689 results about "Three dimensional vision" patented technology

The invention discloses a humanoid robot grabbing method based on three-dimensional vision, and the method comprises the following specific steps: S1, environment modeling and perception: carrying out the three-dimensional scanning of a working environment, and constructing a three-dimensional map of the environment; s2, object identification and 6D attitude estimation: identifying a target object, and estimating the position and attitude of the object in a camera coordinate system; s3, coordinate conversion and target positioning, wherein the posture of the object is converted into a robot base coordinate system from a camera coordinate system through TF conversion; and S4, path planning and motion control are conducted, specifically, path planning is conducted through a cumotion algorithm, a grabbing path is generated, and the two arms are driven to move according to the planned track to achieve grabbing. By means of environment modeling and sensing, the robot can update environment information in real time, conduct three-dimensional scanning on the working environment and generate the three-dimensional grid model of the environment in real time, the model is used for obstacle detection and avoidance planning, and the dynamic environment adaptability of the robot to object grabbing is improved.

The invention relates to a non-standard special-shaped part high-precision measurement and verification method based on three-dimensional vision, and belongs to the field of three-dimensional reconstruction and computer vision. Point cloud data of a non-standard special-shaped part are obtained through multi-angle scanning by means of multi-camera-projector collaborative layout in combination with a structured light coding technology and a phase shift code technology; the method comprises the following steps of: acquiring point cloud data, ensuring comprehensiveness and accuracy of the data, realizing high-precision alignment of the point cloud data through matching an underivative optimization algorithm with image feature points, and performing fusion, denoising and registration processing on the acquired point cloud data to ensure that point clouds acquired from a plurality of angles can be effectively fused so as to reconstruct a high-precision three-dimensional model. In the measurement and verification stage, the deviation between the point cloud data and the CAD model is measured through a standard coordinate system, multi-stage registration and a high-precision algorithm, rapid detection and size verification of the geometric deviation of the industrial workpiece are achieved, and reliable technical support is provided for quality control.

The invention discloses a body-in-white welding seam recognition and automatic welding method based on a machine vision technology, particularly relates to the technical field of computer vision and image processing, and is used for solving the problem of welding seam track recognition accuracy caused by insufficient processing capability of an existing three-dimensional vision recognition method on incomplete and uncertain point cloud data. Through the steps of multi-view point cloud acquisition and registration, probabilistic confidence evaluation, region growth of track continuity constraint, multi-track fusion optimization and the like, accurate identification of a body-in-white welding seam track under a complex working condition is realized; firstly, multi-view point cloud data are obtained, probabilistic registration is carried out to generate a confidence evaluation result, then candidate tracks are generated based on confidence weighting and semantic constraint, finally, an optimal track is generated through intelligent optimization and converted into a welding instruction which can be executed by a robot, and the accuracy and robustness of weld joint recognition are effectively improved.

A method and apparatus for reconstructing semantic instance, a device and medium, which relate to the field of three-dimensional vision. The method comprises: processing an original image by using a first target detection network to obtain first feature information of a target object, and processing a three-dimensional point cloud by using a second target detection network to obtain second feature information; predicting a first coarse point cloud on the basis of the first feature information, and predicting a three-dimensional detection result on the basis of the first feature information and the second feature information, so as to obtain a second coarse point cloud; and obtaining an initial point cloud of the target object on the basis of the first coarse point cloud and the second coarse point cloud, and processing the initial point cloud by using preset shape generation network to obtain semantic instance reconstruction result of the target object.

A method and apparatus comprising computer code configured to cause a processor or processors to obtain, from a bitstream, a mesh representing an encoded volumetric data of at least one three-dimensional (3D) visual content; partition a plurality of vertices of the mesh into a plurality of groups; and decode the encoded volumetric data by predicting the vertices in each group of the plurality of groups based on a plurality of traversal orders depending on an adaptive reference vertex of the vertices, and the plurality of traversal orders includes a first order from the adaptive reference vertex to a first alternative reference vertex of the vertices, and wherein the plurality of traversal orders includes a second order from the adaptive reference vertices to a second alternative reference vertex of the vertices.

