50 results about "Geometric fitting" patented technology

The invention discloses a welding seam track extraction method and system based on RANSAC parameter fitting, and the method comprises the steps: S1, obtaining three-dimensional point cloud data, and obtaining the normal vector and local curvature of each point cloud data point; selecting an initial seed point from the three-dimensional point cloud data according to the local curvature of each point cloud data point; s2, establishing a feature similarity evaluation system, and adding the initial seed points and the corresponding similar adjacent points into the same plane; according to different planes where the point cloud data points are located, plane area coarse segmentation is carried out; and S3, a plurality of geometric fitting models are constructed for the welding track based on the RANSAC algorithm, the geometric fitting model with the highest matching degree is selected from the geometric fitting models, and the welding track in the welding area is extracted. According to the technical scheme, the welding seam track can be rapidly and effectively extracted, the universality of welding seam track recognition is improved, and the extraction error of the welding seam track is remarkably reduced.

The invention discloses an instance segmentation and structured topology reconstruction method for a complex geometric shape building, and relates to the technical field of three-dimensional model generation, and the method comprises the following steps: S1, building point cloud instance segmentation: employing a SoftGroup network to carry out the semantic analysis of an unmanned plane LiDAR point cloud, outputting an independent part instance containing the semantic tag of each building assembly, and carrying out the segmentation of a building point cloud instance; fine segmentation of each building component is realized through a point cloud instance mask; and S2, geometric primitive extraction: grouping instance point clouds according to semantic tags, and executing differential geometric fitting. Through semantic-geometric coupling modeling, unified expression of a special-shaped structure and a regular component is realized, the bottleneck of segmentation and parameterization of a traditional method in a complex building scene is solved, and the method is more efficient and efficient. And the boundary expression of the conical top or the hemispherical top and the supporting structure is simplified by adopting an elliptic curve section, the calculation complexity is reduced, and meanwhile, the integrity of the building framework is guaranteed.

The invention discloses an artificial intelligence-assisted standard artificial temporomandibular joint automatic placement method. The method comprises the following steps: training a three-dimensional reconstruction model used for extracting a mandible and zygomatic bone zygomatic arch joint prosthesis placement area; based on an improved 3D U-Net neural network, carrying out segmentation extraction on the interested areas of the glenoid fossa and the mandibular branch, and obtaining a space geometric structure for prosthesis installation; the method comprises the following steps: extracting outer bone surface point cloud data of a zygomatic arch and a mandibular ramus on the basis of a space geometric structure of prosthesis installation, identifying anatomical key points, and generating a standard prosthesis fitting surface through geometric fitting and constrained optimization; determining the relative position relation between the maxillary joint fossa prosthesis and the mandibular branch prosthesis, and determining the installation direction, the hole position definition and the assembly reference point; and determining a contact area, a posture direction and a placement position of the prosthesis, and completing three-dimensional positioning and posture matching of the prosthesis in the patient model as a surgical planning scheme.

The invention discloses a dual-light registration and infrared camera calibration method, which comprises the following steps of: synchronously acquiring a visible light image and an infrared image, and acquiring multiple groups of dual-light image pairs under different distances within a preset object distance range; cross-modal feature key points are extracted, a matching corresponding relation is established, registration parameters under object distances are generated, and a dynamic compensation database is constructed; lead arrays are embedded in a plurality of horizontal edges and a plurality of vertical edges of a traditional checkerboard calibration plate, and leads are driven to generate uniform heat radiation; checkerboard corner coordinates are accurately extracted based on developed heating side lines in the infrared image, a real-time target detection model software deep learning model is utilized to construct a multi-scale feature fusion network, and combined geometric fitting is carried out on checkerboards in the bimodal image; and carrying out adaptive region segmentation on the fitted checkerboard, and reconstructing a black and white alternate state of the visible light checkerboard. The technical problem of registration failure in a complex environment can be solved, and the stability and repeatability of calibration are improved.

The invention relates to the field of assembly automation and flexible manufacturing, and discloses an assembly method for a prefabricated firewall of a transformer substation, and the method comprises the following steps: setting a plurality of space positioning reference points at the edge of a wallboard; acquiring three-dimensional coordinate data to form a first point set and a second point set; calculating an initial rigid body transformation parameter between the wallboards according to the first point set and the second point set; establishing an energy functional for describing a total error; carrying out minimization solution on the energy functional by adopting a variational method to obtain an optimal disturbance vector field; according to the optimal disturbance vector field, a plurality of fine adjustment actuators of the wallboard are controlled, posture fine adjustment is conducted, and physical assembly connection of the wallboard and the target wallboard is completed. According to the invention, accurate attitude estimation and error correction in the wallboard assembly process are realized; the robustness and geometric fitting capability of the assembly process are improved; the assembly precision and efficiency are remarkably improved, and the problem that traditional redundancy control distribution is unstable is solved.

