126 results about "Angular point" patented technology

The invention discloses a hand-eye calibration method, device and equipment for a mechanical arm and a storage medium. The method comprises the steps that a calibration plate is fixed to the tail end of a mechanical arm, and the mechanical arm is controlled to move along a preset track, so that the calibration plate is always located in the view field of a camera; under at least three different postures of the mechanical arm, the calibration plate image is collected, and meanwhile the pose of the calibration plate at the tail end of the mechanical arm under a mechanical arm coordinate system is recorded; calculating the pose of the calibration plate in the camera coordinate system through calibration plate angular points in the calibration plate image; and according to the pose of the corresponding calibration plate in the mechanical arm coordinate system and the pose of the corresponding calibration plate in the camera coordinate system, calculating a rotation matrix and a translation vector between the mechanical arm coordinate system and the camera coordinate system so as to complete hand-eye calibration. According to the technical scheme, high-precision hand-eye calibration is achieved, and the stability of automatic application of the robot is remarkably improved.

The invention discloses a projection single attitude calibration method, system and device and a medium, and relates to the technical field of computer vision and projection interaction.The method comprises the steps that a plurality of calibration patterns are generated and projected to a calibration plane, gray level images are collected, corner point detection and sub-pixel refinement processing are carried out, and a projection single attitude calibration result is obtained. The method comprises the following steps: acquiring an angular point corresponding relation between a projector image and a camera image, carrying out iterative solution on a projection matrix of a projector and a projection matrix of a camera, separating an internal parameter matrix and an external parameter matrix, calculating a basic matrix and an essential matrix, and decomposing to obtain a rotation matrix and a translation vector of the camera relative to the projector; camera imaging distortion is estimated, a distortion correction coefficient is obtained, and distortion correction is carried out; and counting a re-projection error generated in the correction process, and adjusting parameters of the projector and the camera until the error meets a preset precision requirement. Projector parameter calculation is completed through projection single posture calibration, posture adjustment is not needed in the calibration process, the automation degree is improved, and meanwhile manual intervention is reduced.

The invention discloses a laser radar-camera fusion calibration method and system, and belongs to the field of calibration. According to the method, after a target is shot through a laser radar and a camera, multiple pairs of corresponding three-dimensional point clouds and two-dimensional images are obtained; angular point coordinates in the phase unwrapped image are obtained through the phase unwrapped image and the two-dimensional real image, three-dimensional angular point coordinates in the three-dimensional point cloud are further obtained, the two-dimensional angular point coordinates and the three-dimensional angular point coordinates are input into an external parameter calibration program of the laser radar and the camera, a rigid matrix between two sensor coordinate systems is obtained, and calibration is completed. According to the method, a fast inversion method of angular points on a phase unwrapped graph based on a Gaussian elimination method is designed, a calculation strategy of laser radar point cloud-phase unwrapped graph-angular point detection-angular point three-dimensional coordinates is provided, after three-dimensional point cloud and two-dimensional images of a target are collected, data can be imported into a calibration program for automatic calculation, and the accuracy of calibration is improved. The method has the advantages of less human intervention, high automation degree, fast operation and high efficiency.

The invention discloses a micro-sensing trapezoidal method, device and equipment based on Aruco recognition and a medium, and relates to the field of computer vision. The method comprises the following steps: S1, projecting an Aruco grid and collecting an image, and extracting marked angular points to construct a position set; s2, calculating a trapezoid tension factor based on the trapezoid area and the opposite side distance, and quantifying local deformation; s3, fusing multi-labeled spatial parallax and tension information, constructing a continuous tension equilibrium domain, and generating a final mapping position through gradient correction basic inverse projection mapping and boundary disturbance compensation; and S4, outputting an inverse perspective matrix based on a fusion result, and estimating a microscopic pose offset parameter for fine adjustment of the projector. And through tension factor and spatial parallax modeling, adaptive correction of a distortion region is realized, the edge consistency and overall symmetry of the image are improved, and the stability and precision of the projected image are enhanced.

The invention discloses a camera calibration method for a low-precision plane target, and relates to the field of camera parameter calibration, and the method comprises the following steps: carrying out the multi-view shooting through a camera, and collecting data; checkerboard angular points are detected, and pixel coordinates of each angular point are determined; establishing a corresponding relation between the coordinates of the angular points and the coordinates of the actual world, adding a three-dimensional offset factor on the angular points of the target, and solving the parameters of the camera when the sum of the offset between the feature point pairs on the ideal target and the actual target is zero; global constraint is applied to the offset factor, a cost function is rewritten, and optimization is carried out through a nonlinear optimization algorithm to minimize a re-projection error. Under the condition that a low-precision target is used, high-precision camera calibration is realized by correcting feature points on the target. Simulation and real experiment results show that under the condition that a low-precision target is used, the method can still obtain a high-precision calibration result and measurement precision which are comparable with those of a traditional calibration method adopting a high-precision target.