The invention provides a pipe thread positioning and measuring method and system based on three-dimensional vision, and belongs to the technical field of industrial automation and three-dimensional vision. According to the method, a measuring system composed of a robot measuring system and a three-dimensional visual locating system is built, and hand-eye calibration is completed; the method comprises the following steps: acquiring a three-dimensional point cloud model of the end part of a pipe thread workpiece, and extracting an end surface normal vector, a tangent vector and a circle center coordinate through preprocessing, cylinder fitting, near-end surface point cloud extraction and end surface circle fitting so as to establish a measurement reference coordinate system; and converting the reference coordinate system to a robot base coordinate system based on a hand-eye calibration result and the tail end pose of the robot, generating a robot moving trajectory, guiding the robot to be positioned to a measurement position, and completing preparation before measurement. According to the invention, automatic high-precision extraction of the pipe thread measurement reference is realized, the problems of non-uniform reference, large error and low efficiency in traditional measurement are solved, the method is suitable for various pipe thread workpieces, and a reliable basis is provided for subsequent high-precision measurement.

The invention discloses a three-dimensional vision calibration and positioning method and system based on an event camera, and belongs to the field of positioning. The method comprises the steps of constructing an array containing a plurality of unique frequency coding light sources, constructing a binocular event camera system, collecting light source array light signal event streams, extracting pixel positions, frequencies and timestamp information, determining pixel coordinate centroids of the light sources through frequency and pixel clustering analysis, and establishing frequency and centroid mapping. Auxiliary light sources with the same coding rule are arranged on the surface of a target object, and after a binocular system collects light signals of the auxiliary light sources, the center-of-mass coordinates of the light sources are matched according to the mapping relation. And based on event camera calibration parameters and binocular external parameters, correcting and stereoscopically matching centroid coordinates, and calculating three-dimensional coordinates of the auxiliary light source through triangulation. And in combination with the known geometric topological relation of the auxiliary light source on the surface of the object, the spatial pose of the target object is solved by using a rigid transformation algorithm, and high-precision positioning is completed. According to the embodiment, the calibration precision can be improved in a complex application environment.

The invention relates to the technical field of synchronous positioning and mapping, in particular to a three-dimensional mapping method and system. Extracting feature points of the obtained target map image by using an ORB-SLAM system, and performing three-dimensional visual reconstruction based on the feature points to generate an initial sparse point cloud map; fusing the sparse point cloud data of the target map image acquired by the plurality of sensors to obtain dense point cloud data, and processing the obtained dense point cloud data by using a point cloud segmentation algorithm and a target detection algorithm to obtain target information in the dense point cloud data; and performing spatial segmentation and organization on the dense point cloud data according to the target information in the dense point cloud data by using a tree data structure algorithm to obtain a point cloud map with a hierarchical structure. According to the method, the map data is accurate, and the management of the map data can be efficiently realized.

The invention discloses a three-dimensional visual detection method, system and equipment for industrial application, and relates to the related field of three-dimensional computer vision, and the method comprises the steps: obtaining a three-dimensional point cloud data set of a target product, carrying out the multi-dimensional feature analysis based on the three-dimensional point cloud data set, and constructing a multi-modal feature vector set; performing three-dimensional visual defect detection according to the multi-modal feature vector group to generate a defect detection quality report; and making a control decision instruction according to the defect detection quality report, and automatically controlling the target product through the control decision instruction. The technical problem that existing industrial application-oriented three-dimensional visual detection is insufficient in detection accuracy and comprehensiveness is solved, and the technical effect of improving the detection accuracy and comprehensiveness is achieved.

