827 results about "Global coordinate system" patented technology

The invention relates to the technical field of highway engineering informatization, in particular to a geometric registration method and system based on a BIM model and an AR real scene. The method comprises the following steps: obtaining multi-source data of a construction site, and generating an AR real scene of the construction site according to the multi-source data; fusing multi-source data to obtain accurate parameters of the construction equipment; calculating a spatial transformation matrix of a construction site through accurate parameters of construction equipment, and obtaining an initial registration result of the BIM model and the AR real scene by combining engineering characteristics and the spatial transformation matrix; a dynamic error compensation mechanism is introduced, the initial registration result is corrected through the dynamic error compensation mechanism, and a local registration result is obtained; and constructing a global and local coordinate system mapping optimization model, and mapping the local registration result to a global coordinate system by using the global and local coordinate system mapping optimization model to obtain a global registration result. According to the method, a reliable spatial data basis is provided for highway engineering quality inspection, construction guidance and defect identification.

The invention belongs to the technical field of tunnel inspection and auxiliary rescue, and particularly discloses an unmanned aerial vehicle multi-source fusion inspection and auxiliary rescue method based on an urban tunnel, multiple sensors are integrated on an unmanned aerial vehicle platform, and multi-source data are processed through a dynamic weighting factor graph optimization method based on a sliding window. By introducing a Mahalanobis distance consistency test mechanism, dynamically evaluating the consistency between sensor observation data and IMU prediction data, screening out data with high credibility, and endowing each factor with different optimization weights, the better the quality of the sensor observation data is, the higher the factor weight is, so that the robustness of final target estimation is improved, and the target estimation accuracy is improved. And then uniformly converting sensor data at each moment into a global coordinate system by using an SLAM framework, gradually constructing a three-dimensional map of the tunnel, and finally realizing high-precision real-time positioning of the unmanned aerial vehicle and construction of the three-dimensional map of the tunnel for subsequent inspection and auxiliary rescue tasks.

The invention relates to a common-field-of-view-free camera external parameter calibration method and system based on a laser tracker. The method comprises the following steps: firstly, setting a laser tracker and defining a global coordinate system based on calibration of external parameters of a common-field-of-view-free camera of the laser tracker; for each single camera, if no common view exists between the single camera and the tracker, the tracker is transferred to the position with the common view, a temporary calibration plate is arranged, and the pose relation between the temporary calibration plate and the global coordinate system is calculated; when a public view exists, a calibration plate with a target seat is arranged in the public view, a laser tracker is used for measuring coordinates of a target ball, a camera is used for measuring coordinates of a conversion ball, a target ball coordinate system and a conversion ball coordinate system are respectively established and unified, and therefore the pose relation between the camera and the calibration plate and the pose relation between the calibration plate and the global coordinate system are calculated; and finally, obtaining external parameters of the common-field-of-view-free camera by combining all pose relationships. Compared with the prior art, the method has the advantages that the external parameters of the camera can be accurately and efficiently acquired when the camera system without the public view field is processed, the anti-interference capability is strong, the efficiency is high and the like.

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention discloses an anti-collision monitoring system based on a depth estimation and instance segmentation fused three-dimensional model, particularly relates to the field of intelligent driving security and protection, is used for solving the problem of target recognition and anti-collision in an environment, realizes efficient feature extraction of global semantics and local textures through a multi-branch network structure, and ensures the accuracy and comprehensiveness of target segmentation. In combination with a cross-frame identity association and conflict detection mechanism, the consistency of target identities is effectively maintained, and the mismatching problem caused by shielding or similar appearances is reduced; multi-scale space-time shielding mode analysis and boundary motion consistency verification are adopted, a fusion algorithm is utilized to comprehensively evaluate shielding complexity and segmentation robustness, a high-risk shielding area is identified, secondary difference correction is executed, and space positioning information of a target is remarkably optimized; and finally, integrating the optimized segmentation and depth data into a global coordinate system, constructing a high-precision and coherent three-dimensional semantic scene, and providing stable and high-quality input data for collision detection and trajectory prediction.

