879 results about "Characteristic point" patented technology

The invention relates to the technical field of digital twinning control, and discloses a composite structure prefabricated segment digital twinning-adaptive assembly correction method and system, and the method comprises the steps: reading BIM geometric model data, and constructing an assembly reference coordinate system and a digital twinning geometry of a prefabricated segment; composite data are collected in real time, and end tooth groove boundary feature point cloud is extracted; matching the actually measured point cloud with the digital twinborn geometry through a dynamic point cloud registration technology, calculating a six-degree-of-freedom pose error vector, and generating a predicted total pose error vector in combination with a pre-constructed pose drift prediction model; based on the error vector, a mechanical fine adjustment jack is driven to execute position and posture adjustment until the position and posture are converged, and then tooth groove precise meshing and mechanical locking are completed; according to the method, through the synergistic effect of the dynamic mapping of the digital twinborn model and the self-adaptive correction algorithm, high-precision dynamic correction of the multi-combination structure segment assembly process is achieved on the premise that an original mechanical connection structure is not changed.

The invention provides an interaction method and system based on deep learning, and relates to the technical field of computer vision, and the method comprises the steps: collecting a continuous image sequence, extracting feature points, calculating the position variation and gradient variation of the feature points, constructing a two-dimensional feature vector, and carrying out the clustering through a Gaussian mixture model, thereby obtaining a stable feature class. And weighting the feature points based on a feature weight distribution function, calculating feature similarity and constructing a matching relationship. And calculating a dynamic parallax threshold in combination with the vehicle movement speed, screening reliable matching feature pairs, and solving pose transformation parameters. A feature change sequence is extracted through a feature detection network, a motion feature mapping matrix is constructed, and a compensation depth map is generated in combination with depth information. And scene reconstruction is carried out based on the compensation depth map and the pose transformation parameters, the scene confidence score is calculated, the pose parameters are calibrated, a high-precision pose parameter sequence is obtained, and the robustness of feature matching and the accuracy of pose estimation can be improved.

The invention discloses a visual inertial positioning method based on dynamic target detection and semantic information constraint, and belongs to the field of motion estimation and dynamic environment processing. According to the method, a dynamic target detection mechanism is introduced, the dynamic target is effectively detected based on the target detection network, inertial navigation information and geometric constraints, the dynamic target is effectively recognized in the image processing process, the corresponding dynamic feature points are screened out, the mismatching rate of the dynamic features is remarkably reduced, and high-quality observation input is provided for back-end optimization. In the back-end sliding window optimization stage, a semantic information consistency constraint method is constructed, and the estimation stability of the system in a weak texture area or a repeated texture area is enhanced by utilizing the consistency of feature points in the same semantic area on a geometric structure. Visual inertia pose estimation is realized based on dynamic target detection and semantic information constraint, and high-robustness and high-precision pose estimation can still be realized in a complex environment with dynamic interference of pedestrians, vehicles and the like and severe scene change.

The invention discloses a self-adaptive dynamic SLAM method based on Gaussian distribution, and relates to the technical field of dynamic environments. According to the method, the dynamic environment positioning precision is improved, the system can accurately recognize and eliminate interference of dynamic objects through a dynamic target elimination strategy based on CRF in combination with YOLO detection and conditional random field optimization, and a traditional SLAM system is likely to be affected by moving objects in the dynamic environment to generate pose offset, so that the system is not easy to operate. Reprojection error evaluation and feature point weighting processing are introduced, differential modeling is carried out on static points and dynamic points, the dependence of pose estimation on static features is strengthened, estimation errors caused by dynamic interference are effectively reduced, the positioning precision and stability of the system in a complex dynamic scene are improved, and the positioning accuracy of the system in the complex dynamic scene is improved. Moreover, the consistency and integrity of the map are enhanced, and the mapping efficiency and scene adaptability are improved.