The invention relates to the technical field of robot detection, and discloses a Delta mechanical arm precision detection method, system and device and a storage medium, and the Delta mechanical arm precision detection method comprises the steps that multiple actual position coordinates of the tail end of a mechanical arm in the movement process along a preset track are obtained according to a selected detection mode; geometric fitting is carried out based on the actual position coordinates, and corresponding circle center coordinates are determined; the tail end is controlled to move to the circle center coordinates, and actual position coordinates at the moment are obtained; and error information representing an absolute precision error is generated based on a difference value between the position and the circle center coordinate. According to the method, automatic detection of the absolute positioning precision of the manipulator is achieved, and error quantization can be completed without depending on external measuring equipment; the generated error vector accurately reflects the direction and size of system deviation, compensation and correction are facilitated, and the method has the advantages of being high in detection efficiency, reliable in result and high in applicability.

The embodiment of the invention provides a method and device for recognizing a rock mass structural plane. The method comprises the following steps: in response to an obtained photo control point geodetic coordinate and an aerial survey photo about a target area, generating a three-dimensional entity model about the target area through an image data processing rule based on the photo control point geodetic coordinate and the aerial survey photo; in response to a two-dimensional image map extracted from the aerial survey photo, inputting the two-dimensional image map into a pre-trained neural network, and identifying the two-dimensional image map by the pre-trained neural network to obtain a corresponding structural surface two-dimensional contour; associating the two-dimensional contour of the structural plane to a three-dimensional entity model through a space mapping algorithm, and generating a three-dimensional structural plane edge contour; and carrying out geometric fitting on the edge contour of the three-dimensional structural surface to obtain the structural surface size and occurrence parameters of the target area. By utilizing the method disclosed by the embodiment of the invention, the spatial orientation and size parameters of the structural plane can be accurately solved.

The invention discloses a non-contact tire deformation intelligent identification method based on deep learning. The method comprises the following steps: firstly, performing distortion correction on an image acquired by a high-speed optical camera through camera calibration parameters; and then, a target detection and semantic segmentation algorithm is constructed based on a deep learning technology, and tire side images in different environments are trained to obtain an efficient, stable and accurate tire intelligent identification and segmentation model. According to the model, accurate segmentation of a tire to be detected can be realized, geometric fitting is carried out on a mask contour obtained through segmentation through OpenCV, and finally, deformation parameters of the tire are calculated based on a fitted circle. According to the method, pixel-level, stable and high-precision segmentation of the tire deformation area can be realized, the recognition efficiency and accuracy are remarkably improved, and the problems that an existing machine vision recognition method is low in intelligent level and poor in recognition performance in a complex environment are solved.

This invention discloses a project status management method and system for intelligent point cloud modeling, relating to the field of project status management technology. The method includes registering point cloud block files, label files, and 3D model files generated during point cloud segmentation, point cloud partitioning, geometric fitting, and pipeline parameter adjustment as subsequent versions and recording operation impact information; constructing a 3D spatial index structure to associate and store point cloud block files, pipeline objects, and corresponding operation impact information; and when a rollback request is received, determining the set of objects that intersect with the target 3D spatial range and whose semantic category belongs to the target semantic category set, and determining the set of affected objects whose version number is later than the target version identifier. This invention can, upon receiving a rollback request containing the target spatial range and the target semantic category set, only locate the affected local object set and perform a local rollback, which helps to preserve the modeling results of unaffected areas and reduces redundant processing and manual verification work.

The invention discloses a phase consistency enhancement and adaptive geometric fitting corner detection method. The method comprises the following steps: firstly, carrying out adaptive histogram equalization CLAHE contrast enhancement on a cross target area image detected by YOLO; calculating a phase consistency response of the enhanced image to obtain a corner candidate region insensitive to brightness and contrast change; and calculating Harris corner response on the enhanced image, combining the Harris response with the phase consistency response score to construct a comprehensive response diagram, and screening out a corner candidate set from the comprehensive response diagram by adopting adaptive multiple thresholds. According to the method, through combination of YOLO target detection and CLAHE region enhancement, illumination non-uniformity interference such as overexposure and shadow can be effectively suppressed in a target region, a comprehensive response diagram is constructed by adopting phase consistency and Harris response in a combined manner, corner candidates are screened by using adaptive multiple thresholds, and the luminosity invariant feature and gradient significance of corners are considered, so that the accuracy and the robustness of the target region are improved. And the angular point extraction accuracy is improved.

