1051results about "Picture interpretation" patented technology

The invention relates to the technical field of image processing, in particular to a dual-branch coding desert segmentation model network structure based on structure state space duality, which adopts multi-dimensional dynamic convolution to replace traditional convolution in the initial stage of an encoder, introduces a mamba2 module based on the structure state space duality into the backbone design of the encoder, and improves the robustness of the encoder. The efficiency and adaptability of the model are remarkably improved, a double-branch parallel design is adopted, one branch uses cavity convolution to extract multi-scale context information, the other branch reinforces feature expression through a mamba2 module, the model is connected in series with a space attention module and a channel attention module between an encoder and a decoder, and the algorithm is more accurate. The method has the advantages that the method is simple and easy to implement, interference of irrelevant information on segmentation results is suppressed, a deformable large kernel attention module is introduced to the tail end of a decoder, and global and local modeling capability of the model in processing desert complex boundary regions is effectively improved by combining flexibility of deformable convolution and global receptive field characteristics of large kernel convolution.

The invention relates to the technical field of unmanned aerial vehicle perception, and discloses a visual navigation method and system of a six-rotor full-drive unmanned aerial vehicle, a terminal and a storage medium. A navigation framework integrating dynamic visual perception and full-drive control characteristics is constructed, and a visual encoder is adopted to extract multi-scale environment characteristics; according to the method, time sequence information is processed in combination with a long-short-term memory network, efficient feature extraction and motion trail prediction of an image sequence under high-speed rotation and violent attitude change of the unmanned aerial vehicle are achieved, the problem of feature tracking loss of a traditional algorithm in a high-dynamic scene is solved, global correlation modeling of environment features is enhanced through an attention mechanism, and the tracking accuracy of the unmanned aerial vehicle is improved. According to the six-rotor-wing full-drive unmanned aerial vehicle navigation method, the navigation precision and robustness of the unmanned aerial vehicle in a complex dynamic environment are remarkably improved by combining control instruction generation of dynamics constraints, high-precision inertial navigation compensation is not needed, and an efficient and reliable navigation solution is provided for application of the six-rotor-wing full-drive unmanned aerial vehicle in emergency rescue, high-speed inspection and other scenes.

The invention provides a three-dimensional modeling and mapping method, system and device based on a fixed-wing unmanned aerial vehicle, and relates to the technical field of geographic mapping. According to the method, through synchronous data acquisition of an inclined camera, a laser scanner and a position and attitude measurement system, and in combination with navigation trajectory calculation, point cloud coordinate transformation, image attitude calculation and multi-source data fusion processing, space collaboration of image data and point cloud data is realized under a unified coordinate system. And performing aerial triangulation based on the fused data, further carrying out spatial modeling and texture mapping, and effectively improving geometric consistency and modeling continuity among multi-source data. According to the method, ground control points do not need to be arranged, high-precision and high-consistency geographic space information expression can be achieved, and real scene modeling and multi-type surveying and mapping data result generation in a large-scale mapping scene are supported.

The invention relates to the technical field of digital surveying and mapping of constructional engineering and construction quality control, and discloses a house surveying and mapping system for constructional engineering, which comprises a data acquisition module for synchronously generating a panoramic image signal and an inertial measurement unit signal; the preprocessing module receives a panoramic image signal and an inertial measurement unit signal, and fuses the panoramic image signal and the inertial measurement unit signal through a visual inertial odometer to generate a pose calibration signal; the fusion modeling module receives the purification point cloud signal and the pose calibration signal, and generates building component parameterized data based on a deep learning component recognition algorithm; the result output module receives the building component parameterized data and generates a construction deviation analysis report based on a preset tolerance threshold value; the intelligent control module generates an acquisition quality feedback signal through a real-time quality evaluation algorithm to the data acquisition module so as to adjust a scanning path, and sends a dynamic sampling rate control signal to the preprocessing module so as to optimize a calculation load. The problems of low house surveying and mapping efficiency, poor modeling precision and weak field real-time feedback are solved.

