583results about "Picture taking arrangements" patented technology

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

The invention relates to the technical field of topographic change analysis, in particular to an unmanned aerial vehicle-based topographic change detection system, which comprises a slope direction sensing track control module, a texture structure extraction module, a crack evolution track construction module, a direction trend comparison module and a patrol recheck positioning module. According to the method, a continuous elevation point column of an unmanned aerial vehicle scanning area is extracted, laser reflection point coordinates are fused, a space relation of transition point distribution is constructed, dynamic adjustment of a ground-imitated flight path is achieved, and a texture structure area with continuous directivity is recognized in combination with a high-angle image boundary communication relation; texture boundary evolution is compared at different time nodes to form a crack path, the stability of the path and the slope direction is judged through an included angle sequence, recognition and sorting of areas with the consistent direction are completed, a space comparison result is registered in a three-dimensional coordinate system, terrain change areas are accurately marked, and rapid positioning and continuous tracking of high-risk areas are achieved.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

A method for surveying an environment by a movable surveying instrument with a progressional capturing of 2D-images by at least one camera and applying a visual simultaneous location and mapping algorithm (VSLAM) or a visual inertial simultaneous location and mapping algorithm (VISLAM) with a progressional deriving of a sparse evolving point cloud of at least part of the environment, and a progressional deriving of a trajectory of movement. The method comprises a progressional matching of the sparse evolving point cloud with a previously derived 3D-geometry, with a minimizing of a function configured to model a distance between the sparse point cloud and the previously derived 3D-geometry and deriving a spatial localization and orientation of the surveying instrument. At least one surveying measurement value of the environment by a spatial measurement unit is combined with the sparse point cloud or the previously derived 3D-geometry.

The invention relates to the technical field of vehicle control, and discloses a vehicle positioning and control method and system based on machine vision, and the method comprises the steps: building a vehicle kinematics prediction model, and generating a reference pose sequence according to a driving instruction through the vehicle kinematics prediction model; recording a first reference pose during image acquisition; calculating a deviation vector between a positioning result and the first reference pose when positioning calculation is completed; determining a dynamic damping coefficient according to a direction included angle between the velocity vector and the deviation vector, generating a compensation gain sequence, and converting the compensation gain sequence into a compensation torque of a current loop given value; according to the method, the effective decoupling of the sensing time delay of the sensor and the dynamic response of the carrier is realized, the phase lag is reduced on the premise of ensuring the momentum continuity of the controlled object, the instantaneous control oscillation of positioning correction is eliminated, and the mechanical stress impact of an execution mechanism is inhibited.

The invention provides a high-precision real-time multi-channel cooperative control method and system. The method comprises the following steps: establishing a conversion relation among a geodetic coordinate system, a geocentric coordinate system and a station center coordinate system; each device solves the observation data of the target into the global ECEF coordinate of the target through the conversion relation according to the geographic coordinate of the device; fusion of observation data of multiple cameras (integrated in a turntable cabin) is realized by adopting an iterative weighted least square method, and the optimal estimation of the initial position of a target is obtained; and taking the fused position as an observed quantity, inputting the observed quantity into a Kalman filter constructed based on a uniform-speed motion model, dynamically adjusting an observation noise covariance and a process noise covariance, and predicting and tracking a target motion state, thereby realizing multi-camera cooperative control.

The invention discloses an air-ground collaborative observation unmanned system key target intelligent search method, which comprises the following steps that: an unmanned aerial vehicle carries a positioning module to provide a positioning coordinate, and executes a preset fixed-point cruise task; the unmanned aerial vehicle performs data acquisition on the environment of the cruising area; a height self-adaptive target detection module is adopted to carry out target detection on image data collected by the unmanned aerial vehicle, a rescue target and temporary obstacles generated after a disaster are recognized, coordinate transformation is carried out on the obstacles and target points, and a detection result is mapped to a position under a global coordinate system; transmitting the recognized target point and obstacle coordinates to a ground mechanical dog in real time through a communication module; the ground mechanical dog updates a dynamic cost map according to the received environment information to realize real-time perception of the environment; and the ground mechanical dog performs path planning based on the environment map. According to the method, efficient perception and rapid rescue of the target point can be realized, and the task execution efficiency and the system robustness in the post-disaster complex environment are remarkably improved.

