426 results about "Stereo matching" patented technology

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The invention discloses a distribution network tree barrier real-time analysis method and system based on dynamic vision and SLAM. The method comprises the following steps: generating a dynamic visual baseline by cooperatively controlling the translation and flight displacement of an unmanned aerial vehicle holder, and constructing a bionic binocular model to simulate a time sequence image into a binocular image pair; generating a depth point cloud through epipolar correction and stereo matching; key targets are recognized and extracted through a semantic segmentation network, and semantic point clouds are generated; establishing a dimensionality reduction motion model by utilizing pan-tilt stability augmentation, and fusing a visual inertial odometer and RTK data by adopting a filtering or optimization algorithm to realize centimeter-level pose estimation; and finally, performing optimization processing on the semantic point cloud, completing three-dimensional reconstruction based on multi-modal fusion, and outputting a risk assessment result through tree line spacing calculation and safety margin analysis. According to the invention, accurate, efficient and automatic routing inspection and risk early warning of the distribution network tree obstacles are realized.

The invention particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention discloses a monocular and binocular cooperative localization and mapping method and device for underwater refraction compensation, and the method comprises the steps: extracting checkerboard angular points, compensating an underwater light path through employing a two-time vector correction model based on a Snell law, and precisely calibrating the internal and external parameters of a binocular camera. According to the method, an underwater real light path is effectively restored, and calibration errors caused by medium refraction are remarkably reduced. Then epipolar correction is performed on the corrected left and right images, so that the geometric consistency of stereo matching is ensured; and finally, monocular and binocular poses are fused, and seven-degree-of-freedom similarity transformation global alignment is applied, so that the problem of monocular scale uncertainty is effectively solved, and high-precision and high-robustness three-dimensional mapping is realized by means of binocular real scale introduction. Key links such as camera pre-calibration, refraction compensation, image correction, monocular tracking, binocular depth measurement, scale alignment and the like are connected in series to form an integrated process, so that the underwater vision positioning and mapping precision is improved.

The invention relates to the technical field of image data processing and intelligent control, and discloses a patch board spot welding track intelligent control method and system based on visual perception. According to the method, synchronous image streams are collected through a binocular vision sensor, and epipolar correction image pairs are generated through timestamp synchronization, ROI extraction and epipolar correction processing; calculating a disparity map by adopting a stereo matching algorithm, and constructing a compensated three-dimensional point cloud model based on deformation vector field fusion multi-frame point cloud data of a radial basis kernel function; welding spot position error vectors are generated by extracting welding spot feature points and performing spatial filtering optimization; and carrying out inverse kinematics calculation on the mechanical arm by adopting a damping least square method, carrying out safety constraint optimization in combination with prospective collision risk assessment and real-time pose data, and generating a trajectory compensation instruction. According to the method, the problems of dynamic deformation compensation and motion safety in patch plate welding are solved, the submillimeter welding spot positioning precision is achieved, and the welding quality stability and the system robustness are improved.

The invention provides a robust stereo matching method fusing monocular semantic prior and multi-dimensional expert aggregation, relates to the technical field of computer vision and stereo matching, establishes a robust stereo matching framework fusing monocular semantic prior and multi-dimensional expert aggregation, and comprises a monocular branch and a binocular branch, inputting a stereo image into a robust stereo matching framework based on fusion of monocular semantic prior and multi-dimensional expert aggregation for matching calculation, wherein the matching calculation comprises the following steps: extracting semantic prior features of the input image by using a monocular branch; the method comprises the following steps: extracting a geometric enhancement feature map of a stereo image by using binocular branches, generating a matching cost body based on the geometric enhancement feature map, and carrying out refined iterative updating on the matching cost body to obtain a matched disparity map. According to the method, monocular and binocular depth estimation is cooperatively supported in a unified network architecture, and a parallel monocular depth prior path is utilized to actively guide and strengthen a core binocular matching process.