The invention belongs to the field of building surveying and mapping, and particularly discloses a building three-dimensional defect detection method, device and equipment based on multi-sensor fusion, and the method comprises the steps: obtaining data detected by a laser radar, an IMU, a wheel speed meter and image capturing equipment; fusing the data observed by the laser radar, the IMU and the wheel speedometer based on the ESKF, and determining the speed and the pose of the vehicle-mounted acquisition equipment; obtaining a three-dimensional point cloud model under a world coordinate system by combining the speed, the pose and the point cloud data observed by the laser radar; generating a corresponding three-dimensional Gaussian function according to the three-dimensional point cloud model, projecting the three-dimensional Gaussian function in a world coordinate system to a coordinate system of an image capturing device in combination with a pose, and performing microrasterization to obtain a preliminarily rendered image; and constructing a loss function in combination with a visual image observed by an image capturing device, optimizing the three-dimensional Gaussian function, and iterating to obtain a rendered three-dimensional visual model. According to the invention, fusion positioning and high-precision mapping of the building are realized in combination with multiple sensors.

The invention provides a railway turnout identification method and device, equipment and a medium, and the method comprises the steps: S1, constructing a classification network, obtaining training data, carrying out the customization training of the classification network, and obtaining a trained railway turnout classification model; s2, obtaining turnout rail shape three-dimensional visual point cloud data in real time, and processing the turnout rail shape three-dimensional visual point cloud data through the railway turnout classification model to obtain turnout rail shape classification data; s3, adding the turnout rail shape classification data to a preset sliding window queue, and enabling the preset sliding window queue to keep a preset length; and S4, performing statistical analysis on the sliding window queue to obtain a railway turnout classification result. According to the technical scheme provided by the invention, the accuracy of visual turnout identification in the railway industry can be improved by comprehensively considering the turnout identification algorithm of the left and right rail states in the time window.

A method is provided for generating a three-dimensional (3D) visually representative model of a vehicle. The method includes receiving images acquired from a number of viewpoints of different sections of the vehicle, and performing photogrammetry on the images to extract a profile of the vehicle. The method includes creating a wireframe mesh or point cloud from the profile, and generating the 3D model of the vehicle. The images are processed to determine areas on a surface of the vehicle in which a defect is detected, and markers are appended onto respective areas of the 3D model that correspond to the areas on the surface of the vehicle such that the defect is appended onto the 3D model. And the method includes generating a display of the 3D model of the vehicle including the markers that indicate the areas on the surface of the vehicle in which the defect is detected.

The invention discloses an electronic cabin assembly quality detection method based on three-dimensional vision. The method comprises the following steps: firstly, generating data for comparison during detection for a standard electronic cabin; shooting all to-be-detected objects in the to-be-detected electronic cabin to obtain a depth image and a color image of each to-be-detected object; according to the depth image and the color image of the to-be-detected object, combining with the data of the standard electronic cabin for comparison during detection; and whether screws of the electronic cabin to be detected are neglected and not installed in place, whether cable plugs are neglected and not installed in place, whether connectors and cable plugs in the wiring unit are wrongly matched, and whether the bending radius of cables is too small are detected in sequence. Through fusion of depth information, three-dimensional quantitative detection of the assembly quality is realized, and the detection precision, reliability and automation level are significantly improved.

The invention relates to the technical field of garbage recognition, and particularly discloses an intelligent garbage recognition system and method based on multi-modal fusion, a hardware sensing unit is composed of a three-dimensional vision module, a touch sensing module and a near infrared spectrum module, and point cloud data, a pressure distribution matrix and spectrum data of garbage are obtained respectively; and an edge computing unit is equipped to carry an NPU acceleration chip for data processing. And the software module realizes multi-modal feature fusion by using ResNet-50, LSTM (Long Short Term Memory), 1D-CNN (Convolutional Neural Network) and Transform Encoder, and is also provided with a multi-sensor timestamp synchronous controller. And the dynamic feature database starts online learning to update the material feature library when the garbage recognition confidence coefficient is lower than 85%. During feature fusion, a cross-modal feature mapping relation is established, the weight is adjusted through an attention mechanism, and dynamic learning adopts a model lightweight method based on knowledge distillation. And starting an arbitration mechanism to correct the classification result when different modal identification results conflict. According to the method, the advantages of different modal data are fully utilized, and the recognition capability in a complex environment is improved.

