is optimized and adjusted through a feedback mechanism.

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 provides an unmanned aerial vehicle real-time road condition monitoring method and system and a medium, the method relates to the field of image data processing, and the system comprises an unmanned aerial vehicle which comprises an image acquisition device and a sensor group and is used for acquiring aerial videos and flight parameters of the unmanned aerial vehicle in real time; the streaming media server is used for receiving and storing the aerial video transmitted by the unmanned aerial vehicle; analyzing the video stream into a frame-by-frame image sequence with aligned timestamps; performing target detection on each frame of image, and identifying a target vehicle by using a pre-trained deep learning model; carrying out target tracking on the target vehicle in the continuous frames based on an intersection-to-union ratio matching and trajectory prediction algorithm, and constructing a target motion trajectory; and calculating the displacement distance of the target vehicle in the image based on an optical flow method, feature point matching and a deep learning algorithm, and estimating the actual running speed of the vehicle in combination with the flight parameters of the unmanned aerial vehicle. According to the system, computer vision and sensor technologies are combined, and accurate speed measurement is realized through target detection, tracking and speed estimation.

The invention discloses a method for determining a preset position of a video monitoring camera. Camera presetting positions usually need to be manually set one by one, and time and labor are wasted. The method is used for a video monitoring system, and automatic setting of the preset position of the camera is realized by utilizing a group of scene images and the to-be-monitored target image, so that the efficiency is greatly improved. The method comprises the following steps: acquiring feature points based on a scene image set by adopting a computer vision technology, generating a three-dimensional model of a scene by adopting a motion recovery structure technology and a surface reconstruction algorithm, and obtaining the position of a to-be-monitored target in a three-dimensional space by matching the feature points of a to-be-monitored target image with the feature points of the scene image set. Therefore, a proper camera is selected, a corresponding preset position is calculated, and finally the preset position is corrected according to an actual monitoring effect.

The invention discloses a slope deformation monitoring method and system, a medium and equipment, and relates to the field of slope deformation monitoring, and the method comprises the steps: obtaining a slope image sequence with a fixed time interval; extracting features of the first image, tracking the features of the first image in the to-be-detected image by using a Lucas-Kanade optical flow method, estimating a view field translation amount and a rotation angle of the to-be-detected image relative to the first image, and performing view field motion correction on the to-be-detected image; performing semantic segmentation on the first image and the corrected to-be-detected image to obtain an image pair with a slope region mask; obtaining a feature point matching set of the image pair based on LoFTR; performing feature point mismatching elimination on the feature point matching set by using a two-stage mismatching elimination method to obtain a matching point pair set M [beta]; and calculating the displacement of each point pair in the M [beta], and obtaining a sub-pixel-level slope deformation field through Gaussian interpolation. The scheme is low in cost, small in calculation amount and high in precision.

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 relates to the technical field of UV printing, and discloses a packaging box UV printing method and device and computer device.The method comprises the steps that feature extraction is conducted on CAD model data of a packaging box, and a feature data set of the packaging box is obtained; carrying out coordinate mapping on the working space of the robot to obtain a conversion matrix and space calibration data of the mounting position of the UV nozzle; edge detection and feature point matching are conducted, and position coordinate data and posture angle data of the UV printing area are obtained; performing multi-section track planning on the ink jet track to obtain initial jet printing track control data; and performing synchronous control calculation on the UV light source to obtain a UV light source power modulation parameter and UV light source trigger time sequence data. According to the method, by dynamically adjusting the power output and the triggering time sequence of the UV light source, uniform curing of the ink droplets is achieved, the problem that curing of the ink droplets at different positions is not uniform is effectively solved, and the printing quality is improved.

The invention discloses an air-ground image matching method, device and equipment and a storage medium, and the method comprises the steps: carrying out the sparse reconstruction of an aerial image, obtaining a sparse point cloud model of an aerial image pose and a scene, generating the camera visual angle information of a new camera according to the sparse point cloud model, and enabling the camera visual angle information to serve as a middle visual angle of the scene, the visual angle difference between the space images and the ground images can be reduced, three-dimensional reconstruction is carried out through camera visual angle information, the rendered image of the middle visual angle after rendering is obtained, feature points corresponding to the rendered image are further removed after feature point matching is carried out on the ground image, the aerial image and the rendered image, and the image quality is improved. At the moment, a matching relationship between the ground image and the aerial image can be obtained, and an air-ground joint scene graph is generated according to the matching relationship to complete matching of the air-ground images. According to the embodiment of the invention, the homonymous connection points between the air-ground images can be automatically generated, and automatic air-ground joint model construction is realized.