The invention relates to the technical field of optical detection and calibration, in particular to an optical detection and calibration method based on a laser cross calibration method, which comprises the following steps of: 1, suppressing dynamic ambient light and adjusting laser parameters; step 2, extracting anti-distortion feature points; 3, multi-view error collaborative optimization: inputting the corrected coordinates output in the step 2 and mechanical motion parameters of a rotating shaft encoder into a splicing optimization model, constructing a coordinate transformation matrix with curvature constraint, and obtaining a three-dimensional calibration point cloud under a global coordinate system through iterative optimization; and 4, dynamic closed-loop compensation: dynamically correcting the laser power compensation coefficient in the step 1 and the distortion correction parameter in the step 2. Through close cooperation of multiple steps, high-precision, stable and self-adaptive laser calibration is realized, and reliable optical detection and calibration results can be provided in various environments and working conditions.

The invention relates to the technical field of image compensation, in particular to a reflection compensation method based on image processing, and provides the following scheme: acquiring an environment panoramic image by using a panoramic camera to establish a scene global coordinate system, and acquiring a multi-view original image in combination with a camera array arranged in the circumferential direction of an object; determining a rotation symmetry axis according to the multi-view contour features, reconstructing a three-dimensional geometric body, and calculating normal distribution and curvature change; generating a prediction image based on diffuse reflection and specular reflection hypothesis in the surface expansion domain through virtual visual angle disturbance, identifying a reflection region according to color and gradient consistency, and generating a reflection mask; and performing texture reconstruction on the reflective area by using geometric registration and multi-view compensation, and finally performing splicing and fusion to obtain a non-reflective high-fidelity panoramic image. The method does not need to change the field illumination condition, and can achieve the precise recognition and compensation of the complex curved surface reflection in the cultural relic in-situ collection environment.

The invention belongs to the field of laser radar point cloud matching, and particularly relates to a long-distance laser radar high-precision matching method based on joint probability weight iteration, which comprises the following steps of: 1) preprocessing and calibrating acquired point cloud data, and converting the preprocessed and calibrated point cloud data into a global coordinate system taking a main laser radar as a reference; 2) processing the point cloud data to obtain a screened plane point cloud; 3) refining the screened plane point cloud, and optimizing the coefficient of a plane equation to obtain the plane equation; 4) selecting a splicing point cloud, and matching the point cloud data through a joint probability weight normalization method in the splicing process; and 5) performing iteration matching and splicing for multiple times through a joint probability weight normalization method to obtain an accurate change matrix, and constructing a unified global point cloud model in combination with the internal reference of the calibrated main laser radar. According to the method, an automatic feature matching mechanism is included, and large-area and flat plane features in different laser radar point clouds can be automatically recognized.

The invention relates to a road section risk early warning method based on thunder-vision fusion perception, and the method comprises the following steps: S1, obtaining the high-precision space-time position information of a vehicle in a tunnel through the multi-source information fusion of a millimeter-wave radar and machine vision; s2, mapping the vehicle position under the global coordinate system to a coordinate system expanded along a road center line, and representing the vehicle position as a longitudinal projection position and a transverse offset along the road direction; s3, under the coordinate system, taking anti-collision time as a core index, combining lane judgment and relative speed, constructing a dynamic longitudinal risk grading model, and performing linkage triggering with a warning system; s4, constructing a regional risk integral model; and S5, speed limiting information is issued to all driving-in vehicles in real time through the variable speed limiting screen, and the rear-end collision risk is actively prevented and controlled. According to the method, the sudden risk response time can be shortened, the speed limit control response precision can be improved, the rear-end collision rate of the diversion and convergence areas can be reduced, false alarm interference can be reduced, and the tunnel driving safety and the information guiding efficiency can be improved.

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a computer vision-based hundred-million-pixel panoramic situation awareness method and system, and the method comprises the steps: synchronously collecting original local field-of-view images through multiple cameras; independently executing target detection and semantic segmentation in an original image domain; constructing local-to-global coordinate mapping based on the calibration parameters; projecting the target features to a global coordinate system and detecting a cross-view-field target; extracting multi-view-field texture, edge and color features, and performing weighted fusion according to a visible area; according to the method, through identification preposition and semantic level feature fusion, geometric splicing distortion is avoided, the cross-boundary identification precision is improved, the calculation load is reduced, target continuous tracking and situation real-time output in a high-density scene are realized, and the method has high engineering suitability and industrial popularization value.