The invention discloses a non-ferrous metal ingot detection method and equipment based on three-dimensional vision. Belongs to the field of nonferrous metal detection. The method comprises the steps of construction of a three-dimensional pixel model with laser projection structured light projection and line laser scanning camera shooting, adaptive filtering preprocessing, establishment of a three-dimensional point cloud model through deep learning, feature information extraction, deep learning algorithm identification and classification, production process problem analysis and optimization and the like in sequence. Therefore, high-precision, multi-angle and all-directional detection of the non-ferrous metal ingot is realized. The equipment comprises a multi-axis collaborative three-dimensional visual inspection subsystem consisting of a mounting bracket, an X-Y-Z servo motion platform, a line laser scanning camera and the like. According to the method, the quality detection precision and defect detection rate of the non-ferrous metal ingot can be effectively improved, detailed product quality analysis is provided, and the method plays an important role in improving the product quality, optimizing the production process and reducing the production cost.

The invention relates to an end-to-end automatic driving planning method based on three-dimensional visual language pre-training, and the method comprises the following steps: obtaining a multi-view bird's-eye view feature X, and inputting the multi-view bird's-eye view feature X into a BEV encoder in a pre-trained BEV former to calculate a global visual feature; integrating the real description of the environment into a BERT structure-based text attention block to obtain a text representation; calculating grouping alignment loss; inputting the global visual features and the prompt features into a language model, outputting word vectors by the language model, and training a generative end-to-end automatic driving planning model pre-trained based on a three-dimensional visual language; and inputting the actual multi-view image into the automatic driving planning model, and outputting an actual automatic driving planning path. Compared with the prior art, the method has the advantages of improving the real-time performance and generalization ability of path planning and the like.

The invention discloses a digital twin manufacturing and testing system for key parts of aerospace equipment, which belongs to the field of intelligent manufacturing and comprises an intelligent manufacturing module, a surface detection module, a nondestructive inspection module and a mechanical detection module. The intelligent manufacturing module digitalizes the manufacturing process by means of a digital twinning technology, so that the processing process state and the subsequent detection result can be mapped conveniently; the surface detection module is mainly used for visual detection, two-dimensional or three-dimensional visual information is used, an artificial intelligence algorithm is combined, and surface machining quality detection is achieved; the nondestructive inspection module is used for detecting internal defects of the parts by virtue of internal defect inspection equipment; the mechanical detection module is mainly used for simulating detection of mechanical characteristics and life cycle of parts under dynamic working conditions of stress, temperature and the like. According to the method, digital mapping of the intelligent manufacturing process, the surface characteristics, the internal characteristics and the mechanical properties is realized by constructing a neural network architecture, so that the aim of gradually replacing physical testing with digital testing is fulfilled.

The invention discloses a method for calculating the volume of a muck pile in a strip mine area based on three-dimensional visual point cloud. The method comprises the following steps: step 1, extracting frames from a video of a whole muck pile shot by an unmanned aerial vehicle to obtain a muck pile picture, and processing the collected picture and video by using colmap to obtain internal reference and pose information of a camera; then, taking the picture and camera internal reference and pose information as input to be transmitted into a neural radiation field model, completing three-dimensional reconstruction of the muck pile, and obtaining point cloud data of the muck pile; step 2, processing the point cloud data of the muck pile to obtain morphological parameters of a longitudinal profile, a volume and a throwing distance of the muck pile; according to the method, three-dimensional reconstruction is performed on the muck pile through the neural radiation field, the muck pile point cloud is generated, and then the muck pile point cloud is processed, so that the muck pile volume and other morphological parameters are obtained quickly and accurately.