The invention relates to the technical field of path planning, in particular to a path planning method and system for an inspection robot, and provides the following scheme: executing an inspection task through an initial electronic map, collecting first image data and pose information, recognizing an inspection target, completing image registration, and generating second image data; inverting and calibrating the optimal observation direction based on the structural sensitivity distribution of the local visual features and the illumination variation trend, and determining the shooting position and angle to form a path node; a multi-objective optimization model is adopted, the path length, the view angle continuity and the illumination interference are comprehensively considered, and an optimal inspection path is generated; the method is suitable for outdoor inspection scenes with complex structures and non-uniform target distribution.

The invention discloses a highway engineering road and bridge measurement method based on BIM, and belongs to the technical field of highway engineering measurement. According to the method, the problems that the modeling efficiency and accuracy are difficult to consider, the modeling precision of a non-important area is redundant and the like in the existing road and bridge measurement method for the highway engineering are solved. The method comprises the following steps: acquiring satellite positioning data by using a satellite positioning system in a measurement area, acquiring point cloud data by using laser scanning equipment, and acquiring road and bridge images by using image acquisition equipment; converting satellite positioning data into unified coordinate system data information, carrying out spatial registration on the unified coordinate system data information and the point cloud data, and dividing an important region and a non-important region; constructing a precise three-dimensional point cloud model for the important area, constructing a path point cloud model for the non-important area, and integrating road and bridge images; and finally fusing the model to obtain road and bridge parameters. The method can improve the model efficiency and reduce the model resource overhead while effectively improving the measurement precision, and is suitable for the measurement work of roads and bridges of highway engineering in various environments.

The invention discloses a robot gait self-adaptive adjustment method and system combined with topographic feature recognition, and relates to the technical field of robot gaits, and the method comprises the steps: collecting a topographic image in front of a robot advancing path, carrying out the deblurring processing, and extracting ground feature data through a lightweight neural network; matching the ground characteristic data with a preset gait control parameter database to obtain a gait control parameter set, and generating a control signal through a proportional-differential control method according to the gait control parameter set; correcting the control signal based on a reinforcement learning algorithm, and generating a target control instruction for driving a joint actuator; in the process of executing the target control instruction, collecting state feedback data in real time, and comparing and optimizing the feedback data with the currently executed target control instruction to realize feedback adjustment; the problems of insufficient terrain recognition precision caused by image blurring, response lag caused by sensing and control decoupling and lack of adaptive ability are effectively solved.

The invention discloses a heading equipment cutting head position calibration method based on image acquisition, which comprises the following steps: S1, acquiring image data and preprocessing the image data; s2, extracting cutting head feature points, and constructing a two-dimensional feature point set; s3, calculating a three-dimensional coordinate by using perspective transformation, and generating a point cloud set; s4, performing rigid registration on the point cloud set and a standard structure model of the tunneling equipment, and iteratively solving a rotation matrix and a translation vector to obtain pose parameters; s5, simulating change tracks of the feature points in continuous frames, constructing a time consistency constraint, and optimizing pose parameters; s6, the optimized three-dimensional coordinates are converted to a reference coordinate system, space alignment is completed, and a position calibration result is generated; and S7, outputting a calibration result for control instruction generation and path planning. According to the invention, high-precision and non-contact three-dimensional position calibration of the cutting head of the tunneling equipment is realized, and operation automation and control precision are improved.

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

The invention provides a heading machine positioning method and system, and relates to the technical field of heading machines, and the method comprises the steps: pre-arranging a target on a heading machine, and building a heading machine gravity center coordinate system; capturing a plurality of frames of images with targets by adopting imaging equipment; performing image recognition on the multi-frame image by adopting a target detection model, extracting a feature point set of an area where the target is located, and generating multi-frame data of each feature point in the feature point set; performing multi-angle fusion on each feature point based on the multi-frame data of each feature point, and converting each feature point from a two-dimensional coordinate in an image coordinate system to a three-dimensional coordinate in a world coordinate system; based on the centroid coordinate system of the heading machine, converting the three-dimensional coordinates of the feature points in the world coordinate system into the three-dimensional coordinates in the centroid coordinate system of the heading machine, so as to generate position information of the heading machine; the problem that an existing heading machine is poor in pose positioning result accuracy is solved.