The invention relates to a method and device for determining a welding track, electronic equipment and a medium, and belongs to the technical field of welding.The method comprises the steps that a left view of a target binocular image of a target steel structure part is input into a well-trained deep learning model to obtain a welding seam area mask image, and the welding seam area mask image is obtained; the target binocular image is shot by a binocular camera installed on the target robot; the left view and the right view of the target binocular image are matched based on a Foundation Stereo model, and a point cloud of the target steel structure part is obtained; segmenting the point cloud of the target steel structure part based on the welding seam area mask graph to obtain a welding seam area point cloud; performing geometric fitting on the point cloud of the weld zone to obtain a weld welding track; and spatial pose information of a welding gun of the target robot is obtained based on the weld joint welding track, and the welding track of the target robot is determined based on the spatial position information. The welding precision of the steel structure part is improved.

The invention relates to the technical field of additive manufacturing quality control and process optimization, and particularly provides a method and system for predicting the quality of a molten forming part of a laser powder bed, and the method comprises the steps: obtaining the three-dimensional point cloud data of the molten forming part of the laser powder bed; dividing the aligned three-dimensional point cloud data into a plurality of cross section point sets along the central axis direction; performing independent local geometric fitting on each cross section point set to obtain local fitting parameters of each cross section; based on the three-dimensional point cloud data and the local fitting parameters of all the cross sections, the three-dimensional original surface of the formed part is unfolded into a two-dimensional surface deviation topological graph; and calculating the surface roughness based on the two-dimensional surface deviation topological graph, and carrying out statistical evaluation on the process-induced macroscopic geometric deviation of the formed part. According to the method, through a closed loop from process prediction to result evaluation, powerful data support is provided for accurate decoupling and integrated representation of two types of key quality characteristics of the LPBF forming part, and a new way is opened up for digital pre-optimization of the process.

The embodiment of the invention provides an adaptive surface grid generation method and system based on a grid method, and the method comprises the steps: carrying out the traversing of a volume grid unit, carrying out the recursive subdivision of the volume grid unit meeting a preset condition, and obtaining a subdivided volume grid unit; traversing surface units of the subdivided volume grid units, and marking the volume grid units on two opposite sides of the surface units belonging to the interior of the geometric model as boundary surface units; constructing the grid nodes on the boundary surface units into a to-be-projected point set; calculating and recording projection coordinates of the grid nodes in the to-be-projected point set to the nearest or associated geometric model surface; assembling the boundary surface units into a discrete surface grid, and performing preliminary fitting on the discrete surface grid based on the projection coordinates to obtain an initial surface grid; arranging the surfaces of the geometric model in a descending order according to the area to determine the processing sequence of the geometric surfaces; and according to the processing sequence, performing grid fitting operation on each geometric surface in sequence to obtain a fitting surface grid. By executing recursive subdivision, automatic adaptation of the grid size is achieved, tiny features can be automatically captured and encrypted, and the surface grid can be generated for any complex three-dimensional model in a robust mode. A boundary-driven geometric fitting algorithm is adopted to guide a generated surface grid to be precisely attached to a target geometric model, and it is guaranteed that key curved surfaces and sharp features are kept in a high-fidelity mode after discretization.

The invention relates to a graph-free crossroad smooth control method and system based on a 4D millimeter wave radar. On the premise of not depending on a high-precision map and a V2X facility, through self-adaptive ground segmentation of longitudinal segmentation and gradient continuity constraint and in combination with boundary point judgment and geometric fitting of multi-dimensional feature fusion, an accurate static boundary model of a travelable area of the crossroad is reconstructed in real time; a static boundary is used as a hard constraint, a dynamic obstacle prediction trajectory is used as a safety constraint, a linear time-varying model prediction control problem with motion smoothness as a primary optimization target is constructed, and the linear time-varying model prediction control problem is converted into quadratic programming for real-time solution; besides, a closed-loop adaptive mechanism based on a smoothness index is also introduced, perception-control collaborative optimization is realized through multi-hypothesis generation and micro-programming forward simulation, and a geometric memory bank is configured for bottom control and experience reuse, so that all-weather, low-cost and high-smoothness graph-free crossroad automatic driving traffic capacity is effectively realized.

