3198 results about "Structured light" patented technology

The invention provides a circuit board welding fault identification method and system based on machine vision, and relates to the technical field of visual identification, and the method comprises the steps: obtaining an image of a circuit board welding region through employing a structured light projection system and a multi-camera imaging system; realizing accurate positioning of a defect region in the region of interest by utilizing three-dimensional shape information and through morphological operation and an edge detection algorithm; for the accurately positioned defect area, obtaining high-precision three-dimensional point cloud data of the defect area, extracting a multi-scale curvature feature descriptor, and fusing the multi-scale curvature feature descriptor with visual features to form a multi-feature descriptor; inputting the multi-feature descriptors into a defect classification and recognition module based on ensemble learning to obtain the types and severity of the welding defects; and according to a defect identification result, the three-dimensional space distribution and severity of the welding defects are visually displayed.

According to the PCB production line defect detection system and method based on image processing, a plurality of high-resolution cameras, an infrared thermal imaging camera and a 3D laser scanner are adopted, a dynamically-controlled annular LED light source, polarized light illumination and a structured light projection technology are combined, a foreground and a background are separated through a self-adaptive threshold Otsu method, and the defect detection accuracy is improved. A convolutional neural network denoising model and a frequency domain filtering technology are introduced, welding spot and line features are extracted from a preprocessed image, a coding scheme is designed, multi-modal features are fused, spatial-temporal feature analysis is introduced, fine classification and positioning are performed in combination with SVM preliminary screening and a YOLOv5 deep learning model, an online learning mechanism is introduced, model parameters are dynamically optimized, and a multi-modal image is obtained. An intelligent early warning mechanism is established, automatic grading and alarm sending are carried out, a statistical report is automatically generated, production process parameters are recorded, root cause analysis is carried out in combination with big data analysis, and potential defects are early warned in advance.

The invention discloses a directional detection method and system for a power supply shell based on a multi-source heterogeneous sensor, belongs to the field of material characteristic detection, and aims to solve the problems of single dimension, weak anti-interference performance, data isomerism and poor adaptability in traditional industrial detection. Through systematic innovation of multi-source sensor collaboration, dynamic mode control, cross-modal data fusion and self-learning optimization, a high-precision, high-efficiency and high-reliability solution is provided for the precision manufacturing field, and an optical, infrared and ultrasonic multi-modal collaborative detection framework is constructed; the optical unit realizes accurate capture of surface texture and morphology through polarized light imaging and 3D structured light scanning, the infrared thermal imaging unit analyzes the heat conduction characteristic of a material, and the ultrasonic array analyzes internal structure defects to form a surface-material-internal full-dimension detection capability; and nine types of defects such as scratches, pits, weld marks, bubbles, sink marks, material layering, stress cracking, thermal stress abnormity and material pollution are covered.

The invention provides a cast workpiece three-coordinate structured light rapid measurement system, and relates to the technical field of industrial automatic measurement, and the system comprises a calibration parameter module which is used for calibrating a standard ball, building an initial conversion relation between a structured light coordinate system and a three-coordinate measuring machine world coordinate system, and collecting temperature, humidity and vibration data in real time; dynamically updating the conversion relation to form calibration parameters; and the measurement path planning module is used for dividing the casting workpiece into a plurality of measurement areas based on the calibration parameters, planning a motion path of the three-coordinate measuring machine and driving the motion platform to move to each measurement position. According to the invention, full-process automation from calibration, measurement to modeling analysis of the casting workpiece and the advantage of comprehensive quality analysis are realized, and a reliable three-dimensional measurement and quality control solution is provided for the casting industry.

The invention relates to a concrete working performance measurement method and system based on a multi-modal visual large model, and solves the problem that rapid detection of concrete working performance parameters is troublesome, and the method comprises the steps: based on the spatial semantic understanding capability of the multi-modal visual large model, combining a multi-view stereoscopic vision and structured light scanning technology, and calculating the working performance of concrete; reconstructing a three-dimensional geometric structure of the concrete slurry, extracting morphological characteristic parameters, and forming characteristic vectors; inputting the feature vectors into a pre-trained multi-task neural network, fusing the spatial-temporal features and combining a rheological algorithm to identify various working performance parameters; integrating identification results for at least three times by adopting integrated learning, and verifying parameters based on a fluid dynamics basic equation through a fluid simulation platform; and based on the verification result, generating a mix proportion optimization suggestion containing the material components. The method has the advantages that non-contact rapid measurement of concrete working performance parameters is achieved, precision and efficiency are improved, and mix proportion optimization suggestions are provided.

The invention relates to the technical field of stamping part detection, and discloses a stamping part size and defect synchronous detection method and system. The method comprises the steps that the multi-sensor measurement module is used for collecting line laser scanning morphology data, infrared thermal image strain data and structured light projection contour data of a stamping part; multi-sensor data registration is achieved through a feature point matching algorithm, and a three-dimensional space coordinate mapping relation is generated; calculating the thermal expansion compensation amount of the material in combination with a thermal deformation correction model; filtering the structured light projection contour data, and extracting key contour feature points and defect region boundaries; inputting the related data into a multi-source data fusion model to obtain a dimensional deviation and defect fusion detection result; based on this, a measurement path is updated through a dynamic path planning algorithm, and a synchronous detection scheme is output. The device can synchronously detect the size and defect of the stamping part, improves the detection precision and efficiency, achieves the quality grade classification, and is high in adaptability.