The invention relates to a binocular deflection system calibration precision determination method, a medium and equipment, and the method comprises the steps: setting a plurality of coding patterns in the overlapping sight range of each collection device, and converting the coordinates of any angular point of the coding patterns into a same coordinate system, so as to calculate the distance error or / and the relative error between the coordinates in the same coordinate system, thereby achieving the calibration precision of a binocular deflection system. Determining a dual-target calibration error, acquiring a coordinate and a normal direction of a center point of the encoding pattern under any acquisition equipment coordinate system as a first three-dimensional parameter, then displaying a set stripe pattern to acquire a deformed stripe pattern reflected by the surface of the encoding pattern, and reconstructing the deformed stripe pattern to obtain a first three-dimensional parameter; the coordinates and the normal direction of the center point of the coding pattern under the coordinate system of the equipment are obtained and serve as second three-dimensional parameters, so that the deflection calibration error is determined, and the calibration precision of the binocular deflection system is determined according to the binocular calibration error and the deflection calibration error. The problem that the calibration result cannot be evaluated from the global perspective in the prior art is solved.

The invention discloses a bolt attitude estimation method based on deep learning and template matching. The bolt attitude estimation method comprises the following steps: S1, collecting an image of a cantilever bolt of a contact network and making a data set; s2, marking a bounding box of the bolt image and hexagonal shape area information of the bolt; s3, identifying six angular points of the bolt through a deep learning method; and S4, obtaining 6D pose information of the bolt. According to the method, the angular points and the 6D poses of the bolts can be accurately recognized by combining deep learning and template matching, the precision of bolt pose estimation is greatly improved, meanwhile, the positioning and pose conditions of a plurality of bolts in one area can be detected in real time, efficient detection and segmentation of the bolts are achieved, and the processing speed and efficiency are improved.

The invention discloses an automatic calibration method and device based on fine inspection camera edge finding and a storage medium, which are used for improving the calibration efficiency and avoiding artificial errors when a fine inspection camera aligns with a mark point. The method comprises the following steps: obtaining a fixed offset through advanced teaching; according to the fixed offset, the picture center of the fine detection camera falls into a calibration plate through a motion control assembly, and a calibration point is arranged in the calibration plate; the motion control assembly controls the fine detection camera to carry out edge finding motion so as to collect a characteristic pattern of the calibration plate, the linear edge and the angular point of the inner frame of the calibration plate are identified according to the characteristic pattern, and the fine detection camera is guided to carry out edge finding motion and angular point alignment; determining a first mechanical coordinate at the moment; according to the physical parameters of the calibration plate and the first mechanical coordinate, determining a second mechanical coordinate when the picture center of the fine detection camera is aligned with the center of a calibration point on the calibration plate; and calculating an affine transformation relation according to a pre-collected third mechanical coordinate and a pre-collected second mechanical coordinate, and completing calibration.

The invention provides a three-dimensional calibration method using a speckle image as a calibration object and combining weight parameters. The method comprises the following steps: acquiring a standard grid image and the speckle image through a camera; determining angular point positions in the standard grid image through an angular point detection algorithm; calibrating the stereoscopic vision system by using the obtained angular points to obtain an initial calibration result; calculating 3D coordinates of the camera calibration board in the world by using the disparity map; determining a subset of the reference image corresponding to the checkerboard angular points in the target image through a digital image cross-correlation algorithm; calculating a weight parameter of each calibration point based on the re-projection error; solving internal and external parameters of the camera by using the weight parameters, radial alignment constraint and a least square method; optimizing the main point of the camera by using the weight parameters; and if the re-projection error or the number of iterations does not meet the requirement, repeatedly calculating the weight parameter of the calibration point and the subsequent steps, otherwise, obtaining a camera calibration result, and obtaining the distortion curved surface of the camera lens.