The present application relates to the field of washing machine glass observation window profile size detection, disclose a kind of auxiliary washing machine glass observation window's profile size high-precision detection method and system, method includes: obtaining step is using equal spatial interval sampling, obtain the point cloud data set of spatial uniform distribution;In preprocessing step, local surface feature analysis is carried out to point cloud data, edge candidate point is marked and spatial connectivity analysis is carried out, and after isolated point is removed, edge point cloud data is obtained;In fitting step, standard geometric model is iteratively adjusted based on geometric constraint condition, until overall cumulative deviation variation is less than convergence threshold;In size generation step, profile size parameter is extracted.The system includes signal acquisition module, data preprocessing module, geometric fitting module and parameter extraction module.The present application eliminates the influence of motion speed fluctuation on sampling uniformity, effectively eliminates surface defect interference, and realizes high-precision, high-efficiency automatic detection of washing machine glass observation window profile size.

The application relates to the technical field of additive manufacturing quality control and process optimization, and specifically provides a laser powder bed fusion forming part quality prediction method and system, which comprises the following steps: acquiring three-dimensional point cloud data of a forming part fused by a laser powder bed; dividing the aligned three-dimensional point cloud data into a plurality of cross-section point sets along a central axis direction; independently performing local geometric fitting on each cross-section point set to obtain local fitting parameters of each cross section; based on the three-dimensional point cloud data and the local fitting parameters of each cross section, expanding a three-dimensional original surface of the forming part into a two-dimensional surface deviation topology graph; and based on the two-dimensional surface deviation topology graph, calculating surface roughness and statistically evaluating process-induced macro-geometric deviation of the forming part. The closed loop from process prediction to result evaluation provides strong data support for accurate decoupling and integrated characterization of two key quality characteristics of the LPBF forming part, and opens up a new way for digital preposition optimization of the process.

The application discloses a kind of instance segmentation and structured topological reconstruction methods of complex geometric building, it is related to three-dimensional model generation technical field, comprising the following steps: S1, building point cloud instance segmentation: using SoftGroup network to the semantic analysis of unmanned aerial vehicle LiDAR point cloud, output contains the independent component instance of each building component semantic label, the fine segmentation of each building component is realized by point cloud instance mask;S2, geometric primitive extraction: according to semantic label, group instance point cloud is carried out, and differential geometric fitting is executed, the application is realized by semantic-geometric coupling modeling The unified expression of irregular structure and regular component, solve the segmentation and parameterization bottleneck in complex building scene in traditional method, also adopt the boundary expression of elliptic curve segment simplification conical top or hemispherical top and support structure, reduce the complexity of calculation, while guarantee the integrity of building skeleton.

The invention belongs to the technical field of laser surveying and mapping, and particularly relates to a basic surveying and mapping geographic entity rapid extraction method and system based on laser three-dimensional scanning, and the method comprises the steps: S100, carrying out the density-adaptive voxelization downsampling of an original laser point cloud, and calculating geometric, reflection and context features from the original point cloud in a multi-scale neighborhood of each representative point; s200, constructing a hypergraph, generating initial hyperpoints through spectral clustering, iteratively performing semantic classification and geometric consistency check, and dynamically merging or splitting the hyperpoints to obtain a refined hyperpoint set with stable semantic tags; s300, subgraphs are constructed, edge weights reflect geometric and boundary compatibility of combination between the super points, and similar super points are clustered into physical entity instances by calculating a maximum spanning tree and cutting at weak joints; and S400, carrying out geometric fitting and regularization on the point cloud of each entity instance, generating a parameterized model, calculating a spatial relationship between entities, and constructing a global semantic topological graph containing complete attributes and relationships.

The invention relates to the field of computer simulation and aided design, in particular to a non-ideal plane assembly prediction method considering local deformation. The method comprises the steps that a skin surface model generation method is adopted, data collection and geometric fitting are carried out on the surface of a part, and a surface model containing errors is created; using a finite element analysis method to evaluate local deformation of the part under stress; and establishing a Jacobian matrix in the assembly chain, and describing a relation between displacement and rotation of the part in the assembly process. According to the method, on the basis of the skin surface model, the Jacobian matrix is constructed on the local section of the part and brought back to the model based on finite element analysis, the defect that the influence of local deformation on the assembly precision is not fully considered in traditional plane assembly deviation analysis is overcome, the assembly deviation caused by geometric errors and local deformation is effectively reduced, and the assembly precision is improved. The assembly precision is improved.