The invention provides a three-dimensional vision and infrared thermal imaging combined calibration method and system and a medium, and the method comprises the steps: carrying out the processing of a calibration plate based on a special infrared response material or an electric heating insert, and obtaining a visible light angular point pattern and a calibration plate of a thermal response structure region; shooting an image of the calibration plate based on a three-dimensional vision camera, and extracting image coordinates; acquiring an infrared image of the calibration plate to obtain a thermal image, and analyzing distribution information of thermal characteristic elements on the calibration plate; establishing a nonlinear mapping relationship between the image coordinates and the distribution information of the thermal characteristic elements; analyzing a projection error based on the nonlinear mapping relation, and performing parameter calibration on the three-dimensional vision camera and the infrared thermal imager based on the projection error; the thermal imager and the three-dimensional vision camera are responded at a time through the bimodal calibration plate, a nonlinear injection relation is established, parameter calibration is accurately carried out on the three-dimensional vision camera and the infrared thermal imager according to projection errors, the calibration precision is improved, and the method is suitable for complex environments such as industry and medical treatment.

The invention discloses a method for measuring the navigation pose of a battery replacement robot based on three-dimensional visual features. The method comprises the following steps: 1, acquiring a color image and a depth image of a scene by using a 3D visual sensor; 2, positioning a locking and unlocking area in the color image through a convolutional neural network, and projecting depth data of the area into a point cloud by combining internal parameters of a camera; 3, performing statistical filtering and voxel filtering preprocessing on the initial locking and unlocking point cloud; 4, collecting five key points from the target point cloud by using an FPS sampling method, solving the coordinate of each key point in the target point cloud object coordinate system, and taking the coordinate as a directional vector; 5, points in the scene point cloud are converted into an object coordinate system, candidate points of the key points are screened according to the direction and length of the steering vector, and the key points of the scene point cloud are determined from the candidate points; and 6, fitting the pose of the scene and unlocking the point cloud by adopting a least square method according to the corresponding relation between the two groups of key points of the target point cloud and the scene point cloud, and optimizing the pose estimation result by using an ICP algorithm.

The invention discloses a complex stacking scene robot disordered grabbing method based on three-dimensional vision, and the method comprises the steps: firstly obtaining an original point cloud through a depth camera, and obtaining a high-quality point cloud after statistics and radius filtering, moving least square smoothing, background plane removal and grid simplification; then, hand-eye calibration of participation eyes inside the camera outside the hand is completed, and alignment of vision and a robot coordinate system is achieved; single workpieces are separated through Euclidean clustering; in the key point extraction stage, geometric significance and super voxel density screening are integrated, weighted three-dimensional SIFT and multi-scale SHOT descriptors are adopted for feature expression, and rough registration and symmetric ICP fine registration are combined to obtain the six-degree-of-freedom pose of a workpiece; and based on the self-adaptive curvature and the force closing score, a grabbing posture is optimized, a collision-free path is planned by using an RRT algorithm, and grabbing is executed. The method does not need pre-modeling, can adapt to shielding, symmetry and density change scenes, is low in deployment cost, is simple to maintain, and is suitable for industrial disordered feeding and discharging and logistics sorting.

The invention discloses an unmanned aerial vehicle non-cooperative environment autonomous landing method and system based on three-dimensional visual semantic information, and relates to the technical field of unmanned aerial vehicle landing recognized.The method comprises the steps that during emergency landing, a semantic segmentation model is used for generating a two-dimensional semantic segmentation map and obtaining semantic tags, and a landing area is automatically screened and tracked; fusing vision and inertia information through an airborne binocular camera and an IMU (Inertial Measurement Unit), outputting an accurate pose and depth map, and inputting a map construction algorithm to reconstruct a landform map of a landing area; projecting the semantic tag to a three-dimensional space and fusing the semantic tag with a terrain map to generate an evaluation map; and finally, an optimal landing point is determined by a landing point selection algorithm, and autonomous landing is completed through a flight control system. The method has the characteristics of high precision and fast reasoning, gets rid of dependence on external signals, significantly improves the landing capability under complex terrains, and guarantees the safety and reliability of autonomous landing.