The invention relates to the field of power transmission line channel risk hidden danger monitoring, and provides a data fusion power transmission line channel risk hidden danger monitoring method and system, and the method comprises the steps: collecting the multi-modal sensing data of a power transmission line channel, and generating a multi-modal data flow of a unified time-space coordinate; constructing a three-dimensional space point cloud through a phase unwrapping and stereo matching fusion algorithm, and fusing multi-modal data to generate a space probability tensor; extracting risk semantic latent variables, constructing a Bayesian network and identifying potential risks; performing tensor product on the potential risk and the environmental data to generate a dynamic risk enhancement feature matrix, and constructing a nonlinear dynamic threshold curved surface through quantum annealing and Gaussian process regression; a mechanical equation is constructed, Gaussian kernel density estimation and numerical simulation are combined, the evolution trajectory of the risk in the space-time dimension is predicted, and a risk thermodynamic diagram and early warning information are generated; and generating a structured risk early warning report by adopting a natural language processing method. And the accuracy of power transmission line channel risk hidden danger monitoring is improved.

The invention relates to the technical field of three-dimensional reconstruction, and provides a local feature adaptive fusion and high-fidelity new view angle synthesis method based on three-dimensional reconstruction. According to the method, a local feature module is introduced into a point cloud network, robust matching of local features is realized, a self-adaptive matching strategy is combined, high-resolution images can be efficiently processed, and the precision and stability of three-dimensional reconstruction are remarkably improved. The method is suitable for application scenes such as three-dimensional rendering, three-dimensional reconstruction and new view angle synthesis. The specific process comprises dense reconstruction and initial rendering; and carrying out missing region repairing and iterative optimization. Generating a high-precision point cloud based on an improved stereo matching algorithm, and rendering an initial image of a target view angle in combination with camera parameters; a conditional video generation model is adopted, and holes, distortion and artifacts in initial rendering are repaired; and carrying out progressive complementation on the occlusion region by using the view angle consistency constraint, and improving the reconstruction integrity by jointly optimizing the point cloud geometry and texture. According to the method, the final picture quality is remarkably improved through feature matching optimization and efficient processing capacity. Local feature branches and global context information are coordinated, and the problem of matching ambiguity in a complex scene is solved; the adaptive fusion strategy gives consideration to high-resolution image processing efficiency and detail retention; and the detail fidelity and the overall quality of the rendering result are effectively improved.

The invention discloses a badminton tracking method and system based on three-dimensional vision, and relates to computer vision. Badminton motion video data of a left view and a right view are synchronously collected through a binocular camera, a left view frame sequence and a right view frame sequence which are aligned in time are obtained, and the collected video data are preprocessed; obtaining standardized left and right view input frame data; according to input frame data, constructing a 2D key frame detection model based on an improved YOLOv8 network, and extracting the sum of 2D key frame coordinates of left and right views; according to the 2D key frame coordinate sum of the left view and the right view, 3D space positioning is carried out on the badminton through stereo matching and track prediction, 3D track data are obtained, according to the 3D track data, a broken track is repaired through a frame leakage compensation mechanism, and a final 3D track is obtained. In order to solve the problem that in the prior art, key frames are prone to being lost and track breakage is caused during high-speed movement such as smash, the breakage track of the shuttlecock is repaired.

The invention relates to the technical field of three-dimensional reconstruction, and discloses a mark point region priority processing-based three-dimensional scanner rapid reconstruction method, which comprises the following steps of: configuring a plurality of mark points in a region range of a target to-be-measured object, and collecting left and right images of the target to-be-measured object; identifying mark points in the left image and the right image, and extracting two-dimensional image coordinates of each mark point; performing region division on the left and right images based on the center point; performing feature extraction and stereo matching processing on a target image corresponding to the region of interest; calculating a local three-dimensional point cloud of each mark point in the region of interest according to the local disparity map, and analyzing camera poses under a plurality of target view angles according to the two-dimensional image coordinates and the local three-dimensional point clouds; converting the local three-dimensional point cloud to a preset world coordinate system according to the camera pose; and reconstructing a three-dimensional model of the target object to be measured according to the three-dimensional point cloud data under the plurality of target viewing angles. According to the invention, the reconstruction efficiency of the three-dimensional scanner can be improved.

Photometric stereo techniques enable using a single camera to perform an enrollment process for creating a user-specific anatomical model of an eye for gaze tracking. The user-specific anatomical model includes information about a user's center of vision at multiple dilation states of the eye, which can be used to enhance the accuracy of gaze tracking techniques. Accurate gaze tracking techniques enable the use of gaze tracking at close range, for example, gaze tracking within a head-mounted display device.