The invention discloses an interferometer error calibration method and device and a computer readable storage medium, and belongs to the technical field of measurement equipment.The method comprises the steps that measurement data collected when an interferometer moves on the x / y axis of a motion table is obtained, and x / y axis compensation amount is generated through calculation processing; meanwhile, a coordinate system transformation matrix is established and optimized by using data of a marking plate arranged on a motion table, and x-axis and y-axis non-orthogonal parameters are obtained. And then the compensation amount and the non-orthogonal parameters are input into a preset kinematic model, and the accurate position of the motion platform under the global coordinate system is calculated in real time. And when a preset updating condition is met, dynamically updating the error compensation value of the interferometer through the deviation between the actual position detected by the visual system and the calculated position. Reflector surface type error compensation and non-orthogonal parameter correction are fused, and high-precision error compensation calibration of the interferometer is achieved.

The invention discloses a multi-unmanned aerial vehicle cooperative positioning method and system, and belongs to the technical field of unmanned aerial vehicle positioning, a global coordinate system is defined firstly, a task starting point is taken as an original point, a Z axis is vertically upward, and unmanned aerial vehicle coordinate systems take respective inertial measurement units as centers; the unmanned aerial vehicle A is controlled to perform hovering motion at the indoor closed space laser radar feature point; controlling the unmanned aerial vehicle B to receive the pose covariance matrix and the local feature points of the unmanned aerial vehicle A in real time; laser SLAM output of the unmanned aerial vehicle B and the reference pose of the unmanned aerial vehicle A are fused through tight coupling Kalman filtering, accumulative errors are dynamically corrected, and an optimized pose and a covariance matrix of the unmanned aerial vehicle B under a global coordinate system are output; and unifying observation of the unmanned aerial vehicle A and observation of the unmanned aerial vehicle B to the same coordinate system by sharing pose constraints, and dynamically aligning local and global features to realize multi-unmanned aerial vehicle cooperative positioning. The method and the device are used for solving the technical problems of accumulated drifting and the like caused by long-time operation of laser SLAM in perception weakening scenes such as galleries and pipe galleries.

Methods and scan systems for analyzing an object. The methods include obtaining a local operation dataset that includes a plurality of operation data points obtained from an object and a corresponding location, within the operation coordinate system, for each operation data point of the plurality of operation data points. The methods also include mapping the plurality of operation data points from the operation coordinate system to a global coordinate system for the object. The mapping includes mapping to generate a global operation dataset that includes the plurality of operation data points and a corresponding global location, within the global coordinate system, for each operation data point. The scan systems include a robot that performs a plurality of operations on an object within an operation coordinate system and a computing device that maps the plurality of operation data points from the operation coordinate system to a global coordinate system.

The invention discloses a dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method and system based on a digital array radar, and the method specifically comprises the steps: setting radar system parameters, building a time-varying motion model of an unmanned aerial vehicle cluster, and obtaining the formation pose distribution of the unmanned aerial vehicle cluster under a global coordinate system; component decomposition is carried out on an unmanned aerial vehicle CAD model, shielding analysis is carried out under the radar sight line constraint condition, time-varying scattering center parameter extraction is carried out according to the dynamic electromagnetic effect between targets, scattering center space distribution of an unmanned aerial vehicle cluster is updated in a time sequence recursion mode, and construction of a time-varying scattering center model is completed; a radar system signal model is constructed according to digital array radar DAR parameters, multi-dimensional dynamic feature extraction and parameter mapping are carried out, digital array radar multichannel signal modulation and unmanned aerial vehicle cluster target radar echo construction are completed, and radar scattering electromagnetic characteristic modeling of a dynamic unmanned aerial vehicle cluster is realized. According to the method, the model description accuracy is improved, and the calculation process is more efficient.

The invention discloses a high-precision point cloud map construction method based on pose map optimization, and the method comprises the steps: preliminarily detecting the observation reliability of each frame of GNSS, and screening credible data; a priori factor is constructed for initial position GNSS signal quality, and map coordinate anchoring is completed; estimating a relative pose between adjacent frames, and constructing an inter-frame constraint edge; scanning context global descriptors are introduced to realize efficient screening and accurate matching of loopback candidate frames; further evaluating the quality of the GNSS by using a loopback detection result and the pose of the laser odometer, setting a GNSS factor weight, and constructing a GNSS factor edge; constructing a complete pose graph model, performing overall optimization on all pose nodes by using a nonlinear optimization method, and correcting accumulative errors; and converting the laser point cloud into a global coordinate system, and splicing to generate a global consistent high-precision point cloud map. According to the invention, the global positioning capability of the high-quality GNSS data and the local geometric precision of the laser point cloud can be effectively fused, and the high-precision point cloud map can be continuously and stably constructed.