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention relates to the field of data processing, in particular to a part machining detection method and system, and the method comprises the steps: obtaining images of a to-be-detected part and a template part on a production line, and carrying out the preprocessing of the images, and obtaining corresponding gray images; using an edge detection algorithm to obtain a gradient magnitude and a gradient direction of each pixel point in the grayscale image, performing non-maximum suppression in a neighborhood of each pixel point, screening candidate feature points, and based on the candidate feature points and a plurality of attributes in the neighborhood, respectively obtaining significance feature values of the candidate feature points of the to-be-detected part and the template part; matching is carried out according to the saliency characteristic values of the to-be-detected part and the template part, the comprehensive matching degree is calculated, whether the image of the to-be-detected part is abnormal or not is judged based on the comprehensive matching degree, and part machining detection is completed; according to the method, the real feature points and the false feature points are effectively distinguished by introducing multi-dimensional features such as significance feature values and shape complexity, and the mismatching rate caused by similar texture structures is reduced.

The invention relates to a point cloud registration method based on improved ISS-TOLDI features in combination with ICP, and belongs to the technical field of laser point cloud application. The method comprises the steps of obtaining a source point cloud and a target point cloud, and performing preprocessing; extracting feature points from the point cloud data by using an internal shape descriptor ISS algorithm; performing feature description on the extracted feature point set by using an improved TOLDI algorithm; performing coarse registration on the point cloud by using a sampling consistency initial registration algorithm; and performing fine registration on the coarsely registered point cloud data by using an iterative closest point ICP algorithm to complete point cloud registration. According to the method, the TOLDI algorithm is improved, so that the calculation complexity is reduced, the feature integrity is ensured, and the point cloud registration precision is higher.

The invention discloses a stereo matching method based on context geometric features and distortion parallax optimization, which comprises the following steps of: firstly, shooting a calibrated left picture and a calibrated right picture through a binocular camera, extracting multi-scale context features of the two pictures, and calculating the similarity between feature points according to the feature points of the left feature graph and the right feature graph under different parallaxes; the matching cost is the matching cost, so that the constructed geometric cube contains rich geometric matching information. Meanwhile, context features are copied and expanded into context feature cubes with the same size according to parallax, and rich context information is contained. The geometric cube contains matching conditions of feature points of left and right views with different parallax errors, cost aggregation is guided through context features of the left view, and context feature information, feature point cost matching information under different parallax errors and geometric information of adjacent feature points are preliminarily fused to improve cost matching reliability. And generating a geometric attention weight cube. And filtering the contextual feature cube.

The invention provides a deep learning-based die process die surface automatic supplement generation method and system, and relates to the technical field of deep learning, and the method comprises the steps: extracting the contour line and feature point information of a die surface as a training sample; feature learning is carried out based on a multi-scale image convolutional network and a double-flow network, and optimized local geometric features and global topological features are extracted; generating a supplementary modular surface through the generative adversarial network; and carrying out stress field constraint verification and then fusing with an original model. According to the method, intelligent supplementary generation of the mold surface is realized, and the mold design efficiency and quality are improved.

The invention provides a material data curve identification and fitting method based on deep learning, and relates to the technical field of material data extraction, and the method comprises the steps: obtaining an image file containing a plurality of material data curves; separating the material data curve from the image background by adopting an image segmentation technology, and extracting feature points of the material data curve through a convolutional neural network; and taking the feature points as control points, performing parametric fitting on the curve through polynomial or spline interpolation, and generating a smooth continuous curve. The method further comprises the following steps: detecting the position and boundary of a coordinate axis in the image file through an image processing technology; the OCR technology is used to identify the scale label on the coordinate axis, and the deep learning model is combined to analyze the numerical value and unit of the scale. According to the method, through innovative technologies such as multi-modal feature fusion, self-adaptive preprocessing and intelligent data labeling, precise recognition of complex curves, multi-curve separation and association, intelligent recognition of non-standard coordinate axes and efficient real-time processing are achieved.

The invention relates to an intelligent solid state disk quality detection method, system and device and a medium, and the method comprises the steps: obtaining test demand data of a to-be-detected solid state disk, extracting feature point data in the test demand data from the test demand data through a feature extraction algorithm, and obtaining demand feature point data; acquiring environment data, and transmitting the demand feature point data and the environment data to a pre-trained detection model for comparative analysis to obtain a test adjustment scheme; performing a virtual environment test according to the test adjustment scheme to obtain a virtual environment test result; and when the virtual environment test result does not conform to the expectation, obtaining difference information from the virtual environment test result, correspondingly modifying the test adjustment scheme according to the difference information to obtain an optimized adjustment scheme, and adjusting the test process and parameter configuration of the to-be-detected solid state disk according to the optimized adjustment scheme. The method has the effect of improving the accuracy of solid state disk quality detection.