The invention discloses a weld deformation tracking compensation method based on a convolutional neural network, and aims to solve the problem that continuous and stable tracking of a weld is difficult under complex working conditions such as arc light, smoke dust, splashing and welding gun shielding. Outputting a shielding probability graph by using the shielding sensing branch, and generating modulation parameters to perform channel and space modulation on the features; a weld joint probability graph is output through the weld joint position estimation branch, weld joint candidate points are determined, and meanwhile, a position variance graph is output through the uncertainty estimation branch to represent the positioning uncertainty of the candidate points; and determining a fitting weight by combining the shielding probability and the position variance, performing weighted geometric fitting on the candidate points to obtain a weld center line model, performing continuous gating on the compensation gain according to the fitting residual error and the tracking credibility, and smoothly outputting a compensation instruction in cooperation with amplitude limiting and time. Robust tracking and stable compensation control over the center line of the welding seam under shielding interference are achieved, and the tracking reliability and the welding track compensation stability are improved.

The invention discloses an engineering field three-dimensional reconstruction physical scale estimation method with reference to a standard component, and belongs to the technical field of building engineering digitization, three-dimensional reconstruction and intelligent construction, and the method comprises the steps: constructing a three-dimensional reconstruction physical scale estimation platform; a multi-view image sequence containing a standard component is collected, a scale-free three-dimensional point cloud and a camera pose are obtained through structure self-motion reconstruction SfM and multi-view stereo matching MVS reconstruction, a point cloud subset of the standard component is detected and positioned, a reconstruction scale size is obtained through geometric fitting, a scale factor is calculated in combination with a real physical size, and a three-dimensional point cloud is obtained. And carrying out global scaling on the scale-free model to finally obtain a real physical scale three-dimensional model. The method is suitable for various image acquisition devices and construction scenes, the scale recovery precision reaches the millimeter-to-centimeter level, the robustness is high, and the method can be widely applied to engineering measurement, BIM model alignment, digital twinning construction and other scenes and has remarkable engineering application value.

The invention discloses a vectorized high-precision map construction method and device, electronic equipment and a medium, and the method comprises the steps: carrying out the parameterization representation of map elements through employing a segmented Bezier curve, and guiding the image feature sampling through adaptive offset sampling according to a curve structure, so that a model can pay more attention to the feature information of an area where the map elements are located, and the construction efficiency is improved. Therefore, irregular and flexible linear features of map elements are accurately captured, high geometric fitting degree is achieved, detection precision is also improved, a traditional dense BEV grid construction step is completely avoided through a pure sparse normal form, and the calculated amount is remarkably reduced.

The invention discloses a project state management method and system for point cloud intelligent modeling, and relates to the technical field of project state management, and the method comprises the steps: registering a point cloud block file, a label file and a three-dimensional model file which are generated in point cloud segmentation, point cloud segmentation, geometric fitting and pipe network parameter adjustment as subsequent versions, and recording operation influence information; constructing a three-dimensional space index structure, and associatively storing the point cloud block file, the pipe network object and the corresponding operation influence information; and when a rollback request is received, determining an object set which intersects with the target three-dimensional space range and of which the semantic category belongs to the target semantic category set, and determining a version set which affects the object set and of which the version number is later than the target version identifier. According to the method, when the rollback request containing the target space range and the target semantic category set is received, only the affected local object set is positioned, and local rollback is executed, so that a modeling result of an unaffected area is reserved, and repeated processing and manual checking work are reduced.

The invention relates to the field of washing machine glass observation window contour dimension detection, and discloses an auxiliary washing machine glass observation window contour dimension high-precision detection method and system, and the method comprises the steps: employing equal space interval sampling in an obtaining step, and obtaining a point cloud data set with uniform space distribution; in the preprocessing step, local curved surface feature analysis is carried out on point cloud data, edge candidate points are marked, spatial connectivity analysis is carried out, and edge point cloud data are obtained after isolated points are removed; in the fitting step, the standard geometric model is subjected to iterative adjustment based on geometric constraint conditions until the overall accumulated deviation variation is smaller than a convergence threshold value; in the dimension generation step, contour dimension parameters are extracted. The system comprises a signal acquisition module, a data preprocessing module, a geometric fitting module and a parameter extraction module. The influence of motion speed fluctuation on sampling uniformity is eliminated, surface defect interference is effectively eliminated, and high-precision and high-efficiency automatic detection of the overall dimension of the glass observation window of the washing machine is realized.