The invention relates to a loopback detection method based on active vision and text semantics. The loopback detection method comprises the steps that the pose of a mechanical arm is changed to drive a camera to conduct image collection on the surrounding environment to obtain a front view text image; synchronously controlling the laser radar to collect multiple frames of point clouds to screen key frame point clouds; selecting a key text image corresponding to the key frame point cloud from the multiple frames of front view text images, extracting text information in the key text image, and converting the text information into a global coordinate system; calculating the similarity between the text information in the key text image under the global coordinate system and the historical text information obtained at the historical moment, and determining candidate loopback point clouds according to the similarity; and performing geometric consistency calculation on the candidate loopback point clouds to obtain a distance value so as to obtain a loopback detection result. The technical problem of loopback detection errors caused by feature degradation, sparse or similar environments is solved by actively and visually collecting the front view image of the text information and combining the double loopback verification of the point cloud, and the precision and robustness of system positioning and mapping are improved.

The invention provides a method for automatically calibrating collected data of high-precision measurement and control deformation monitoring equipment, and belongs to the technical field of wind tunnel inner profile measurement. Multi-equipment clock synchronization is realized by establishing a unified time reference system, and measurement data of different frequencies are processed by adopting a CPU-GPU heterogeneous parallel processing architecture, so that the measurement accuracy is improved. The method comprises the following steps: performing three-level alignment on time, space and precision dimensions by using a multi-level clustering algorithm, eliminating clock differences among equipment by using a clock drift correction equation set, establishing a space coordinate system conversion matrix to unify a global coordinate system, and performing adaptive data fusion according to a precision weight by using a Kalman filtering fusion algorithm. And a data quality evaluation system is established to ensure the accuracy of a calibration result, a high-precision calibration data set is finally output, and the technical problem that the measurement precision is reduced due to space-time inconsistency of heterogeneous data of multiple devices is solved.

The invention discloses an underground environment laser radar and millimeter wave radar cooperative SLAM method, and relates to unmanned driving positioning. The method comprises the following steps: collecting point cloud data of an underground environment by using a laser radar, and carrying out filtering processing; collecting target data by using a millimeter wave radar and carrying out filtering processing; curvature-based feature extraction is carried out on the preprocessed point cloud data to obtain a point cloud feature set, spatial correlation is carried out on a target point set with a speed label and the point cloud feature set, point cloud features corresponding to a dynamic target are identified and eliminated according to the speed label, and a static environment feature set is obtained; inter-frame matching and pose estimation are carried out based on the static environment feature set, a static target in a target point set with a speed label is used as an additional constraint, and an optimized pose sequence is obtained through a graph optimization method; and projecting the static environment feature set to a global coordinate system according to the optimized pose sequence, and constructing a static environment map. According to the invention, the interference of the dynamic target on the SLAM is avoided.

The invention relates to the technical field of intelligent welding, in particular to a distributed reinforcement learning control method and system for multi-robot cooperative welding, each welding robot collects vision measurement information under a global coordinate system by using a local vision sensor, and forms self observation information in combination with robot state information obtained by a self-sensor; the information is input into respective Actor-Critic networks, an Actor-Critic algorithm is executed, control actions are generated, welding operation is achieved, visual measurement information between the robots is shared through a 5G communication network, cooperation efficiency is enhanced, the Actor-Critic networks are trained with a cross entropy loss function as a target and with a two-norm of an action value as a constraint condition, and the robot vision measurement information is obtained. The target network adopts a soft update algorithm, the parameter update efficiency is improved, the central control bottleneck is eliminated by the distributed reinforcement learning architecture, and the robustness and reliability of the system are enhanced.