The invention provides a historical building digital modeling and outer wall diagnosis method based on multi-source data fusion, and relates to the technical field of historical building protection and digitization, the method aims at comprehensively obtaining historical building data and analyzing the condition of an outer wall body, firstly, a building main body image is captured through aerial photography of an unmanned aerial vehicle at multiple angles and multiple levels, and the flight height is 30-80 m; sites are arranged on the ground, millimeter-level scanning is conducted on the building facade through a three-dimensional laser scanner, the distance between every two adjacent sites is 10-30 m, and the sites cover the area 10-15 m away from the ground; the method comprises the following steps: firstly, carrying out multi-source data fusion on a wall body, then carrying out close-range shooting on upward view and typical local parts by using a micro lens of a single lens reflex, then preprocessing various data, and finally, measuring and analyzing the concave-convex condition of the wall body according to fused point cloud data, thereby providing accurate data support for historical building protection and research. By means of unmanned aerial vehicle aerial photography and three-dimensional laser scanning station arrangement, it is ensured that high-quality data is obtained.

The invention provides a bridge dynamic displacement measurement method based on an unmanned aerial vehicle, and relates to the field of bridge health monitoring, and the method comprises the steps: obtaining a bridge image sequence through the unmanned aerial vehicle; obtaining transformation matrixes according to the relation between the other frames of images and the fixed measurement points of the reference frame, correcting the coordinates of the motion measurement points of the corresponding frames of images according to the transformation matrixes, obtaining a corrected bridge image sequence, and further obtaining an initial displacement signal; variational mode decomposition is carried out on the initial displacement signal, a constraint variational optimization problem is constructed, the optimization problem is solved through iteration to obtain each mode component of variational mode decomposition and a corresponding center frequency, and a low-frequency mode component is removed to obtain a displacement signal after secondary correction of the influence of the unmanned aerial vehicle; and obtaining the real displacement according to the scale factor between the image obtained by the unmanned aerial vehicle and the real world. The influence caused by the movement of the unmanned aerial vehicle is corrected, and the accuracy, reliability and stability of a measurement result are effectively improved.

The invention relates to a method for the determination of the carbon balance in a predetermined geographic area, applicable to natural and / or human-managed systems, which integrates a series of known and validated technologies into a single information system on the carbon sequestered by natural and human-intervened systems. A system for determining the total carbon balance of a predetermined geographic area is also provided.

The invention belongs to the technical field of glacier identification, and provides a system for monitoring glacier dynamics by an unmanned aerial vehicle low-altitude flight photogrammetry method, which comprises the following steps that: a glacier area unmanned aerial vehicle low-altitude photogrammetry module selects an unmanned aerial vehicle as required, and plans a route and parameters; the image preprocessing module is used for extracting key feature points from each image by using Pi * 4Dcap; fine three-dimensional information, an ortho-image and a glacier digital surface model DSM are obtained through point cloud generation and densification, and glacier dynamic information can be identified by utilizing the glacier digital surface model DSM after correction and interpolation processing are carried out on the three-dimensional information, the ortho-image and the glacier digital surface model DSM; calculating the elevation, volume, speed and mass change of the glacier based on a digital surface model (DSM) of the glacier; the glacier fissure is identified by utilizing the orthoimage and the glacier digital surface model DSM, and the system can comprehensively and accurately monitor the dynamic information of the glacier.

The invention discloses a building height detection method and device and a storage medium, and relates to the technical field of image data processing, and the method comprises the steps: generating a digital elevation model of a target region and a digital orthoimage of the target region based on an aerial image; inputting the digital orthographic image into a building detection model for building identification to obtain an identification result of the building in the target area; according to an identification result of the building, establishing world coordinates of the building in the digital orthographic image; and determining a building elevation value and a ground elevation value of the building in the digital elevation model according to the world coordinates, and determining the height of the building according to the building elevation value and the ground elevation value. The technical problems of poor precision and low efficiency of building height detection in urban wide-area complex areas are solved, and the precision and efficiency of building height measurement are improved.