The invention discloses an interaction method and device, a storage medium and equipment. The method comprises the steps of displaying a three-dimensional environment generated by augmented reality equipment; displaying a three-dimensional virtual keyboard in the three-dimensional environment, wherein a plurality of virtual keys are displayed on the virtual keyboard; identifying a stamping gesture of the current object; determining an interaction event between the current object and the virtual keyboard according to a stamp gesture, a stamp gesture collision body corresponding to the stamp gesture and a hovering collision body corresponding to a virtual key in the virtual keyboard; in response to the interaction event, interaction feedback information is output, and the interaction feedback information at least comprises the visual indication. According to the virtual keyboard with the three-dimensional visual effect, interaction feedback information can be output, interaction convenience and input efficiency of the virtual keyboard are improved, and interaction intuition and naturalness are improved.

The invention belongs to the crossing field of three-dimensional scene understanding and multi-modal artificial intelligence, and discloses a self-adaptive three-dimensional large language model system based on query guidance. A query-guided adaptive cutting module and a multi-modal object-level feature enhancement module are provided, the problems of redundant information processing and semantic information insufficiency in a three-dimensional scene are solved respectively, and the method comprises four steps of three-dimensional visual language alignment, query-guided adaptive cutting, multi-modal feature enhancement and language model training optimization. Through joint processing of multi-modal data, the method can realize efficient and accurate three-dimensional scene understanding under the condition of no or less annotation of a data set. Experimental results show that the performance of the method on a plurality of standard data sets is superior to that of the prior art, particularly in three-dimensional questions and answers and scene description tasks, remarkable precision improvement and robustness are shown, and particularly, higher accuracy and interpretability are achieved when complex three-dimensional structures and fine grit problems are processed.

The invention relates to the field of three-dimensional vision measurement, in particular to an aluminum alloy CNC product defect detection method and system based on structured light scanning. The aluminum alloy CNC product defect detection system based on structured light scanning comprises an image acquisition and analysis module, a self-adaptive illumination control module, a point cloud reconstruction module, a defect detection module and a report generation module. According to the method, through texture direction perception and matrix type light source dynamic angle modulation, and in combination with a light intensity-wavelength self-adaptive scheme of saturated pixel grading, mirror surface glare of the aluminum alloy high-reflection surface is greatly reduced; in cooperation with multi-exposure gray scale weighted fusion, a stripe image which is uniform in contrast and free of overexposure can be obtained in single scanning; in the same scanning time, compared with a fixed illumination scheme, the point cloud integrity rate can be improved, and a more reliable three-dimensional data basis is provided for subsequent defect detection.

The invention provides a welding seam identification and trajectory optimization method and system based on three-dimensional vision, and the method specifically comprises the following steps: firstly, collecting the three-dimensional point cloud data of a welding seam, extracting the local geometric features of the welding seam, and carrying out the feature enhancement of the local geometric features, so as to screen out representative effective edge points; and then, collecting multi-source point cloud data of the welding seam area, constructing a multi-scale welding seam feature recognition network, inputting effective edge points obtained by screening into the network, realizing accurate recognition of the welding seam area, and further extracting a linear welding seam area. According to the recognition result, a fitting algorithm is adopted for conducting curve fitting on the weld joint area, and a preliminary welding track is generated; and for the condition that multiple sections of welding seams are intersected, an optimal fitting point searching mechanism is further introduced, the preliminary track is optimized, and the continuity and integrity of a welding path are ensured. And finally, based on the center line of the welding seam, the posture of the welding gun is precisely planned in combination with the dihedral structure model, and therefore high-precision track control and posture adjustment are achieved.