The invention discloses an image registration method based on improved ORB feature extraction and grid feature matching. The method comprises the following steps: inputting an image to be extracted; using an improved FAST algorithm to extract feature points of the to-be-spliced image; establishing a BRIEF descriptor, and obtaining a BRIEF descriptor with a direction; according to the feature point set of the to-be-spliced image, establishing a Delaunay triangular grid taking the feature points as vertexes; judging the similarity of triangles, and obtaining similar triangles formed by the feature points; matching the similar triangle vertexes by using a Hamming distance to obtain an optimal matching feature point pair; obtaining affine matrix parameters by using an SVD method; according to the image registration method provided by the invention, the image quality is ensured, the feature extraction efficiency and the extraction precision can be improved, wrong matching is reduced, the stability is improved, the real-time performance of video stitching is improved, the waste of memory resources is reduced, the equipment cost is reduced, the working efficiency is greatly improved, and the method is suitable for popularization and application. And a better method is provided for image splicing.

The invention relates to the technical field of bolt length detection data processing, in particular to a bolt length detection system, which is characterized in that a three-dimensional data acquisition module generates a high-density point cloud and extracts a thread curvature extreme point, and a dynamic model matching module screens an optimal lead angle parameter based on a Hausdorff distance; and constructing an axial expansion model carrying phase offset gradient features. The projection mapping correction module adopts a genetic algorithm to optimize a compensation coefficient matrix, and generates a dynamic weight correction parameter to suppress geometric distortion; and the phase error correction module combines Fourier spectrum decomposition and an exponential decay weight coefficient to construct a frequency domain transfer function to eliminate an axial accumulative error. According to the method, the problems of projection distortion and phase deviation caused by the lead angle are effectively solved through geometric-phase double-domain correction, dynamic parameter iteration and an error collaborative suppression mechanism, and the spatial positioning precision of the feature points at the tail end of the thread is improved.

The invention discloses a target object pose estimation method, system and device and a medium, and relates to the technical field of pose estimation, and the method comprises the steps of offline data set establishment and online real-time multi-target pose estimation. In the off-line process, continuous RGBD streams surrounding a single object are collected, the relative poses of front and back frames are calculated frame by frame, and meanwhile, the three-dimensional surface of a target object is reconstructed to obtain a key frame off-line data set; in the online process, a visible target set in a data stream is firstly detected, VOS segmentation is performed on each target to obtain real-time effective texture and point cloud, offline key frame images of to-be-detected targets are arranged to realize single matching of all feature points and find out a closest reference frame at the same time, and RANSAC is adopted to calculate relative poses of a current frame target and the reference frame; according to the method, an expensive data labeling process is avoided, rapid integrated deployment of detection of an object without a new target is realized, the matching speed is improved, and meanwhile, online real-time multi-target pose estimation can be realized.

The invention discloses a multi-scale machine vision matching method, and belongs to the technical field of vision matching, and the method comprises the steps: determining an original scale template image I1 based on a search image I0 inputted by a user, and obtaining a template feature point set under each layer of pyramid of the original scale; calculating a template scale step s; calculating upper and lower limits of a template scale; calculating a scale factor set S; traversing the scale factor set S to obtain each layer of pyramid feature point set corresponding to different scale factors; performing feature correction on each layer of pyramid feature point set of which the scale factor is less than 1; calculating a pyramid image sequence set of a search image I0 input by a user; performing top layer template similarity calculation to obtain a top layer matching result; performing neighborhood suppression on all the top-layer matching results to obtain a top-layer matching result set; and traversing all pyramid layers by adopting a layer-by-layer approximation mode to obtain a final matching result. According to the invention, targets of multiple scales can be effectively positioned.