The invention relates to a BEV space drivable area boundary automatic marking method based on a point cloud, and the method comprises the steps: obtaining a continuous frame point cloud data stream and corresponding positioning data, and separating ground and non-ground point clouds through a pavement segmentation algorithm; and distinguishing initial dynamic and static point clouds, and eliminating static point cloud noise. And projecting the ground point cloud to a global coordinate system, and combining multiple frames to generate a dense global ground point cloud. Dividing point clouds into voxel grids according to an XY plane, extracting static obstacle point clouds and updating ground point clouds; and projecting the static obstacle point cloud to a global coordinate system to generate a dense static point cloud. Misclassified ground points in the initial dynamic point clouds are removed to obtain dynamic obstacle point clouds, and the dynamic obstacle point clouds and the static point clouds are combined into comprehensive obstacle point clouds. And on the basis of the updated ground point cloud and the comprehensive obstacle point cloud, generating a drivable area boundary in the BEV view through a ray scanning algorithm, and outputting the drivable area boundary to a user interaction interface, so that high-precision automatic marking is realized, obstacles in any form are covered, and the manual marking cost is reduced.

The invention provides a multi-dimensional map generation method and device based on multi-sensor tight coupling SLAM. The method comprises the following steps: acquiring image data, point cloud data and inertial data acquired by using a predetermined sensor combination; state propagation is carried out based on the inertial data, and positioning output under a global coordinate system and an estimation result used for point cloud position correction are obtained; generating a three-dimensional map and a semantic map; geometric elements are extracted based on the three-dimensional map, and a geometric annotation map is formed by global coordinates according to the geometric elements; projecting the spatial information to a plane, and superposing ground color textures from the image data to generate a two-dimensional texture grid map; and associating the positioning output, the three-dimensional map, the semantic map, the geometric annotation map and the two-dimensional texture grid map, and providing an association result for a positioning and path planning module of the mobile equipment to call. The multi-dimensional map can be synchronously output through one-time mapping, the coordinates of the four layers are uniformly updated in real time, and the manual labeling cost is remarkably reduced.

The invention provides a parking visual positioning method and system based on parking space semantic information and a vehicle-mounted terminal.The method comprises the steps that an aerial view image of a vehicle in a parking environment and a global parking space semantic map are obtained, and the global parking space semantic map is constructed according to the parking environment; inputting the aerial view image into a parking space detection model for parking space detection, and obtaining first parking space semantic information under an image coordinate system; according to the relative position relation between the vehicle and the parking space in the aerial view image, global coordinate conversion is carried out on the first parking space semantic information, second parking space semantic information under a global coordinate system is obtained, and the global coordinate system is constructed based on the parking environment; and matching the second parking space semantic information with the global parking space semantic map, and determining the position of the vehicle in the parking environment. Parking visual positioning is carried out based on the corresponding parking space semantic information of the vehicle in the current parking environment, the position of the vehicle in the parking environment can be accurately determined, and the accuracy of vehicle positioning during parking is improved.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit an uplink message (e.g., a scheduling request) indicating one or more beam directions (e.g., one beam direction, a set of beam directions, a sequence of beam directions), one or more beamwidths (e.g., for the one or more indicated beam directions), an angular area of interest for radar sensing, or a combination thereof. The network entity may then provide, to the UE, a grant of resources for performing the radar sensing on specific radar sensing transmit beams (e.g., that will not interfere with other radar sensing by other UEs, uplink communications form other UEs, sidelink communications by other UEs, etc.). The UE may indicate the directions of the beams with reference to a global coordinate system. The network may configure the UE with lookup tables (LUTs) defining relationships between indices, beam directions and / or beamwidths.

The invention relates to a tunnel three-dimensional holographic splicing method and device for panoramic array camera networking, and the method comprises the steps: enabling a reference transmission camera array and a three-dimensional laser scanner on an inspection vehicle to move to different inspection positions along with the inspection vehicle for adjustment, achieving the transmission of peripheral control information at two ends of a tunnel into a measurement region, and constructing a dynamic control field; the limitation on measurement conditions is reduced; high-precision positioning and attitude determination are carried out based on fixed connection constraints of a look-around camera array and a three-dimensional laser scanner and a constructed dynamic control field, so that a measurement platform is unified to a global coordinate system, and the inherent defect of large accumulative error of a traditional three-dimensional modeling method is effectively avoided; and then the high-resolution look-around image is mapped to the three-dimensional point cloud data under the global coordinate system, so that high-resolution and high-precision three-dimensional holographic splicing of the tunnel ring surface under the global coordinate system is realized, global and local high-resolution and high-precision are achieved, and the acquisition precision of the tunnel three-dimensional hologram is greatly improved.