The invention relates to the technical field of computer vision, and particularly provides a tunnel video stream three-dimensional modeling system based on binocular stereo matching and SLAM (Simultaneous Localization and Mapping), which comprises a multi-modal image acquisition unit, a binocular infrared camera and an RGB (Red, Green and Blue) camera are configured to synchronously acquire an infrared image pair, an RGB image pair and inertial measurement unit data of a tunnel environment, the multi-source data is uploaded to the cloud processing platform in real time through the wireless transmission module; based on a dynamic switching mechanism of environmental perception, performing adaptive fusion pose estimation of a feature point method and a direct method on input multi-modal image data; the stereo matching and dense reconstruction unit is used for calculating a disparity map by adopting an improved self-adaptive window stereo matching algorithm, generating a three-dimensional point cloud in combination with the pose information output by the pose estimation unit, and constructing a global consistency point cloud model of the tunnel scene through a time sequence point cloud registration and splicing algorithm; and a multi-algorithm target detection and semantic fusion unit. The method can meet the requirements of precision and robustness of tunnel reconstruction.

The invention provides an intelligent measuring and calculating method for the high-density fish body length of a culture pond in the technical field of fish body length measuring and calculating. The method comprises the steps that S1, a large number of historical fish school images are acquired to train a fish detection model; s2, setting a fish form constraint parameter, calibrating a camera parameter, collecting a culture pond video through a binocular camera, and analyzing the culture pond video to obtain a left eye image and a right eye image; s3, inputting the left eye image and the right eye image into a fish detection model to obtain a fish body bounding box carrying confidence, and filtering the fish body bounding box through the fingerling form constraint parameters and the confidence; s4, performing stereo matching on the fish body bounding boxes based on the left eye image and the right eye image so as to calculate the depth value of the fish body corresponding to each fish body bounding box; and S5, obtaining the fish body length based on the fish body bounding box through the camera parameters and the depth value. The method has the advantages that the accuracy, efficiency and reliability of measuring and calculating the length of the fish body are greatly improved.

The invention provides an intelligent real-time detection and risk assessment method for road surface potholes based on deep learning, and relates to the technical field of deep learning, which comprises the steps of dividing detection sub-regions by acquiring geographic information data, calculating a difficulty coefficient to determine the number of unmanned aerial vehicles and task allocation, and generating an optimal aerial photography path based on a deep learning algorithm. Preprocessing and feature extraction are carried out after images are collected, pothole point cloud data are constructed by adopting an improved multi-view stereo matching algorithm, and then a pothole damage assessment report is generated. According to the invention, intelligent and automatic detection and evaluation of the pavement pit slot are realized, and the detection efficiency and accuracy are improved.

The invention relates to the technical field of computer vision, in particular to a three-dimensional reconstruction method and device based on optical polarization and stereoscopic vision principles. The three-dimensional reconstruction method sequentially comprises the steps of image preprocessing, polarization parameter extraction, binocular stereo matching, disparity map generation and optimization, normal vector correction, depth information fusion and three-dimensional point cloud reconstruction and modeling generation. A polarization normal vector field is constrained and corrected as a'skeleton ', and the azimuth angle pi ambiguity problem which puzzles polarization three-dimensional reconstruction for a long time is solved; high-frequency surface normal details contained in corrected polarization information are used as textures to enhance and fill up depth information loss of binocular vision in weak texture and repeated texture areas, the inherent limitation of a single sensing technology in a three-dimensional reconstruction task is overcome, and the three-dimensional reconstruction of the surface of an object, especially a diffuse reflection object, is realized. And high-precision and high-integrity three-dimensional shape recovery is realized.

The invention discloses a panoramic image determination method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a current scene image, shot by at least one camera device, of an environment to which a vehicle belongs, and kinematics data and attitude data of the vehicle from a current shooting moment to a previous shooting moment; determining a scene image transformation matrix based on the kinematics data and the attitude data; acquiring a historical scene image shot by the camera device at a previous shooting moment, and performing stereo matching on the current scene image and the historical scene image based on the scene image transformation matrix and shooting parameters of the camera device to obtain a scene stereo image corresponding to the camera device; and determining and displaying a panoramic image based on the scene stereo image of each camera device. The authenticity, accuracy and reliability of panoramic image determination are improved, and the driving safety is improved.