The invention discloses an FP-XFat feature device and a typical rescue scene-oriented three-dimensional reconstruction method and system, and relates to the technical field of three-dimensional reconstruction. The FP-XFat feature device is internally provided with a pre-training model, and the pre-training model comprises an improved PIOU loss function; the implementation mode of the improved PIOU loss function is as follows: 1) constructing a dynamically adjusted weight coefficient, reinforcing geometric constraints when the IoU is low, and weakening the constraints when the IoU is high; 2) introducing a shape difference metric item based on an arc tangent function, converting the aspect ratio into an angle dimension, and eliminating the influence of scale difference on shape judgment; and 3) adopting a gradient separation technology to block back propagation of an IoU gradient to a weight coefficient. The FP-XFat feature device introduces an improved PIOU loss function, and through an adaptive weight mechanism and a shape perception penalty term, the accuracy of feature point positioning can be significantly improved.

The invention relates to the technical field of fault detection, in particular to an intelligent fault detection and diagnosis method and system, and the method comprises the following steps: extracting and classifying fault feature vectors item by item based on the normal state and abnormal state operation data of a vehicle, transmitting the fault feature vectors to a fault coding layer through coding, inputting a potential space, and carrying out the clustering of feature points, and obtaining a preliminary fault identification feature. According to the method, the accuracy and the efficiency of fault diagnosis are remarkably improved by refining fault feature extraction and expanding sample analysis, and the accuracy and the efficiency of fault diagnosis are remarkably improved by analyzing normal and abnormal state data, extracting fine fault feature vectors, performing preliminary identification by utilizing a clustering technology, further expanding a sample set by simulating samples and deepening fault mode learning. And graded fault detection is introduced, response is adjusted according to fault severity, early warning is realized through dynamic monitoring, the equipment operation period is effectively prolonged, the shutdown risk is reduced, the maintenance cost is saved, and the system reliability is improved.

The invention discloses a dynamic scene robust vision SLAM (Simultaneous Localization and Mapping) method based on multi-feature collaborative optimization, which comprises the following steps of: acquiring an image sequence, carrying out dynamic target detection and segmentation through an instance segmentation network, generating a segmentation mask containing a dynamic region mark, and identifying and separating a dynamic object and a static background; removing feature points corresponding to the dynamic object based on the segmentation mask to obtain static feature points; carrying out pose estimation based on the static feature points, and for the key frame, carrying out feature matching with the previous key frame by minimizing a re-projection error, and solving to obtain the camera pose of each key frame; for non-key frames, performing camera pose tracking and data association on the previous frame by adopting an optical flow algorithm, and accumulating solving results to obtain pose tracks of all the non-key frames; the key frames and the non-key frames are subjected to differential processing by fusing feature matching and an optical flow algorithm, so that the calculation efficiency is remarkably improved while the positioning precision is ensured, and the real-time performance is improved.

The invention relates to the field of metal material nondestructive testing, and provides a non-ferromagnetic metal material surface profile reconstruction method based on a rotating alternating current electromagnetic field, which comprises the following steps of: determining a detection point array of a detection area, acquiring a vertical magnetic field signal z of the detection area by adopting a TMR magnetic sensing chip, and forming an initial Bz numerical matrix Bz0; performing bilateral filtering; obtaining a Bz numerical matrix Bz2 after morphological processing; obtaining a weighted Bz numerical matrix Bz3; a positive value matrix is formed, binarization processing is conducted on the positive value matrix, and a key numerical value matrix area Tkey containing crack related feature points is formed; calculating the center-of-mass coordinates of the disconnected independent areas, which are called nodes; on the basis of an improved Boruvka algorithm, new edge weight design is carried out on connecting edges between nodes, and a generated minimum spanning tree is effective representation of the surface profile of the bifurcated crack of the metal part.

In various examples, systems and methods are disclosed relating to generating each initial feature map of a plurality of initial feature maps based on a respective input image of an input dataset, each initial feature map, incorporating depth data of the respective input image, corresponds to a plurality of pixels of the respective input image, generating a sparse feature point cloud including a plurality of features determined using the plurality of initial feature maps, transforming the sparse feature point cloud into multi-resolution sparse grids, each of the multi-resolution sparse grids comprising a plurality of voxels, modeling, using a plurality of neural networks according to a hierarchal architecture, the multi-resolution sparse grids to construct a hierarchical volume representation, and providing constructed content based on the hierarchical volume representation.