The invention relates to an end-to-end spacecraft pose measurement method based on deep learning, and belongs to the field of spacecraft pose measurement, the method uses a sliding window transformer as a feature extraction backbone network, introduces a multi-layer large kernel convolution module, and is used for enhancing a global receptive field and feature expression ability. A rotating head detection network and a position head detection network are arranged in parallel after the backbone network, a rotating head is used for predicting a 3D key point and a projection weight thereof, a position head is used for predicting a 2D key point, a 2D-3D corresponding relation is sent to a differentiatable EPro-PnP module for pose solving, it is ensured that a gradient can be completely transmitted back to a feature extraction layer, and a feature extraction layer is obtained; therefore, a globally optimal solution is obtained, and integrated network training of feature learning and geometric pose solution is realized. According to the method, a complex loss function is constructed, through multi-angle supervision and complementary optimization, it is ensured that the model can accurately predict the 6D pose of the spacecraft, and robustness and generalization performance in a complex environment are enhanced.

The invention provides a settlement detection method and device based on machine vision, belongs to the technical field of underground engineering, and is applied to a settlement monitoring area, a measuring point target is arranged in the area, and a reference point target is arranged outside the area; comprising the steps that S1, a settlement monitoring area is continuously shot, a monitoring image set is obtained, and the monitoring image set covers a monitoring point target arranged in the settlement monitoring area and a reference point target arranged outside the settlement monitoring area; s2, processing the monitoring image set to obtain the relative elevation of each measuring point target; and S3, comparing the relative elevation of each measuring point target with the corresponding initial elevation to obtain the settlement amount of each measuring point target. The beneficial effects are that by arranging the measuring point target and the reference point target, the normal operation of road traffic is not interfered, the road traffic is not easily damaged by rolling of running vehicles, the problem of machine vision wide-area monitoring is solved, and the settlement monitoring area can be monitored more comprehensively and accurately.

The invention relates to a mangrove forest space expansion-atrophy monitoring method based on a continuous time sequence spectral index. The mangrove forest space expansion-atrophy monitoring method comprises the following steps: step (1), preprocessing a remote sensing image; (2) generating an original time sequence; (3) removing abnormal observation data in the time sequence; step (4), constructing a vegetation index time sequence sensitive to the mangrove forest within the year; step (5), extracting a mangrove forest potential area every year based on a threshold method; step (6), identifying and correcting potential areas of the mangrove forest in data abnormal years; (7) capturing the years of space expansion and atrophy of the mangrove forest; (8) drawing a space-time distribution diagram of expansion and atrophy of the mangrove forest; and (9) quantitatively evaluating the annual mangrove forest area increase and loss. The method draws the spatial distribution diagram of mangrove forest expansion and atrophy based on long-time series multispectral remote sensing data, can accurately monitor the time point when the mangrove forest is subjected to atrophy and expansion events, and ascertains the spatial and temporal characteristics of the mangrove forest distribution range.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy earthwork dynamic monitoring method and system based on multi-source point cloud data fusion, and the method comprises the steps: carrying out the identification and standardization processing of airborne LiDAR, ground scanning and oblique photography point clouds, and generating a standard point cloud data set with uniform spatial positions and balanced density; dividing an abrupt slope area, a gentle slope area and a flat area based on topographic gradients, and constructing a grid framework adaptive to topographic features; carrying out weighted fusion by combining the point cloud coverage rate and the normalized terrain gradient to generate high-precision grid elevation data; calculating an earthwork variation distribution matrix by comparing historical elevation models; dynamically analyzing the change trend of the excavation and filling area, and synchronously evaluating the construction progress deviation and the geological disaster risk. According to the method, the problems of insufficient multi-source point cloud fusion precision, poor terrain adaptability and dynamic evaluation splitting in the prior art can be solved, and the monitoring precision and the engineering safety early warning efficiency under the complex terrain condition are remarkably improved.

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.

A system and a non-transitory computer-readable storage medium for executing a method of detecting road anomalies is disclosed. The method includes obtaining a live feed from an end computing device in a vehicle travelling on a road using a camera associated with the end computing device, and inputting the live feed to a machine learning model trained to detect and classify a road anomaly such as potholes, longitudinal cracks, transverse cracks, or alligator cracks on the road. The method further includes determining multiple features of the detected road anomaly such as a size of the road anomaly. The method further includes assessing severity of the road anomaly based on features, and transmitting, via a roadside computing device installed on the road, a notification such as a message, an image, a location, or severity of the road anomaly, to multiple end computing devices in the proximity of the roadside computing device.