The invention relates to a finished stay cable length determination method and device, and the method comprises the steps: carrying out the geometric fitting of each measurement space coordinate set of each cable anchor lug plate on a main beam and a main tower of a bridge, and obtaining an initial center coordinate; therefore, the space coordinates of the cable anchor lug plate are measured, high-altitude operation is not needed, and the operation difficulty is reduced; performing clustering analysis on all the initial circle center coordinates according to the measurement radius of each cable anchor lug plate to obtain an optimal circle center coordinate and an optimal radius; when the optimal radius meets the preset condition, unstressed length calculation and temperature correction are conducted on the linear distance between the optimal circle center coordinate of the main beam and the optimal circle center coordinate of the main tower, and the cable length of the finished stay cable is obtained; according to the method, clustering analysis is carried out on data measured for multiple times, an optimization result is verified in combination with the optimal radius, the detection precision is improved, unstressed length calculation and temperature correction are carried out through the optimal result, possible errors are removed, and the accuracy of the cable length of the finished stay cable is improved.

The invention discloses a binocular vision detection and spatial positioning method for a sparse texture target embedded with geometric constraints. The binocular vision detection and spatial positioning method is characterized by comprising a binocular vision detection method embedded with the geometric constraints and a spatial positioning method based on point cloud of a detection area and geometric fitting. According to the method, the problem that a sparse texture target is difficult to identify and locate in an industrial scene with a complex background and multiple interference factors in a visual locating process is solved, and the method comprises the following steps of: detecting the target through a visual detection network model embedded with geometric constraints to determine a target area; and a target pose is solved through a target space positioning method based on ROI parallax point cloud construction and geometric feature fitting.

The invention discloses an unsupervised three-dimensional acoustic logging reflector imaging interpretation method based on evolutionary computation. The unsupervised three-dimensional acoustic logging reflector imaging interpretation method sequentially comprises the steps that underground acoustic wave full wave train data collected by a multi-pole sub-array acoustic logging instrument is obtained; wave field separation and reflected wave extraction; multidirectional migration imaging is carried out; adaptive image enhancement driven by evolutionary computation; constructing a three-dimensional point cloud; adaptive spatial clustering driven by evolutionary computation and geometric fitting and interpretation of a three-dimensional structure are carried out; according to the scheme, the processing parameters are adaptively optimized through an evolutionary computation algorithm, so that unsupervised three-dimensional imaging of the geological reflector around the well, adaptive optimization of the automatic segmentation and processing parameters and true three-dimensional quantitative interpretation of the geological structure are realized; the technical problems that parameter adjustment depends on artificial experience and cannot adapt to a dynamic noise environment, geometric distortion exists in two-dimensional interpretation, and supervised learning is difficult to use due to lack of labeled data in an existing sound wave remote detection technology are solved.

This invention discloses a corner detection method based on phase consistency enhancement and adaptive geometric fitting, comprising the following steps: First, adaptive histogram equalization and CLAHE contrast enhancement are performed on the cross-shaped target region image detected by YOLO; the phase consistency response of the enhanced image is calculated to obtain corner candidate regions that are insensitive to changes in brightness and contrast; the Harris corner response is calculated on the enhanced image, and the Harris response and phase consistency response scores are jointly used to construct a comprehensive response map, and an adaptive multi-threshold is used to filter the corner candidate set from the comprehensive response map. This invention, by combining YOLO target detection and CLAHE region enhancement, can effectively suppress illumination inhomogeneity interference such as overexposure and shadows within the target region. Furthermore, by using phase consistency and Harris response to jointly construct a comprehensive response map and using adaptive multi-threshold to filter corner candidates, it takes into account both the photometric invariance and gradient significance of corners, thus improving the accuracy of corner extraction.

The invention relates to the technical field of image processing, and provides a mining method tunnel building information model construction method and system, equipment and a medium, and the method comprises the following steps: S1, obtaining tunnel three-dimensional point cloud data; s2, fitting the three-dimensional point cloud data into a tunnel arch model and a pavement model; s3, reconstructing a standard cylinder coordinate system; s4, fitting left and right maintenance path models; s5, calculating a key structure line and an arch expansion line; and S6, obtaining mining method tunnel building information model parameters according to the key structure line and the arch expansion line. According to the scheme, a complete technical process from point cloud geometric fitting to parametric modeling is constructed, high-precision, light-weight and automatic mining method tunnel BIM model construction is achieved, and therefore the core requirements of digital maintenance for the real-time performance, analyzability and precision of the model are met.