The invention discloses an extended target feature point tracking method based on motion characteristic compensation, and belongs to the field of target tracking of computer vision, and the method comprises the steps: firstly constructing a matching feature optimization method based on robust feature points, reducing the number of target feature point mismatching, and achieving the estimation of optimal transformation model parameters; secondly, a tracking prediction correction method based on Kalman filtering constraint is designed, and the uncertainty of the matching tracking model is better compensated; and finally, designing a long-time and short-time fusion feature template updating strategy, fusing historical information and current target state information, enhancing the robustness of template features, effectively avoiding noise superposition, and realizing long-time-sequence stable target tracking. According to the method, the image space information and the interested feature point information are interactively interpreted in the feature matching and prediction process, so that the model of the matched features is more robust, the prediction target position error is smaller, and the long-time tracking result is more stable and accurate.

In a dynamic environment, a visual SLAM (Simultaneous Localization and Mapping) system often causes the problems of large positioning error and inaccurate map construction due to dynamic target interference. In order to improve the robustness and precision of the system, the invention provides a dynamic scene SLAM optimization method based on improved YOLOv11 and geometric consistency constraint. Firstly, ORB features in a scene are extracted, and meanwhile a prior dynamic object and feature points on the prior dynamic object are removed through a YOLOv11 semantic segmentation model; secondly, eliminating feature points on the potential dynamic object by utilizing geometric consistency constraint, and recovering a background shielded by the dynamic object through a semantic perception Gaussian filter; and finally, selecting a high-quality key frame and applying the key frame to loopback detection and global optimization, constructing a basic Gaussian graph through a group of determined poses and point clouds, and finally fusing repair frame information to realize new view rendering and three-dimensional scene optimization.

The invention discloses a 6D pose estimation method and device based on online 3D Gaussian reconstruction and a medium, and the method comprises the steps: firstly obtaining the initial pose of a target object in an input RGB-D image through a feature point method, then optimizing the initial pose according to the depth corresponding to a matched pixel between two images, putting a new frame and the optimized pose into a local image, and carrying out the optimization of the initial pose. The method comprises the following steps that: firstly, a local image is obtained by a thread, the local image is matched with other frames by the other thread, the pose is further optimized, finally, the 3D Gaussian is initialized and optimized according to the image and the pose in the local image, and an obtained model provides new loss item auxiliary optimization for the previous pose optimization process in turn. The invention provides a method for solving the 6D pose estimation problem of any object, any model information related to the target object does not need to be provided during operation, and the universality of the 6D pose estimation method is improved.

The invention discloses a detection method, device and equipment for wafer edge polishing. The detection method comprises the following steps: positioning a wafer to be detected; obtaining plane part morphology data and curved surface morphology data of the positioned wafer to be detected; performing curve fitting on the plane part morphology data and the curved surface part morphology data to obtain a first curve and a second curve; performing feature extraction and feature point matching on the curve; calculating a homography transformation matrix between the two curves based on the matched feature point pairs; determining an overlapping area of the first curve and the second curve; determining an optimal stitching point; and butting the first curve and the second curve according to the optimal stitching point, and taking the butted curve as an edge detection curve of the wafer to be detected. Through the integrated design of the functions of centering positioning, height compensation, curve fitting, morphology observation and the like of the wafer, the data synchronism of multi-parameter measurement is ensured, and the accuracy and reliability of a measurement result are enhanced.

The invention discloses a 3D GS cultural relic digital reconstruction method and system based on a block chain, and the method comprises the steps: collecting the RGB image data and depth perception data of a cultural relic, eliminating the influence of different shooting conditions through an illumination separation processing technology, and building a standardized image data set; recognizing a surface area suitable for reconstruction based on image analysis, determining feature point distribution by using kernel density estimation, and generating initial three-dimensional representation through Gaussian ellipsoid fitting; performing gradient calculation and feature extraction on the depth data, and combining with Gaussian representation to form a geometric constraint mechanism; self-adaptive encryption based on visual importance is executed for a sparse region, and a layered rendering effect is achieved through opacity parameter adjustment; the rendering characteristics and the conversion relation of different view angles are analyzed, key observation points are determined through stability analysis, and a smooth multi-view-angle display sequence is constructed; and integrating multi-view rendering information to generate volumetric representation, and completing right confirmation of the high-quality three-dimensional digital model through digital signature.