The invention discloses a real-time monitoring and early warning method for deformation of a foundation pit support based on oblique photography, and relates to the technical field of photogrammetry and deformation measurement, high-resolution images of a support structure are acquired in a multi-angle manner through a multi-stage route, and a high-density three-dimensional point cloud reference model is constructed after preprocessing; then periodically acquiring a monitoring point cloud, and realizing high-precision space alignment with the reference model through control point coarse registration and iterative nearest point fine registration; and finally, calculating the three-dimensional coordinate deviation of each point through point cloud matching, extracting the full-surface deformation, and generating a deformation cloud picture and a statistical report. According to the method, the technical spanning from discrete point monitoring to full-field continuous monitoring and from low-dimensional data to true three-dimensional vectorization deformation analysis is realized, and the comprehensiveness and accuracy of deformation monitoring are improved.

The invention relates to the technical field of distribution network unmanned aerial vehicle autonomous tour-inspection navigation, in particular to a distribution network unmanned aerial vehicle tour-inspection autonomous navigation method based on GPS information and visual information, which comprises the following steps: acquiring a GPS and smoothing projection coordinates, constructing a Thiessen region random index, optimizing a route and acquiring a flight image, evaluating a risk and adjusting exposure parameters, and generating a light environment value. According to the method, an environment model is constructed by fusing GPS information, Gaussian projection is adopted to eliminate earth curvature errors, moving average filtering is adopted to suppress signal noise, Thiessen polygon is utilized to quantify routing inspection point and obstacle distribution, a tornado optimization algorithm is combined to generate an optimal route, the shortest path without repetition and obstacle avoidance are realized, and obstacles are identified based on a YOLOv12 model. A risk avoiding strategy is made according to the risk value, a route is dynamically optimized, a light environment fingerprint database is constructed, illumination change is predicted in combination with LSTM, aperture shutter sensitivity and white balance are adjusted in a self-adaptive mode, image brightness and definition are optimized, and inspection efficiency and data reliability are improved.

Satellite images have inherent geo-positional errors of orders a few meters. Corrections are achieved by adjusting a sensor model which maps ground coordinates of control features into image coordinates and establishing a correspondence between the ground and image features, in this case a road network. The ground coordinates are obtained from mobile pose points. To adjust the sensor model we rely on the fact that the roads are typically much more uniform than surrounding features, and therefore have smaller entropy. The sensor model is adjusted so that the image pixels, obtained from projecting ground coordinates of the mobile pose points onto the image, minimize the entropy of the pixels that represent the road network.

The invention discloses a bionic visual positioning system and method based on multispectral perception and a storage medium, and the system comprises a front-end multidimensional optical perception module, a mesopic vision preprocessing module and a rear-end fusion perception and target positioning module, the front-end multi-dimensional optical sensing module is used for performing optical imaging, spectral decomposition and polarization imaging; the mesopic vision preprocessing module is used for carrying out image preprocessing and optical flow estimation; and the rear-end fusion perception and target positioning module is used for feature fusion and target identification and positioning. According to the bionic visual positioning mechanism, through a bionic compound eye structure + multispectral + polarization + optical flow fusion perception strategy, the perception bottleneck of a traditional visual system in a high-dynamic and complex illumination environment is effectively solved, and the bionic visual positioning mechanism has the advantages of high robustness, low delay, wide field of view, multiple modes and the like; the method is especially suitable for application scenes such as automatic driving, unmanned aerial vehicles, robots and the like with extremely high requirements on real-time performance and reliability.