The invention discloses a linear edge and corner detection method and device and a storage medium, relates to the technical field of machine vision, and is used for accurately acquiring linear edge and corner features. The method comprises the following steps: carrying out convolution on an input image to obtain a gradient magnitude diagram and a gradient direction diagram; acquiring a first image area in the gradient directional diagram; performing first hypothesis testing on the first image region to obtain a first region set and a second region set; dividing the second region set into a first sub-region set and a second sub-region set, and respectively carrying out second hypothesis testing on the first sub-region set and the second sub-region set; if both pass the second hypothesis test, calculating the absolute value of the mean value difference in the gradient direction of the two; determining to add the second region set into the first region set according to the absolute value; performing morphological expansion on the first region set, and calculating the rectangularity of each region; obtaining a linear edge feature region according to a rectangularity clamping control threshold value; and obtaining target linear edge features and angular point features in the linear edge feature region.

The invention relates to the technical field of deburring, in particular to deburring equipment of a tool jig and an automatic burr positioning method. The method comprises the following steps: acquiring a plurality of monitoring images of a to-be-deburred tool jig under each view angle; performing Gaussian pyramid scale transformation on each monitoring image, performing angular point detection, and dividing the angular points in each monitoring image into two categories according to the presentation condition of the angular points in a scale image; obtaining a texture expression value of each angular point in the scale image by combining the relative distance between each angular point and other angular points in the corresponding scale image of each monitoring image and the edge change condition; and obtaining a texture feature value of each angular point in the monitoring image by combining the texture representation value of each angular point in the corresponding scale image in all the monitoring images under the same visual angle, and further dividing and screening a burr region for each category. According to the invention, the positioning accuracy of the burr area and the burr removal effect are improved.

The invention relates to the technical field of object recognition based on image and point cloud fusion, and discloses a machine vision-based obstacle detection method and system in a container moving process, and the method comprises the steps: obtaining three-dimensional point cloud data through a laser radar installed on container carrying equipment, and obtaining a two-dimensional image through an industrial camera; performing terrain reference correction on the three-dimensional point cloud data, and establishing an inclined coordinate system matched with the ground gradient; performing perspective distortion correction on the two-dimensional image based on the inclined coordinate system to obtain a corrected two-dimensional image; extracting edge features, angular point features and semantic features from the corrected two-dimensional image; extracting geometric features and reflection intensity features of the three-dimensional point cloud data under the inclined coordinate system; performing cross-modal fusion on the edge features, the angular point features, the semantic features, the geometric features and the reflection intensity features to generate fusion features; and the obstacle type is identified based on the fusion feature, and the obstacle sensing reliability in the complex industrial environment is improved.

The invention relates to the technical field of image processing, in particular to a workpiece marking position detection method based on image processing, and the method comprises the steps: firstly collecting a workpiece surface image as a target image, extracting the edge and angular point of the target image, and classifying the edge and angular point into a straight edge and a turning edge; according to the minimum attribution distance of each straight edge and the arc factor of each turning edge, the representation importance degree of the angular points is calculated, then the distance sequence is formed by the distances of the angular points of the target and the template image, the representation validity degree of each angular point is calculated, and then the comprehensive representation value of each angular point is obtained; and finally, the marking position of the workpiece is determined according to a matching result. According to the method, various factors are comprehensively considered, interference of workpiece surface scratches, light reflection and other complex conditions on corner recognition is effectively avoided, corner matching accuracy is improved, and follow-up marking position detection is more accurate and reliable.

The invention relates to a histogram-based angular point screening method and device, and the method comprises the steps: responding to a condition that the number of angular points in a current frame of image is greater than a preset number threshold value, and carrying out the screening of the angular points based on the endpoints of a group, the frequency numbers of which are ranked in front of a set bit order in a histogram of a previous frame of image from large to small, and the histogram parameters of the previous frame of image, determining histogram parameters of the current frame image; determining endpoints and frequencies of each group in the histogram of the current frame image based on the score of each angular point in the current frame image and the histogram parameter of the current frame image; determining the number of target angular points based on the number of the angular points in the current frame image and a number threshold value; based on the number of the target angular points and the frequency of each group, determining the group where the critical angular points are located; predicting scores of the critical angular points based on the endpoints of the group where the critical angular points are located; and determining the target angular points based on the number of the target angular points, the number of the angular points in the first interval and the number of the angular points in the second interval. The screening efficiency can be improved.