The invention discloses a visual SLAM method, device and equipment and a storage medium, and relates to the technical field of visual SLAM, and the method comprises the following steps: carrying out the target detection and feature point extraction of each frame of RGB image, and obtaining a plurality of targets and corresponding target detection frames; comparing the weighted values of the plurality of targets in the next frame of RGB image with a weighted threshold value, updating the initial dynamic levels of the plurality of targets according to a comparison result, and determining whether the plurality of targets are non-static targets or not according to an updating result; obtaining chi-square values of feature points contained in a target detection frame corresponding to the non-static target based on the multi-frame depth image, comparing the chi-square value of each feature point with a set threshold value, and rejecting the feature points smaller than the set threshold value to obtain static feature points; and constructing a three-dimensional map based on the static feature points. The method can adapt to the dynamic environment, and it is guaranteed that the constructed map is consistent with the actual dynamic environment.

The invention relates to a method for detecting the gradient of a plane and optimizing the machining precision, and the method comprises the following steps: (1), carrying out the preliminary path collection processing of the plane at the current position before the actual machining; (2) carrying out optimal feature point selection based on the acquired data set; (3) carrying out plane fitting processing based on the selected optimal feature point; (4) calculating a normal vector of the current position plane and a corresponding base vector based on the selected optimal feature point; (5) constructing a local coordinate system based on the current position, and calculating transformation between the local coordinate system and the workpiece global coordinate system; and (6) optimizing the machining path of the cutting head according to the local coordinate system. By adopting the method for detecting the inclination of the plane and optimizing the machining precision, the machining path is optimized according to the local coordinate system, so that the machining precision is effectively improved, and errors caused by inclination of the workpiece are reduced.

The invention relates to the technical field of ship detection, and discloses a ship lockage safety detection method and system, and the method comprises the steps: obtaining and analyzing ship image data in real time, and separating a ship target; key feature points representing the core form and structure of the ship target are identified, and a spatial distribution diagram containing the relative position relation of the key feature points is constructed; matching the spatial distribution map with a pre-stored standard ship three-dimensional model library, and acquiring passing information of the ship target according to a matched standard three-dimensional model; according to the method, lockage safety evaluation is carried out based on passing information, deep analysis is carried out on ship image data, key feature points are identified, a spatial distribution diagram is constructed, and then matching with a standard three-dimensional model is carried out, so that accurate ship passing information is obtained; the problem of size and position information deviation caused by inaccurate hull contour extraction in the prior art is effectively solved, and the reliability of lockage safety assessment is remarkably improved.

The invention provides an image splicing method based on improved ORB and MLESAC, and belongs to the technical field of image splicing. According to the method, after a to-be-spliced image is obtained, firstly, feature point detection is performed on the image by using a rapid feature detection method, a descriptor is generated by using a binary robust independent basic feature method, and preliminary matching points are obtained after violent matching; secondly, re-extracting feature descriptors from the screened feature points by using a scale invariant feature transformation method, finding two optimal matches for each descriptor through a K-nearest neighbor algorithm, and adding a ratio test to filter out weak matches; and performing weighting processing on the data points by using a maximum likelihood estimation random consistency algorithm, and performing multiple iterations to obtain an optimal model. And finally, generating a spliced image through a weighted average image fusion technology. According to the method, ORB and SIFT are used in a cascading mode in the rough matching stage, a more efficient MLESAC algorithm is provided to complete fine matching, and the registration precision is remarkably improved while the image splicing real-time performance is ensured.

The present invention discloses an intelligent detection method for determining forming defects by the offset of the blank contour of a stamping part, belonging to the field of sheet metal forming quality detection. In the present invention, the offset amounts of the contour feature points of the blank in the defective state and the defect-free state are obtained through finite element simulation of the stamping process. After manually annotating the defect types, a training data set and a validation data set are obtained, and based on this, a BP neural network model is constructed and trained to obtain a defect detection model. At the stamping site, a laser rangefinder is used to measure the offset amount of the actual stamping blank contour feature points along the material inflow direction, and the offset amount is input into the BP neural network defect detection model to output the defect judgment result. The method of the present invention greatly alleviates the current manual dependence on stamping forming quality detection and has the advantages of high detection efficiency, high accuracy, and low cost.