The invention discloses an unmanned aerial vehicle video live-action map generation method and device, a medium and equipment, and belongs to the field of map generation. According to the method, the real-time video data and the pose information of the multiple unmanned aerial vehicles are acquired, projection conversion is carried out, the pose estimation is carried out by utilizing the visual SLAM algorithm and combining the converted pose information, and the camera pose matrix is constructed. And further, processing the video data by adopting a preset densification and orthorectification method in combination with the camera attitude matrix to obtain each video frame. A video frame is divided into a non-overlapping area and an overlapping area, the non-overlapping area is directly integrated to a global map, the overlapping area is divided into sub-maps, overlapping information is processed and then integrated to the global map, and finally a panoramic base map is formed. And finally, integrating the panoramic base map with the 3D GIS through a projection texture mapping technology, and generating an unmanned aerial vehicle video live-action map. The problem that the video live-action map of the unmanned aerial vehicle cannot be accurately and efficiently generated in the prior art is effectively solved.

The invention provides an unmanned aerial vehicle forest fire prevention smoke intelligent early warning method, device and equipment and a medium, and the method comprises the steps: detecting a smoke concentration value in a target inspection region at a current moment, when the smoke concentration value is greater than a preset concentration value, acquiring a multi-frame image sequence in a first preset time period and acquiring a multi-frame image sequence in a second preset time period; smog feature analysis is carried out on the multi-frame image sequence in the first preset time period and the multi-frame image sequence in the second preset time period to obtain smog multi-dimensional features; a thermal imaging data sequence in a second preset time period is collected, and flame multi-dimensional features are extracted; based on the smog multi-dimensional features and the flame multi-dimensional features, a fireproof early warning grade is judged; if the fireproof early warning level is greater than the preset early warning level, fusing the fire source navigation positioning information and the fire source dead reckoning information to obtain a fire source position coordinate; and generating a fireproof early warning prompt based on the fire source position coordinates, and sending the fireproof early warning prompt to a ground control center. Therefore, the forest fire prevention efficiency is effectively improved.

An unmanned aerial vehicle autonomous electric power facility inspection method based on three-dimensional semantic driving relates to the technical field of unmanned aerial vehicle cruise control, and comprises the following steps: constructing a three-dimensional semantic representation containing key components under a unified world coordinate system, and generating a three-dimensional semantic point cloud with component category labels in real time in combination with airborne visual perception; and the inspection requirements in the operation and maintenance regulation are analyzed into an ordered component-level task sequence, and on the basis, the unmanned aerial vehicle autonomously plans an observation pose and a local flight path according to the three-dimensional semantic distribution of the target component, so that the adaptive approaching and coverage of the key component are realized. According to the invention, operation and maintenance semantics can be precisely followed, fine-grained sensing of key parts is realized, an inspection path is enabled to be close to a concern point, environment adaptability is realized, the inspection efficiency is effectively improved, the cost is reduced, and the problems that current electric power facility unmanned aerial vehicle inspection depends on a preset mode, the operation and maintenance semantics are difficult to meet, and key part sensing and adaptability are lacked are solved. And the inspection tour effect is not good enough.

Methods, systems and apparatus, including computer programs encoded on computer storage media for unmanned aerial vehicle flight operations near physical structures or objects. In particular, a point cloud of the physical structure is generated using aerial images of the structure. The point cloud is then referenced to determine a flight path for the UAV to follow around the physical structure, determine whether a planned flight path to desired locations around the structure is possible, determine the fastest route to return home and land from a given position around the physical structure, determine possibility of inflight collision to surface represented in point cloud, or determine an orientation of a fixed or gimballed camera given a position of the UAV relative the point cloud.

The invention discloses a visual imaging method and system combined with unmanned aerial vehicle multi-temporal automatic comparison, and the system comprises an unmanned aerial vehicle aerial survey module which is used for carrying out the repeated aerial survey of the same target region at different time points, and obtaining multi-period image data; the data transmission and storage module is used for transmitting the multi-period image data acquired by the unmanned aerial vehicle aerial survey module in real time; the method has the beneficial effects that the aerial survey of the unmanned aerial vehicle supports dynamic route adjustment, multi-sensor cooperative acquisition is realized, the overlap ratio of multiple aerial survey paths is high, the image data integrity and consistency are high, and the manual intervention and data supplementary acquisition cost is greatly reduced; multi-stage image automatic registration, feature extraction and change detection are realized through deep learning, the processing precision is high, a large amount of manual operation is not needed, and the efficiency is remarkably improved; three-dimensional modeling and multi-mode comparison are supported, dynamic changes of ground objects can be clearly displayed, interaction functions are rich, and the requirements for single-person detail checking and multi-person collaborative rechecking can be met.