An error correction method and device for a line structured light 3D camera. The method comprises: placing a 3D calibration plate at different positions in the field of view of the 3D camera, and allowing a relative motion to scan the 3D calibration plate to obtain multiple sets of point cloud data at different positions; processing the point cloud data and calculating corner coordinates of the 3D calibration plate corresponding to each set of point cloud data; constructing an error correction model by using an inclination angle, caused by an error, between a straight line along which the relative motion direction lies and a laser plane of the 3D camera as an error model correction parameter; calculating error correction model parameters according to a space vector constraint relationship between corner points; and applying the model to the point cloud data of a measured object to obtain distortion-free point cloud data.

The invention relates to the technical field of robots, and discloses an obstacle detection method, a robot, equipment and a storage medium. The method comprises the steps that a current frame image containing an obstacle and the moving speed of the robot are obtained through the robot, an object detection frame corresponding to the obstacle is generated in the current frame image, and the depth of the obstacle is calculated according to the position of the object detection frame in the current frame image. The method comprises the steps of obtaining an object detection frame of an obstacle, distributing a plurality of target angular points in the object detection frame, predicting a predicted angular point position in a next frame of image through a tracking algorithm according to the depth and the moving speed, generating a predicted object detection frame according to the predicted angular point position, and determining a predicted position of the obstacle according to the predicted object detection frame. According to the embodiment of the invention, the calculation burden of obstacle detection by the robot can be reduced, and the robot can update obstacle information at a higher frequency.

The invention relates to the field of computer vision, and provides a primitive rasterization method and device, electronic equipment and a storage medium. The method comprises the steps of determining an equation and a type of each edge of a primitive according to coordinates of vertexes of the primitive; searching a coefficient adjustment mode of the equation of each edge according to a preset rasterization mode and the type of each edge, and adjusting the coefficient of the equation of each edge according to the coefficient adjustment mode; determining a pixel coverage judgment condition of the primitive according to a preset rasterization mode; according to the coordinate of the angular point of each pixel and the new equation of each edge, whether each pixel meets the pixel coverage judgment condition of the primitive or not is judged, and when any pixel meets the pixel coverage judgment condition of the primitive, it is determined that the pixel is the pixel obtained through primitive rasterization. According to the method, the correctness of conservative rasterization function realization is ensured, and the cost of conservative rasterization is lower, so that conservative rasterization is simply and efficiently completed.

The invention relates to the technical field of SLAM, in particular to a dynamic environment robust positioning method based on improved ORB-SLAM2, and the method comprises the steps: obtaining RGB-D scene data containing an indoor moving object, and extracting ORB feature points described by FAST angular points and BRIEF descriptors; extracting feature points with unreliable heights according to geometric features of ORB feature points of adjacent frames; pixel analysis is carried out on the RGB-D scene data based on a Fast-SAM instance segmentation model, and a mask matrix containing environmental object space boundary information is generated; performing point-prompt extraction on the height unreliable feature points and the mask matrix to obtain a dynamic object mask; removing ORB feature points existing in the dynamic object mask to obtain a reliable feature point set; and calculating matching point pairs in the reliable feature point set to obtain an essential matrix and a corresponding rotation matrix and translation vector, and refining the pose based on a nonlinear optimization method.

The invention discloses a camera calibration method and device, equipment and a storage medium. The method comprises the following steps: acquiring a first image acquired by shooting a calibration plate by a camera; wherein the calibration plate comprises a plurality of angular points; performing inverse perspective transformation on the first image based on the internal reference and the initial external reference of the camera to obtain a second image; detecting angular points in the second image to obtain a first angular point detection result; based on the first corner detection result, the first image and the internal reference, solving to obtain a target external reference of the camera; and calculating an error parameter based on the target external parameter, the internal parameter and the first image, and when the error parameter satisfies a preset condition, determining that camera calibration is completed. According to the technical scheme of the invention, the inverse perspective transformation is carried out on the first image shot and collected by the calibration plate, so that the influence of partial camera distortion can be eliminated, the accuracy of angular point detection is improved, and the success rate and accuracy of camera calibration can be improved.

The embodiment of the invention provides a depth estimation and posture recognition method and device, electronic equipment and a storage medium, and relates to the technical field of computer vision. According to angular point coordinates of a two-dimensional detection frame, camera parameters of a monocular camera, a preset initial depth, size information and a yaw angle, the posture of the monocular camera is recognized; obtaining angular point coordinates of a target in the monocular image to be detected in a three-dimensional detection frame under a camera coordinate system; projecting the three-dimensional detection frame to an image plane where the monocular image to be detected is located based on the camera parameters to obtain angular point coordinates of a projection frame; determining a projection error between the projection frame and the two-dimensional detection frame according to the angular point coordinates of the projection frame and the angular point coordinates of the two-dimensional detection frame, and adjusting the initial depth to obtain a target depth; according to the embodiment of the invention, three-dimensional reconstruction and two-dimensional projection are carried out based on the camera parameters, the influence of the camera parameters on three-dimensional reconstruction is considered, and the accuracy of the estimated target depth is further improved.