The invention provides a complex heterogeneous molded surface three-dimensional vision measurement method and system, and the method comprises the following steps: (1), extracting part design model features through a semantic segmentation and intelligent recognition method, and building a to-be-measured feature set; (2) a three-dimensional measurement viewpoint planning method based on reinforcement learning is adopted to obtain a viewpoint sequence and a motion path; (3) when the local measurement system reaches a preset viewpoint, a self-adaptive single-pixel imaging feature matching method is adopted for mutual reflection and semitransparent molded surfaces; for other molded surfaces, a parameter adaptive fringe projection three-dimensional measurement method is adopted to obtain a single-viewpoint three-dimensional point cloud; (4) converting the single-viewpoint three-dimensional point cloud into a global coordinate system to realize three-dimensional point cloud splicing; (5) traversing each viewpoint of the viewpoint sequence, and executing the steps (3) and (4) to obtain a multi-viewpoint spliced three-dimensional point cloud; and (6) based on the point cloud error estimation, fusing the multi-view three-dimensional point cloud. The problem of accurate acquisition of the three-dimensional point cloud of the complex heterogeneous molded surface is solved.

The invention discloses an intelligent three-dimensional visual perception method based on a parking space scene, and the method comprises the following steps: S1, responding to the illumination change of a parking space environment, and automatically switching a passive or active visual mode; s2, in a passive vision mode, capturing a natural light image by using a binocular camera, and reconstructing a three-dimensional scene; s3, in the active visual mode, projecting a coding pattern, and reconstructing a three-dimensional scene through pattern change; s4, performing semantic segmentation on different objects in the passive visual mode or the three-dimensional parking space scene image in the passive visual mode, and endowing semantic tags; s5, mapping the target object in the two-dimensional image into the three-dimensional scene, and obtaining the position and posture of the target object; by automatically switching the passive vision mode and the active vision mode, the adaptability and the recognition precision of the parking space scene under different illumination conditions are remarkably improved; in a passive vision mode, the system utilizes natural light to capture images, a binocular camera system and a stereoscopic vision technology to accurately reconstruct a three-dimensional scene.

The invention provides a livestock body size measurement method and system, computer equipment and a storage medium, and belongs to the field of computer three-dimensional visual point clouds, and the method comprises the steps: segmenting automatically collected point cloud data of livestock into a plurality of parts through a three-dimensional point cloud segmentation technology; the segmented point cloud is combined with the physiological features of the livestock to accurately position the key points of body size measurement. In order to improve the key point positioning precision, the invention provides a novel key point detection method which comprises the steps of coordinate normalization, key point cluster acquisition, key point optimization and edge detection. By means of the method, key point deviation and measurement errors possibly occurring in the body size measurement process are effectively reduced. According to the method, non-contact, rapid and accurate livestock body size parameter measurement is achieved, and the method has important application prospects in the fields of gene improvement, health screening, animal welfare evaluation and the like. Experimental results show that the method has high accuracy and robustness, and can significantly improve the efficiency and precision of body size measurement.

The invention, which relates to the technical field of image identification, discloses a three-dimensional visual feedback-based child drop guiding system comprising a sensor module, an image processing and identification module, an abnormality identification module, a three-dimensional reconstruction and tracking module and a child drop guiding module. The sensor module is used for acquiring image data, adjusting an image recognition area according to the image detection data fed back by the image processing and recognition module, and transmitting the acquired image data to the image processing and recognition module; the image processing and recognition module is used for analyzing and processing the acquired image data, including grid line positioning, image data detection and chess piece recognition, and outputting the analyzed and processed image data to the abnormity recognition module and the three-dimensional reconstruction and tracking module; and the abnormity identification module is used for analyzing abnormal data in the image data and transmitting the analyzed abnormal data to the three-dimensional reconstruction and tracking module.