The invention discloses a space-based double-satellite asynchronous detection multi-target correlation positioning system and method, the system comprises space-based optical satellites and a central processing unit, each space-based optical satellite is equipped with a large-view-field optical electro-telescope, and the large-view-field optical electro-telescope is used for carrying out image acquisition on a space target; and the central processing unit comprises a computer, the computer is in communication connection with the space-based optical satellite, and an image processing module, an angle measurement data generation module, a data association module and a target positioning module are integrated in the computer. According to the characteristics of space-based double-star asynchronous observation, a complete process from image processing, angle measurement data generation, time series data association to final positioning is designed, and the method is particularly suitable for monitoring and positioning a large number of space non-cooperative moving targets; by introducing time registration and sequence geometric consistency judgment, the association problem caused by asynchronous double-star observation time is effectively solved, and the accuracy and robustness of data association in a multi-target scene are improved.

The invention relates to an unmanned aerial vehicle towed body positioning attitude measurement method based on binocular vision positioning, and relates to the technical field of computer processing, and the method comprises the following steps: S01, constructing a measurement system which comprises an unmanned aerial vehicle and a towed body connected with the unmanned aerial vehicle through a cable; the unmanned aerial vehicle carries a GNSS module, an IMU module and a binocular camera, and the upper surface of the towed body is provided with a coding pattern and carries a sonar sensor; s02, establishing a towed body coordinate system by taking the acoustic center of the sonar sensor as an original point, obtaining three-dimensional coordinates of feature points on the towed body in the towed body coordinate system, and generating a three-dimensional space model operation simulation model of the towed body; and S03, the binocular camera is calibrated. According to the invention, the problem of underwater topographic survey of a near-shore high-flow-velocity turbid water area is solved, and high-efficiency, low-cost and high-precision surveying and mapping operation is realized.

The invention provides an underwater terrain inversion method based on a height measurement satellite, belongs to the technical field of remote sensing geoscience application, and constructs an automatic processing flow integrating space screening, elevation correction, signal denoising, double-strategy surface and bottom recognition and physical correction. The method is suitable for underwater topography generation in complex water body environments such as lakes and reservoirs, and the efficiency of underwater topography data acquisition is greatly improved. A strategy of combining adaptive kernel density estimation and clustering analysis is adopted to remarkably improve the water surface and water bottom signal recognition capability, and a multi-temporal collaborative analysis and refractive index physical correction model is introduced to realize high-precision underwater terrain extraction of water bodies such as inland reservoirs. The method has obvious advantages in the aspects of automation degree, application range and result stability, and has important engineering application and popularization value.

The invention relates to the technical field of disaster site surveying and mapping, and discloses a disaster site three-dimensional surveying and mapping system based on an unmanned aerial vehicle. According to the system, an unmanned aerial vehicle is controlled to carry a multispectral sensor to carry out omnibearing scanning on a disaster site, and earth surface deformation data and multiband reflectivity data are collected; carrying out time sequence alignment and noise filtering on the collected data, constructing an initial three-dimensional point cloud through dynamic voxelization processing, and fusing inertial measurement unit data to correct a spatial position; identifying earth surface fault zone edge and elevation mutation features from the point cloud, and dividing geological stability partitions by combining reflectivity data; a self-adaptive octree segmentation algorithm is adopted to carry out multi-level detail division on the stability partitions, the grid resolution is automatically adjusted according to the fault zone density, and a disaster site three-dimensional topological structure is generated; by comparing historical and current three-dimensional data, the earth surface displacement vector and the settlement gradient are calculated, a potential secondary disaster risk area is marked, and accurate assessment and risk early warning of a disaster site are achieved.