The invention discloses an intelligent multi-view camera tracheal intubation image processing method, device and system, and relates to the technical field of medical instruments, the method comprises the following steps: in a tracheal intubation process, a multi-view image acquisition unit collects a plurality of images and combines the images to form an image pair; and carrying out stereo matching and depth calculation on the image pairs to generate a depth map. And performing three-dimensional reconstruction by combining the depth map and the original image to generate a three-dimensional model. And finally, identifying the target in the three-dimensional model and outputting the depth of the target as an image processing result. The technical problems that an existing multi-view camera is inaccurate in depth perception and insufficient in real-time performance during tracheal intubation image processing are solved, and the technical effect of improving the depth perception precision and the real-time processing capacity of the multi-view camera during tracheal intubation image processing is achieved.

The invention discloses a sparse view angle three-dimensional Gaussian language field construction method and system for humanoid robot grabbing. Comprising the following steps: acquiring an RGB image set and a user natural language instruction under a sparse view angle; reconstructing a three-dimensional point cloud through stereo matching, and initializing a three-dimensional Gaussian primitive field after noise elimination and measurement alignment; two-dimensional semantic features are embedded into the field, joint optimization is carried out through a double-path semantic supervision module, and a three-dimensional Gaussian language field with consistent semantics is constructed; wherein the dual-path semantic supervision comprises an object perception path and a global context path, the local semantic consistency and the overall semantic relationship are constrained respectively, and the final semantic representation of each Gaussian primitive is obtained through weighted fusion; and finally, candidate grabbing postures are generated, semantic reordering is carried out in combination with a language instruction, and the optimal grabbing posture is screened through geometric-semantic joint scoring. Under the sparse image set input condition, the execution accuracy of a robot grabbing task under a complex instruction can be improved.

This invention relates to the field of intelligent reconstruction technology for 3D models, specifically a method and system for intelligent reconstruction of 3D building models based on deep learning. The method includes acquiring multi-source data of the target ancient building; generating a dense point cloud through motion reconstruction and multi-view stereo matching; identifying structural and non-structural components through semantic segmentation and reconstructing an initial mesh model with semantically labeled information; using a graph neural network to analyze the spatial topological relationships of structural components, and combining this with a historical construction knowledge base to generate hierarchical combination methods and connection constraint rules; adaptively adjusting the pose of mesh nodes according to the connection constraint rules to drive the precise assembly of parameterized components, and fusing them to obtain a structured 3D model of the target ancient building. This invention can restore the structural component connection relationships in hidden areas of ancient buildings under non-contact acquisition conditions, significantly improving the structural rationality and geometric accuracy of the 3D model.

The invention discloses a PCB soldering paste printing three-dimensional defect detection system based on multispectral imaging, and relates to the field of machine vision detection, and the system comprises a multispectral image acquisition module, a four-channel industrial camera based on RGB and near infrared, and a tunable LED light source array; acquiring reflection characteristic data under different penetration depths through a wavelength switching mechanism; the motion control trigger module is used for realizing positioning based on an XYZ three-axis objective table driven by a servo motor in cooperation with feedback of an encoder; a pulse width modulation signal is adopted to coordinate the moving speed of a camera shutter and a platform; according to the method, RGB and NIR four-channel imaging is combined with Beer-Lambert law modeling, so that double verification of material component quantitative analysis and three-dimensional shape reconstruction is realized, and limitation of monocular vision is avoided; structured light projection and binocular stereo matching technologies are adopted, cross-frame data alignment is realized in cooperation with an ICP algorithm, and detail defects can be better detected.