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 relates to the technical field of remote sensing image ship motion, and discloses a wake matching-based ship motion parameter estimation method, which comprises the following steps of: constructing a ship wake model comprising an infrared sea surface model, a ship wake height field and a ship wake temperature field; setting multiple navigational speeds, multiple navigations and multiple sea condition parameters by using the model to generate a ship wake infrared image, and constructing a ship wake database; a deep learning convolutional neural network and a multi-task loss function are adopted to extract a wake to be estimated; and constructing a multi-stage image matching model based on the feature point extraction module, the feature vector description module and the feature point matching module, and matching the database and the batch to-be-estimated trails by using the multi-stage image matching model. According to the method, the infrared sea surface model mixedly calculates two sea surfaces, so that the repeatability of the sea surfaces is reduced while the authenticity is ensured; by simulating the wake temperature field and the wake height field, the authenticity of the wake is improved, the constructed wake data set can be reused, and efficient estimation of ship motion parameters 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 belongs to the technical field of medical image processing, and discloses a blood vessel segmentation reconstruction and bleeding analysis method and device. In the bleeding area detection model, detecting whether a bleeding area exists or not; and performing blood vessel segmentation and blood vessel three-dimensional reconstruction operation. According to the method, the time sequence image is adopted for comprehensive analysis, and the problems that blood vessel details are lost due to insufficient diffusion of a contrast agent and the image is blurred due to excessive diffusion are effectively solved. Based on an 8-neighborhood pixel tracking method, accurately identifying blood vessel cross points, and performing segmentation processing on the skeleton line by using the cross points. By constructing the topological structure of the blood vessel, the feature point matching efficiency is greatly improved, and the automatic three-dimensional reconstruction process is also realized. By introducing the architecture design of the hybrid convolutional neural network and the state space model, the local fine-grained feature extraction capability and the global long-range dependence modeling capability are effectively combined, so that the segmentation precision and robustness of the intracranial hemorrhage region are remarkably 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 structural dynamic displacement measurement, and discloses a structural deformation monitoring method, device and equipment and a medium, and the method comprises the steps: converting a vibration video of a target structure into a plurality of frames of pictures, and building a normalized orthogonal projection model of a camera, ellipse fitting is carried out through multi-point local calibration, ellipse parameters are obtained, displacement correction parameters of a camera are calculated, feature points of a calibrated circular image are extracted after ellipse correction is carried out on the calibrated circular image, and information fusion is carried out on the feature points of different frames of images based on the correction parameters; measuring point dynamic tracking is carried out through feature point matching of the calibration circular images in different frames of pictures, dynamic displacement information of the measuring points corresponding to the calibration circular images is calculated, and the deformation condition of the target structure is monitored according to the dynamic displacement information. The method can simplify the camera calibration process, reduce the calibration difficulty, improve the measurement precision of dynamic displacement, and improve the accuracy and reliability of structural deformation monitoring.

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 relates to the technical field of image processing, in particular to a vacuum printed PCB precise feeding method and system guided by machine vision, and the method comprises the steps: obtaining the feature points of a to-be-positioned PCB image and the feature points of a real PCB image; calculating the credibility of each feature point of the real PCB image, screening out a target feature point according to the credibility, and matching the target feature point with the feature points of the to-be-positioned PCB image to complete positioning and feeding of the real PCB image; wherein the calculation of the credibility of the feature points comprises the step of clustering the feature points of the real PCB image to obtain a plurality of clusters. According to the invention, by screening the feature points of the real PCB image, it is ensured that the feature points are more accurate and stable, mismatching is reduced, and the accuracy of image matching is improved; and some redundant or unimportant feature points can be removed, so that the workload of subsequent matching and calculation is reduced.

The invention discloses a camera offset detection and dynamic labeling method based on feature point matching. The method comprises the following steps: constructing a target object feature point data model; acquiring a camera monitoring picture image in real time, performing feature point extraction on the picture and the video frame of the target object, and performing matching with the target object feature point data model by using a feature point matching algorithm to obtain a matched feature point pair; calculating the offset of the camera according to the matched feature point pairs, including the translation amount and the rotation amount; correcting the video frame according to the offset of the camera, and identifying and positioning a target object in the corrected video frame; and generating a corresponding label according to the identification and positioning result of the target object, and dynamically displaying the label in the video frame. The method can effectively solve the problems of poor real-time performance, low accuracy, poor robustness and the like in the prior art, and has a wide application prospect.

The invention discloses a terahertz image registration method fusing morphological characteristics. The terahertz image registration method comprises the following steps: filtering and denoising a terahertz image; performing adaptive threshold segmentation processing on the reference image and the to-be-registered image, and respectively generating three types of images, namely a grayscale image, a binary image and an edge image; extracting texture features, geometric features and contour features from the generated grayscale image, the binary image and the edge image, and carrying out integration and feature description on the extracted feature points; mismatching points are removed through bidirectional feature point matching, registration parameters are calculated based on correct matching points, and image registration and verification are completed. According to the method, the number of extracted feature points in terahertz can be increased, multiple features are fused for registration, the registration precision is improved, image registration can be achieved in similar images, the practicability is high, and the application range is wide.

The invention discloses a spatial positioning method, device and equipment, and relates to the technical field of computers, and the spatial positioning method comprises the steps: carrying out the feature point extraction of a target object region of a current image frame, and obtaining a plurality of depth feature points of the target object region; performing feature matching on each depth feature point and a plurality of map points existing in at least one frame of map point image to obtain a feature point matching pair corresponding to the map point image; and when the number of the matching pairs of the feature point matching pairs corresponding to the map point image is greater than a preset number, performing pose calculation according to the feature point matching pairs corresponding to the map point image, and determining a flight body pose corresponding to the current image frame. Through the above mode, the plurality of depth feature points of the target object area are matched with the map points in the map point image constructed in advance, and when the matching result meets the condition, the flight body posture of the current image frame is determined through feature point matching, so that accurate positioning of the flight body in a complex environment is realized.