The invention provides an automatic calibration method for a numerical control rotary table, which belongs to the technical field of machine vision, machining and automatic control, and comprises the following steps: controlling the rotary table to rotate at the same angle and direction for multiple times, and acquiring a calibration plate image by using a camera after each rotation to obtain an image set; calibrating the internal reference of the camera according to the calibration plate, calculating the angular point position of the checkerboard in each calibration plate image, and calculating the external reference of the camera according to each angular point position; converting each angular point in each calibration plate image into a space point under a world coordinate system, acquiring a circle center fitting initial value by calculating a vertical bisector by using the space point, and performing circle center fitting to obtain a plurality of circle center points; the spatial position of the rotating shaft is determined through straight line fitting, and a transformation matrix for achieving rotation and translation is obtained. The method can improve the calibration efficiency and precision, reduces the manual intervention, and is suitable for various precise machining and automatic control scenes.

Systems and methods are described relating to pose and shape estimation of objects. In some examples, a camera generates images depicting an object from different viewpoints. Within the images, a system identifies corners of the object, and the system uses these corners to generate lines that are projected from different viewpoints through the corners. Points may be identified by intersecting lines such that the points may be used to generate estimations of the object. The estimation with the highest score may be used to place the object in a location different from where the object was located.

The invention discloses an article grabbing and positioning method, system and device and a medium, and relates to the technical field of visual recognition. The method comprises the steps that a two-dimensional image, collected through a monocular camera, of an object to be grabbed is acquired; the types and the number of the to-be-grabbed articles in the two-dimensional image are recognized, and standard physical sizes corresponding to the types are determined; if the object is a single object, the object serves as a target to-be-grabbed object, the top face corner pixel coordinates of the object are detected, and the three-dimensional pose is solved through the PnP algorithm by combining the physical size and the internal reference of the monocular camera; if the number of the objects is multiple, all candidate corner points are detected, quadrilateral corner point sets of all the objects are screened out through clustering and geometric constraint analysis, then candidate three-dimensional poses of all candidate objects to be grabbed are calculated through a PnP algorithm, and then a target object to be grabbed is selected according to the candidate three-dimensional poses; and finally outputting the three-dimensional pose information of the target to-be-grabbed object. According to the invention, accurate positioning of a single article and three-dimensional pose recognition in a multi-article stacking scene are realized only by means of the monocular camera.

The invention discloses a method and device for measuring the size of irregular objects, and relates to the field of optical measurement. The method is used to solve the problem that the method for measuring the size of irregular objects is complicated and has low precision when the lens with nonlinear distortion is too large. The method comprises: extracting the edge of the object from the image of the irregular object obtained by a large distortion camera to obtain the outermost edge coordinates of the irregular object; adding the four outermost edge coordinates corresponding to the longitudinal coordinates and the horizontal coordinates respectively included in the irregular object to the outermost edge point set; obtaining the four corner point coordinates of each outermost edge coordinate on the chessboard calibration plate according to the outermost edge coordinates and the chessboard corner points, and the four corner point coordinates belong to the image edge coordinate set; connecting the four corner point coordinates corresponding to each outermost edge coordinate diagonally to obtain the intersection point, traversing to determine the distance between each intersection point and the corresponding outermost edge coordinate, until the distance between each intersection point and the corresponding outermost edge coordinate is less than a third set value; obtaining the size of the irregular object according to the number of complete chessboards between the outermost edge points in the horizontal direction, the number of complete chessboards between the outermost edge points in the vertical direction, and the proportion of the area where the outermost edge point is located to the chessboard.

The present invention discloses a method and device for extrinsic parameter calibration based on a calibration template, comprising: collecting an RGB image and a depth map of the calibration template; aligning the RGB image and the depth map, and extracting inner corners of the RGB image based on a corner extraction algorithm; obtaining depth information corresponding to each inner corner point extracted from the RGB image on the depth map, and calculating the coordinates of each inner corner point in the camera coordinate system based on each inner corner point and its corresponding depth information; obtaining the coordinates of each inner corner point in the forklift coordinate system based on the relationship that the projection of a preset inner corner line of the calibration template coincides with the origin of the forklift coordinate system; and calculating the calibration extrinsic parameters of the camera based on the coordinates of each inner corner point in the camera coordinate system and the coordinates in the forklift coordinate system. The present invention solves the technical problems in the prior art of the complex calibration process of the calibration template and the low accuracy and robustness of the extrinsic parameter calibration.