The invention discloses a power distribution overhead line inclination angle measurement method, system and device and a medium, and belongs to the technical field of power distribution frame measurement, and the method comprises the steps that an unmanned aerial vehicle collects original data and unmanned aerial vehicle flight data according to a preset flight route; preprocessing the original data to obtain effective traverse points; conducting wires in the power distribution network frame are processed, and the processed conducting wires are utilized to construct an initial three-dimensional model through the segmented weighted catenary model and the effective conducting wire points; establishing a compensation equation according to the flight data of the unmanned aerial vehicle, processing the compensation equation through Kalman filtering to obtain an error, and adjusting the three-dimensional coordinate of the effective traverse point by using the error to obtain a corrected effective traverse point; and correcting the initial three-dimensional model by using the corrected effective traverse points to obtain a final three-dimensional model, and outputting an obtained measurement result. The problems that traditional manual measurement is time-consuming and labor-consuming, and an existing unmanned aerial vehicle inspection technology is easily interfered by the environment are solved.

An aspect of the disclosure provides An earth observation apparatus to be carried by a moving aerial platform or satellite for obtaining images of the surface of the earth, the apparatus comprising: an optical train having an optical field of view for imaging a region of the surface of the earth and being configured to form an image of the region at an image plane; an image sensor disposed at the image plane providing an imaging field of view; a view adjuster configured to control the optical train to: provide forward motion compensation for a stare time; and to displace the image, relative to the imaging field of view, in an across-track direction in a sequence of discrete displacement steps during each stare time; wherein the image sensor comprises a plurality of active areas, each comprising an area array detector and the active areas being spaced apart by inactive areas at the image plane wherein each active area captures a frame of image data for each discrete displacement step thereby to capture a plurality of frames for each discrete displacement step and the plurality of frames captured for each discrete displacement step are displaced in the across-track direction, relative to the imaging field of view, from the plurality of frames captured for the next discrete step.

The invention relates to the technical field of aerial survey of unmanned aerial vehicles, in particular to a mountainous area aerial survey data processing method and device based on an unmanned aerial vehicle and a storage medium. The aerial survey assembly comprises a main control module, a high-precision positioning module, a laser radar module, a multispectral imaging module, a hardware synchronization module, a data fusion processing module and a wireless transmission module. The system has the advantages that the synchronization of data acquisition is ensured through aerial survey equipment integrating the high-precision positioning module, the laser radar module and the multi-spectral imaging module and the hardware synchronization module, the problem of space-time inconsistency of multi-source remote sensing data in a mountainous area complex environment is solved, and the multi-source remote sensing data is obtained through a combined adjustment processing method. The synchronously collected multispectral image and laser point cloud data are subjected to combined processing, precise three-dimensional coordinates provided by the laser point cloud constrain aerial triangulation of the multispectral image, and texture information of the multispectral image corrects a point cloud splicing error.

The invention provides a photogrammetry control method and device based on a laser galvanometer and electronic equipment, and belongs to the technical field of photogrammetry, and the method comprises the steps: controlling the laser galvanometer to deflect according to a preset scanning path in a single exposure period of a camera, so as to form n laser gratings in a single frame image collected by the camera; extracting feature information of each laser grating from the single-frame image, wherein the feature information comprises pixel coordinates in a space coordinate system; and calculating three-dimensional coordinate points of the surface of the object to be measured according to the real-time deflection angles of the laser galvanometer when the laser galvanometer projects the laser gratings, the feature information of the laser gratings and pre-calibrated camera parameters, and summarizing to generate three-dimensional point cloud data. According to the invention, the precision of photogrammetry is improved.