The invention relates to the technical field of camera testing, in particular to a camera triangulation distance measurement detection method. Comprising the following steps: selecting a tested camera, and adjusting the position and angle of the camera; according to the positions of the two targets in the image, setting test areas corresponding to different view field angle pictures, and adopting an area detection mode; angular point detection is carried out on the set area, the angular points and sub-pixel-level angular points are obtained, and checkerboard center angular points are judged and corrected; according to a distortion calibration model, selecting a corresponding function to carry out distortion removal calculation on the angular point to obtain a normalized distortion-removed angular point coordinate, and converting the normalized distortion-removed angular point coordinate into an actual distortion-removed pixel coordinate; calculating the calculation distance of two target angular points of each image, establishing a normalized three-dimensional coordinate, calculating the deviation angle of the left and right angular points by using the three points, and combining the measured parameters to obtain the calculation distance of the two angular points; and calculating a precision error. According to the technical scheme, the accuracy of the internal reference calibration data and the reliability of the distortion model can be effectively verified.

The invention discloses a multi-agent collaborative visual navigation reasoning enhancement method and system, and the method comprises the steps: an inference agent analyzes input data, and generates structured output containing a reasoning process and an initial answer; independently analyzing the input data by the evaluator agent, generating an evaluation answer and reviewing the output of the inference agent; comparing whether the initial answer is consistent with the evaluation answer, and if yes, outputting a final answer; if not, calling a bifurcation feedback agent, and generating a core bifurcation point abstract and a visual evidence verification plan; and calling a final decision maker agent to directionally retrieve the video evidence according to the visual evidence verification plan, and outputting a final answer. Four roles of an inference person, an evaluator, a divergence feedback person and a final decision maker are introduced, a complete'generation-evaluation-feedback-optimization 'decision link is constructed, and the cognition and inference accuracy in a complex scene is remarkably improved.

The invention relates to the technical field of geographic information engineering, in particular to a surveying and mapping geographic system based on a remote sensing technology, which is characterized in that a multi-modal remote sensing data acquisition module synchronously acquires satellite remote sensing images, aviation LiDAR point cloud, unmanned aerial vehicle oblique photography and ground Internet of Things sensing data; the data fusion collaboration module carries out geographic coordinate calibration and position coding, and generates a surveying and mapping data set with unified precision in combination with an improved Mamba fusion model; the dynamic terrain high-precision inversion module performs three-dimensional terrain reconstruction, and completes terrain deformation monitoring in combination with an InSAR interference measurement technology and a time sequence analysis algorithm; the real-time error correction module constructs a BP neural network optimized by an ISSA algorithm, and dynamically corrects a terrain three-dimensional model and a terrain deformation result; the intelligent visual application module constructs a digital twinborn simulation model and automatically generates a standardized surveying and mapping result report. Therefore, the problems of weak multi-source data fusion, low terrain inversion precision and the like in the prior art are solved.

The invention discloses an unmanned aerial vehicle tilt video and vector map real-time projection sleeving method, which comprises an unmanned aerial vehicle body, and is characterized in that a video data acquisition module and a video preprocessing module for preprocessing the tilt video acquired by the video data acquisition module are arranged in the unmanned aerial vehicle body; a map data processing module is further arranged in the unmanned aerial vehicle body, an improved ORB algorithm is adopted in the feature extraction and matching module, and a posture correction and projection transformation module used in cooperation with the video data acquisition module is installed in the unmanned aerial vehicle body. And a real-time display module in the unmanned aerial vehicle body displays the sleeved video and vector map on terminal equipment in real time through a signal module arranged in the unmanned aerial vehicle body. The problems that the real-time performance is poor, the nesting precision is reduced, and long-time high-precision nesting cannot be achieved during working in an existing unmanned aerial vehicle tilt video and vector map fusion technology at present are solved.