The application provides a three-dimensional reconstruction method based on binocular stereo matching, and relates to the technical field of binocular stereo vision. The method combines binocular stereo matching algorithm, triangulation algorithm and surface texture mapping, and can realize surface reconstruction of scene objects in different environments. After stereo calibration is performed on a binocular camera, the internal and external parameters of the binocular camera are obtained, stereo correction is performed, then an image disparity map of the corresponding scene object is generated according to an optimized semi-global stereo matching algorithm, after the disparity information is obtained, the point cloud information converted by the object disparity is triangulated on the surface according to the triangulation algorithm, and finally three-dimensional surface reconstruction of the object is realized through the texture mapping technology. The application can reduce the influence of noise in the disparity map generation process, i.e. the semi-global stereo matching algorithm, improve the accuracy of the disparity information, and complete the three-dimensional surface reconstruction task of the object with little sacrifice of time performance.

The invention relates to the technical field of mine safety monitoring, in particular to a roadway section convergence multi-view three-dimensional reconstruction method and a monitoring system thereof.The method comprises the steps that a rectangular plane coordinate system is established on a roadway section, a plurality of measuring areas are arranged in the circumferential direction, a plurality of equal-height measuring points are evenly distributed in each measuring area in the elevation direction of the section, and every two adjacent measuring areas share at least one measuring point; a laser structured light projector and a CMOS receiving camera are arranged at each measuring point; matching the feature points through an improved stereo matching algorithm to obtain depth feature point information and establish a section point cloud; dividing deformation into local deformation and overall deformation, establishing deformation association between measurement points through deformation feature description, and obtaining overall convergence information of the section by adopting a point cloud registration method after local deformation compensation is formed; a section safety evaluation model is established, section deformation evaluation and reinforcement suggestions are given, all-directional non-blind area monitoring of the section is achieved, and the monitoring precision reaches the millimeter level.

The application discloses a kind of measurement system and method of advance hydraulic support posture angle based on binocular vision, including the connection binocular camera of explosion-proof terminal, the process of real-time measurement advance hydraulic support operation;Before supporting operation, the initial posture angle of advance hydraulic support is measured;During supporting process, the posture angle of advance hydraulic support change at different time is obtained by Euclidean transformation and posture angle algorithm;Innovation optimization is carried out to the feature detection and stereo matching algorithm of target during measurement process.The efficient measurement of advance hydraulic support posture angle is realized by Euclidean transformation and innovative posture angle algorithm, and the solving algorithm is fast, and the measurement time is short;Continuous optimization target feature detection and target stereo matching algorithm, improve the accuracy and robustness of solution result.The application can effectively reduce the measurement error of advance hydraulic support posture angle, complete real-time non-contact online accurate measurement in mine roadway.

The invention relates to the technical field of container loading and unloading operation automation, in particular to a system and a method for identifying container numbers of railway track-mounted cranes based on binocular vision, comprising a binocular vision acquisition module, a data processing and control module, a distance measuring and triggering unit, a container number identification unit and a system interaction interface; the binocular vision acquisition module is electrically connected with the data processing and control module, the binocular vision acquisition module is used for synchronously acquiring a left image and a right image of a small surface of a container, and the data processing and control module is used for processing and controlling data acquired by the binocular vision acquisition module; the data processing and control module comprises a distance measuring and triggering unit and a container number recognition unit, and the distance measuring and triggering unit calculates the accurate distance between the lifting appliance and the small surface of the target container through a stereo matching algorithm according to a depth image given by the data processing and control module; and the container number identification unit carries out container number identification on the acquired image under a triggering condition.

The invention discloses a point cloud data enhancement method based on a false point cloud technology, and the method comprises the steps: innovatively designing a binocular depth estimation method for rapidly inspecting a residual structure, and extracting the feature information of three levels through a real-time binocular stereo matching network RealtimeStereo; a rapid feature aggregation module with an attention mechanism aggregates the features to achieve the performance of a high-cost feature extraction network; a residual structure aggregation cost volume is designed, a two-stage depth optimization module is introduced, the two-stage depth optimization module comprises an accelerated expansion depth self-optimization module and a KNN-based point cloud depth correction algorithm, the accelerated expansion depth self-optimization module carries out self-optimization on a depth map, and the KNN-based point cloud depth correction algorithm introduces point cloud information to optimize the depth to generate a false point cloud with higher quality; an auxiliary module is innovatively designed to remove invalid information; and finally, a high-quality enhanced point cloud is generated through a depth projection module and a point cloud merging module, so that the quality and practicability of point cloud data are improved.