The invention relates to the technical field of Beidou high-precision positioning, provides an urban ventilation corridor planning method and system based on Beidou high-precision positioning, and solves the problem of poor accuracy and effectiveness of urban ventilation corridor planning. The method comprises the following steps: obtaining surface temperature distribution, building morphological parameters and three-dimensional point cloud data of a candidate region of an urban ventilation corridor, and filtering the three-dimensional point cloud data to construct a curved surface model; acquiring surface roughness distribution from the curved surface model, and aligning the surface roughness distribution, surface temperature distribution and building form parameters through a unified space coordinate reference provided by a Beidou high-precision positioning technology; a deep learning model is adopted to process the aligned temperature distribution and building form parameters, and a ventilation weak area and a cold source inlet are identified; based on a path planning algorithm, the urban ventilation corridor planning path avoiding the weak ventilation area is generated by taking roughness minimization and cold source connectivity maximization as targets, and the accuracy and effectiveness of urban ventilation corridor planning can be improved.

The invention provides a gantry type image measuring instrument, and relates to the field of photogrammetry. Comprising a measuring instrument body and a portal frame arranged on the measuring instrument body. The camera shooting assembly is used for camera shooting measurement; the protective belt is annular and light-permeable, and the protective belt can be movably adjusted in the length direction of the protective belt; side plates are arranged on the front side and the rear side of the protection belt, and a protection cavity used for surrounding the camera shooting assembly is defined by the protection belt and the two side plates; the cleaning mechanism is arranged on one side of the bottom of the protective belt; the number of the inflation sealing rings is two, the two inflation sealing rings are arranged at the edges of the two side plates respectively, and the two inflation sealing rings are attached to the front side edge and the rear side edge of the protection belt respectively; and the inflator mechanism is arranged in the protection cavity. The camera shooting assembly is protected through the closed protection cavity defined by the protection belt and the side plates, meanwhile, the protection belt replaces a lens, cleaning is conducted through the cleaning mechanism, and the lens is prevented from being directly brushed and cleaned to cause abrasion.

The invention relates to the technical field of deep learning, and particularly provides a joint classification method for hyperspectral images and laser radar data. The method comprises the following steps: firstly, extracting multi-scale features of multi-modal data through a decoupling three-branch encoder, and then carrying out attention guidance cross-modal fusion on three initial features of the same scale through an autonomously designed feature fusion module to obtain three multi-modal fusion features of different scales; inputting the deepest fusion feature into a weight sharing decoder, and obtaining pseudo data through a reconstruction task; and the real data and the pseudo data are identified by the structure consistency perception discriminator, and the discrimination loss is returned, so that the encoder parameters are updated, and iteration is performed to the optimal. And finally, inputting the three-scale fusion features obtained based on the optimal parameters into a multi-level feature fusion classifier to complete classification, thereby effectively improving the classification precision, and realizing the adaptive fusion and enhancement of the multi-modal features.

A smart satellite system is capable of decision making and prioritization on the fly to optimize the use of downlink bandwidth to deliver prioritized data based upon opportunity and the resources of available payloads. By providing a satellite system with substantial processing power and a level of autonomy, the satellite is able to make decisions about capturing imagery data, including image data processing, object detection, image segmentation, and re-orientation of the satellite based upon the opportunity, from the satellite's perspective, for capturing image data of areas or objects of interest. Through the use of machine learning and in-orbit image analysis, the satellite may transmit only a subset of the captured images, portions of the captured images, or the result of image analysis, thereby efficiently using the downlink communication channel.

The invention discloses a full-leg ship yaw early warning method based on a camera array and an AIS, and the method comprises the steps: deploying a monitoring system based on the camera array in a target water area, and collecting the image data of the target water area; performing laser scanning on the target water area through the unmanned aerial vehicle to obtain laser scanning information; fusing the image data with the laser scanning information to obtain a video monitoring track of the ship; track matching is carried out on track data obtained by an automatic identification system (AIS) and a video monitoring track of the ship; after track matching is completed, weights are distributed according to the reliability of all the data sources, and tracks of all the data sources are fused; and on the basis of the fused trajectory, target water area danger grade division and ship yaw early warning are realized. According to the invention, blind areas monitored by a single camera are effectively compensated, the ship target identification capability is improved, and high-precision space positioning of ship tracks in a navigation channel is realized. And ship multi-source trajectory fusion is realized by monitoring ship trajectory change at the edge of the camera array.