The invention provides a vSLAM and display screen coordinate system alignment method and system based on a two-dimensional code, and the method comprises the steps: controlling a display screen to synchronously display a two-dimensional code with a known physical size and a known position parameter under a display screen coordinate system, collecting an image frame containing the two-dimensional code, and obtaining a camera pose synchronized with the image frame in time; identifying the two-dimensional code, extracting the coordinate of the angular point image, and calculating the spatial pose of the two-dimensional code in a vSLAM world coordinate system in combination with the physical size of the two-dimensional code participated in the camera; establishing a coplanar consistency constraint based on a plurality of two-dimensional code space poses, jointly estimating a display screen world pose of the display screen in a vSLAM world coordinate system, and calculating a deformation parameter used for representing a non-ideal plane state of the display screen; and further constructing a coordinate system mapping relation between a display screen coordinate system and a vSLAM world coordinate system, realizing accurate mapping from a screen coordinate to a world coordinate, and performing verification and exception processing on an alignment result.

The application relates to an object positioning method, device, system, readable storage medium and program product. The method comprises the following steps: acquiring an object angle range and an object edge direction vector of a target object; extracting at least one candidate corner point and at least one candidate edge line segment combination from a shooting image corresponding to the target object; extracting at least one candidate geometric feature combination from all candidate corner points and all candidate edge line segment combinations according to the object angle range and the object edge direction vector; screening a target object shape from a candidate object shape corresponding to each candidate geometric feature combination, and positioning the target object according to the target object shape. The method can accurately position the object.

The invention relates to the technical field of image processing, in particular to a cross-modal alignment method for rock slice images, which comprises the following steps of: firstly, preprocessing an aligned cross-modal rock slice image data set, extracting an effective core contour common to two modalities by using a Mean Shift-Canny rock core contour detection algorithm based on the acquired data set, and extracting an effective core contour common to the two modalities; and forming a feature descriptor based on the extracted core contour, matching feature points, and completing rock slice image feature alignment and extraction of a common effective core contour of two modalities. On the basis of the extracted core contour, angular points with obvious angles are taken as feature points, after the feature points of different modal images are extracted respectively, the main direction and the auxiliary direction of the feature points are extracted, so that angular point coordinates and response value intensity are combined to jointly serve as descriptors, and a judgment basis of a subsequent matching algorithm is formed; and finally, matching the feature points to realize rock slice feature alignment.

The present invention discloses a method for extracting key points from a lateral cephalogram, including: S1, collecting a plurality of lateral cephalograms; S2, performing conditional screening on the collected plurality of lateral cephalograms; S3, preprocessing the plurality of lateral cephalograms to obtain a grayscale image of the lateral cephalogram; S4, calculating the probability density of the image using a Gaussian probability density function to obtain the foreground region and the background region in the image; S5, using a corner feature method to extract the dynamic points among the key points of the head in the foreground region; S6, taking the FH plane on the head in the foreground region as the abscissa and the plane perpendicular to the FH plane as the ordinate to construct a two-dimensional rectangular coordinate system; S7, extracting the coordinate information of the dynamic points among the multiple key points of the head obtained in step S5. Compared with the traditional manual positioning, the present invention is faster and more efficient. Compared with the traditional software-assisted positioning, the present invention has high accuracy and is more convenient, fast and easy to operate.

The application provides a kind of slope surface displacement monitoring method, device, medium and equipment, comprising: collecting multiple image data of target slope;By analyzing multiple image data, determine multiple target targets and get the area code and pixel coordinates corresponding to the target target;Get the spatial coordinates of the four corner points of each target target in multiple target targets and the centroid coordinates of each target target;Respectively for each target target in multiple target targets, according to the spatial coordinates and centroid coordinates of the target target, the diagonal vector of each target target is calculated, the direction vector of the plane of each target target is obtained, and the direction vector has 3 direction degrees of freedom;The target slope is monitored by pixel coordinates, spatial coordinates, centroid coordinates and direction vector, and the monitoring result is obtained;Compared with the prior art, 3 displacement degrees of freedom + 3 direction degrees of freedom monitoring of the target slope is realized, and the precision of slope monitoring is improved.