The invention provides a binocular stereo matching method and system, and the method comprises the steps: obtaining a left and right stereo image pair captured by a binocular camera, and carrying out the feature extraction and enhancement through a depth separable convolution basic network and an ECANet attention model; carrying out topological structure transformation by using a TopoAug feature enhancement strategy, constructing a topological consistency loss function and carrying out adaptive feature fusion; a large neighborhood search strategy and a hybrid node-destructor model are adopted to construct an initial cost body, and cost aggregation is carried out through capacity routing; and constructing an M uniform loss grid and a grid motion model to carry out parallax estimation, and obtaining a final parallax map through unsupervised consistency optimization. Through the technologies of lightweight network design, topology perception feature enhancement, hybrid node optimization cost body construction, unsupervised consistency optimization and the like, the calculation complexity is reduced while high precision is kept, and the method is particularly suitable for real-time monitoring scenes in resource-constrained environments such as substations and the like.

This invention discloses a multi-vision method for measuring the dimensions of small workpieces based on a binocular vision system, belonging to the field of workpiece dimension measurement technology. Addressing the difficulty in achieving accurate three-dimensional dimension measurement using existing methods, the following solution is proposed, comprising the following steps: S1: Chessboard corner point extraction; S2: Calibration plate removal; S3: Plane fitting; S4: Vertex extraction; S5: Stereo matching algorithm; S6: Dimension calculation. The average absolute error of this invention at different angles is 0.33 mm, the total relative error is 0.90%, and the variance is less than 0.01 mm. 2 This enables relatively accurate measurement of the three-dimensional dimensions of small workpieces from multiple perspectives.

The invention discloses a light-weight heat map stereo matching method and device, which are used for solving the technical problem that the precision of a final parallax result is poor due to the fact that information is easy to lose when a tiny depth difference is captured by the existing light-weight heat map stereo matching method. The method comprises the following steps: acquiring a left eye thermal image and a right eye thermal image, and inputting a preset thermal image stereo matching model (including multi-scale fusion feature extraction, edge feature extraction, an enhanced wavelet attention module, a plurality of cost aggregation residual blocks and a convolution module); a multi-scale module extracts features, generates a left eye and right eye fusion feature map, and constructs an initial cost body; the edge module outputs a left-eye edge probability feature graph, and an intermediate cost body is obtained by combining the initial cost body; the wavelet attention module outputs an attention weight according to the left eye fusion feature map; the cost aggregation residual block is multiplied by the middle cost body according to the weight, and an initial disparity map is generated through disparity regression; and the convolution module combines the left-eye edge probability feature map for refining, and finally generates a target disparity map.

The invention relates to the technical field of building material performance detection, in particular to a concrete coarse aggregate corner angle measurement system based on digital image processing, which comprises a multi-view image acquisition module, a three-dimensional point cloud reconstruction module, a corner angle feature vector extraction module and a corner angle quantitative calculation module. A multi-view imaging system is constructed through a rotary objective table and a binocular camera, surface images of aggregate at different angles are obtained, and high-precision three-dimensional point cloud data are generated in combination with stereo matching and a point cloud dense reconstruction technology. And further constructing an edge feature vector based on the volume compression ratio and the surface roughness, inputting a pre-trained support vector regression model, outputting a normalized edge index, and realizing objective quantitative evaluation of the coarse aggregate edge. The concrete aggregate quality grading and screening device has the advantages of being high in precision, high in automation, wide in adaptability and the like and is suitable for quality grading and screening of concrete aggregates.

The invention relates to the field of binocular stereo vision, and discloses a binocular stereo matching method and system based on multi-scale iterative optimization and related equipment. The method comprises the steps of performing semantic structure feature extraction on a corrected left view and a corrected right view through a feature extraction network; determining a cost space pyramid and a probability matrix according to the left view feature map and the right view feature map output by the feature extraction network; extracting a multi-scale context feature of the left view through a context sensing network, and initializing the multi-scale context feature to obtain a hidden state and input of a recursive network; during recursive network iteration, according to the probability matrix, a multi-range search strategy is adopted to index local cost from the cost space pyramid; and inputting the local cost into the recursive network, combining the hidden state obtained by the context features and the input, and carrying out iterative updating on the parallax value to obtain a parallax map, thereby improving the accuracy of parallax calculation.