The invention provides a high-dynamic deformation measurement system and method based on laser section contour differential scanning. The measurement system is composed of a section contour measurement unit, a calibration unit, an error compensation unit and a data processing unit, wherein the section contour measurement unit comprises a multi-sight-line structured light sensor module, a speed sensing module and a distance measurement module; the calibration unit comprises a synchronous control module, an adjustment module and a common tooth-shaped calibration target; the error compensation unit comprises a vibration detection module, a posture detection module and a temperature detection module; the data processing unit is a device with programming and computing functions. The measuring method comprises the following steps: (1) calibrating a laser plane and an image plane; (2) unifying a global coordinate system; (3) obtaining point cloud data; (4) error compensation; (5) constructing a deformation time-history model; and (6) carrying out maximum deformation global optimization. According to the measurement system and method, accurate expression and maximum value global optimization of a pavement micro-deformation three-dimensional space under the load effect can be realized.

The invention discloses a welding track collecting method based on vision, and relates to the technical field of welding. An ArUco box installed on a welding gun and an ArUco code on a reference ruler are collected in real time through a visual collection unit, ID information and position information of the ArUco code are recognized, the pose of the ArUco code on the ArUco box serves as the pose of the welding gun, the pose of the ArUco code on the reference ruler serves as the pose of the reference ruler, and the poses of the welding gun and the reference ruler in a camera coordinate system of the visual collection unit are obtained; the method comprises the following steps: uniformly converting an ArUco code pose of an ArUco box on a welding gun to an ArUco box geometric center coordinate system, uniformly converting an identified ArUco code pose of a reference ruler to a reference ruler global coordinate system, converting a welding gun pose from the ArUco box geometric center coordinate system to a reference ruler global coordinate system, and carrying out fusion processing on acquired data of a plurality of cameras on a visual acquisition unit, and the accurate pose and motion trail of the welding gun are obtained.

The invention relates to a visual analysis method for traffic intersection collaborative perception, and the method comprises the following steps: S1, employing an intersection rapid calibration algorithm, and obtaining camera images at different positions; s2, for the camera image of each position, inputting a target detection model to obtain a target detection result; s3, inputting the target detection result of each position into a target tracking model, and outputting a target tracking result; s4, converting the target tracking result into a unified global coordinate system based on the affine transformation matrix, and executing global cross-lens ID matching to obtain a cross-camera tracking result; s5, smoothing the cross-camera tracking result to obtain a smooth tracking result; and S6, performing visual perception based on the smooth tracking result to obtain a visual perception result. Compared with the prior art, the method has the advantages of ensuring that the same target can be correctly associated under different camera visual angles, realizing cross-camera consistency identification and the like.

The invention belongs to the technical field of measurement and positioning, and particularly relates to an aircraft manufacturing measurement and positioning method, which deeply combines an interference networking measurement method and a space coordinate measurement method of a laser tracker, adopts interference networking to construct a global coordinate system # imgabs0 # by reference control TB points in a small amount of open space, and performs re-system by reference control TB points in an effective distance measurement range. According to the method, an expanded reference ERS point is expanded, a local expanded reference system # imgabs1 # is established to measure and position a local measurement object, the sequence and boundary of global measurement and local measurement in the measurement process according to size chain superposition are defined, and the method has high measurement precision.

The invention provides a small target detection method and device based on a high-resolution fusion feature map. The method comprises the following steps: dividing an image to be detected into a plurality of overlapped sub-regions and recording position information of the overlapped sub-regions; performing feature extraction and multi-scale fusion on each sub-region to generate a high-resolution fusion feature map; generating geometric parameters and initial confidence of candidate frames based on each spatial position on the image, and performing preliminary screening to obtain a candidate frame set of the sub-regions; mapping candidate frames in all the candidate frame sets to a global coordinate system according to the position information, and combining repeated candidate frames pointing to the same small target to form a global candidate frame set; for each candidate frame in the global candidate frame set, cutting a local image area in the to-be-detected image to carry out fine judgment to obtain a fine trimming confidence coefficient; and fusing the initial confidence coefficient and the refined confidence coefficient to obtain a comprehensive confidence coefficient, and outputting a final small target detection result according to a comparison result of the comprehensive confidence coefficient and an adaptive threshold value. According to the invention, the tiny target in the image can be accurately captured.