The invention relates to a sun meridian detection and course angle real-time calculation method based on polarized light field vector characteristics, and aims to solve the problems that a traditional sun meridian detection method is easily interfered in low signal-to-noise ratio scenes such as haze and cloud layer shielding, lens distortion causes polarization angle errors, the false detection rate is high, and the calculation accuracy is high. In order to solve the problems that the real-time performance and robustness of course calculation are affected due to the fact that single polarization angle information is difficult to fully reflect the sun direction, polarization parameter calculation is carried out by utilizing a multi-channel polarization image, and the sun meridian is detected by adopting Hough transform through fisheye lens correction, bilateral filtering and candidate region screening, so that the real-time performance and robustness of course calculation are improved. And a multi-point inversion method is combined to accurately obtain a sun vector and a zenith angle and an azimuth angle thereof, and finally a course angle is calculated in real time. The problems of imaging distortion and insufficient real-time processing in the traditional sun positioning technology can be effectively solved, and a powerful navigation positioning technical guarantee is provided for the fields of unmanned aerial vehicles, autonomous navigation, polarized light navigation and the like.

The present invention relates to a real-time heading angle calculation method based on the vector characteristics of the polarized light field. Aiming at the problems of traditional solar meridian detection methods, such as being easily disturbed in low signal-to-noise ratio scenarios like haze and cloud cover, the occurrence of polarization angle errors and high false detection rates caused by lens distortion, and the difficulty of using single polarization angle information to fully reflect the solar direction, which affects the real-time performance and robustness of heading calculation. This method calculates polarization parameters by using multi-channel polarization images.

The invention discloses a mining area subsidence monitoring method based on unmanned aerial vehicle photogrammetry and LiDAR technology, and the method comprises the steps: carrying out the flight shooting of a mining area through employing an unmanned aerial vehicle, and obtaining the ground surface image data of the mining area; processing the acquired image data through a motion recovery structure algorithm SfM to generate a digital surface model DSM, an orthographic map DOM and a digital elevation model DEM; the method comprises the following steps: scanning a mining area by using an airborne LiDAR system to obtain three-dimensional point cloud data of the surface of the mining area; the differential GNSS technology is adopted to correct LiDAR point cloud data and improve the precision of the LiDAR point cloud data; performing registration fusion on a digital elevation model (DEM) generated by unmanned aerial vehicle photogrammetry and the corrected LiDAR point cloud data in a unified coordinate system; the fused data are processed and analyzed, a mining area subsidence model is constructed, and the mining area surface subsidence condition is dynamically monitored. According to the method, the problems existing in a traditional mining area subsidence monitoring method are effectively solved, and powerful technical support is provided for mine geological disaster prevention and control, ecological environment protection and mining area sustainable development.

The invention provides a dam slope monitoring method and system based on radar interference and aerial survey data, and relates to the technical field of deformation measurement. The method comprises the following steps: acquiring radar interference measurement data of a target dam slope area, and carrying out multi-temporal registration and phase difference processing to obtain displacement time sequence data; determining displacement change data based on the displacement time sequence data, and determining a first risk area according to the displacement change data; controlling the flight platform to collect aerial survey data of the first risk area; performing feature extraction on the image data to obtain an image structure feature map, and performing terrain gridding modeling and curvature calculation on the point cloud data to obtain a terrain curvature distribution map; performing coordinate alignment on the image structure feature map and the terrain curvature distribution map to obtain a combined feature map; and determining a risk scoring value by using the risk scoring function, and determining a second risk area based on the risk scoring value. According to the scheme, the accuracy of dam slope anomaly recognition can be improved while the monitoring coverage is ensured.

The invention discloses an image enhancement and visual SLAM (Simultaneous Localization and Mapping) method and system aiming at a low-illumination scene. The method comprises the following steps: firstly, judging by utilizing an image brightness detection algorithm, and carrying out image brightness adjustment on an image with abnormal current brightness; then carrying out image brightness adjustment based on a mean value adaptive Gamma value, calculating Gamma correction indexes corresponding to different low-illumination images with abnormal brightness, and carrying out brightness correction on the different low-illumination images according to the Gamma correction indexes; and finally, carrying out image contrast adjustment based on a CLAHE algorithm, wherein the CLAHE algorithm is adopted to enhance the contrast of the image after the mean value self-adaptive Gamma correction. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-light environment in the prior art are solved. According to the method, deep learning and a traditional visual SLAM framework are combined, image performance is changed through an image enhancement method to improve image quality, and a feature point extraction mode and matching precision in a low-light environment are improved.