The invention discloses an online monitoring method and system for the light transmittance of an optical lens, and relates to the technical field of precise optical detection. The objective of the invention is to solve the problem of large light transmittance calculation error caused by halo scattering noise interference, low optical path calibration precision of a complex curved lens, a measurement blind area of a large-aperture lens and environmental disturbance of a high-reflection / high-light-transmittance lens in the prior art. According to the scheme, a three-dimensional curved surface model is constructed based on Gray code structured light projection and ICP registration, a dynamic partition scanning instruction set is generated through an improved genetic algorithm, and multi-wavelength polarized light is driven to scan and collect multi-dimensional data; specular reflection and scattering noise are suppressed by combining an improved Stokes vector algorithm and an attention mechanism U-Net network, and the data precision of the complex curved surface is improved by adopting Bayesian optimization and Zernike polynomial weighted fusion; constructing a double-loop coupling optimization architecture to realize closed-loop adaptive optimization; according to the invention, the anti-interference capability, the complex lens adaptability and the full-aperture detection precision are obviously improved.

The invention provides a PCBA circuit board defect detection method, apparatus and device, and a storage medium. The method comprises the steps of obtaining three-dimensional point cloud data of the surface of a circuit board by projecting multispectral composite structured light; performing feature fusion on the multi-modal image of the circuit board according to the three-dimensional point cloud data to generate a fused multi-channel feature map; performing non-rigid registration on the acoustic impedance distribution data and the three-dimensional point cloud data to generate an acousto-optic fusion defect probability distribution diagram; performing multi-scale edge detection on the fused multi-channel feature map to generate an optimized binary edge map; and performing scattering transformation on the optimized binary edge graph, and determining the defect type of the circuit board according to the acousto-optic fusion defect probability distribution graph. Through implementation of the scheme, non-rigid registration is performed by using the acoustic impedance distribution data and the three-dimensional point cloud data, the acousto-optic fusion defect probability distribution diagram is generated, the surface three-dimensional information and the internal acoustic impedance data are combined, internal hidden defects are effectively identified, and comprehensive detection of surface and internal defects is realized.

The invention provides an industrial production part detection method based on machine vision, and the method comprises the steps: collecting multi-dimensional image data through a multispectral industrial camera when a part passes through a detection region, and carrying out the preprocessing of the multi-dimensional image data; constructing a three-dimensional feature space according to the structured light projection, and mapping the preprocessed multispectral image data into the three-dimensional feature space to carry out space registration operation to obtain a to-be-detected image; extracting composite parameters in the to-be-detected image; performing coarse screening identification on the parts through the geometric parameters and the texture parameters, and removing the parts with appearance defects; and carrying out microcosmic fine judgment identification on the roughly screened parts through material characteristic parameters, and rejecting the parts with quality defects. According to the method, the three-dimensional feature space is constructed through fusion of multispectral imaging and structured light projection, and composite analysis of geometry, texture and material characteristic parameters is combined, so that the problem of single detection dimension of a traditional method is solved, and the recognition accuracy and efficiency of complex defects are improved.

The invention discloses a liquid crystal display screen defect detection method, and relates to the technical field of liquid crystal display screen defect detection.A bidirectional polarization transmission structure light source is adopted to project 0-degree and 90-degree polarized light, interference of parasitic reflection on defect detection is reduced, and a self-adaptive exposure control unit is introduced; exposure parameters are adjusted in real time according to the light transmittance and reflection characteristics of the glass substrate, the imaging quality is optimized, and the detection effect is prevented from being affected by overexposure or underexposure; in the image processing stage, parasitic reflection information is extracted by combining a polarization difference algorithm of double-frame subtraction, high-frequency interference is removed by using fast Fourier transform (FFT), an optimized defect image after parasitic reflection suppression is obtained, real defect information is more prominent, and the defect contrast is improved; multi-scale Gaussian fitting filtering, a gradient lifting method and super-resolution reconstruction are adopted to enhance defect features in the image, so that a low-contrast defect area is easier to identify.

The invention relates to the technical field of industrial visual inspection, and particularly discloses a waste metal classification and recognition method and system based on image recognition, and the method comprises the steps: obtaining metal surface visual information through an image collection system, extracting multi-level depth features after preprocessing, and generating a preliminary classification result and confidence evaluation; when the confidence coefficient is insufficient, starting a multi-mode verification mechanism, acquiring element composition data by adopting a laser-induced breakdown spectroscopy technology, and acquiring surface topological characteristics by adopting a structured light three-dimensional scanning technology; matching the element data with a component database to generate a component verification result, and comparing the morphology features with a morphology database to generate a morphology verification result; and finally, three types of results are integrated based on a weighted fusion algorithm to generate a final classification decision, and a sorting mechanism is controlled to complete accurate sorting.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

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 an AI-based neurosurgery auxiliary robot visual positioning system, and relates to the field of visual positioning, which comprises the steps of deploying and initializing structured light scanning equipment and near-infrared imaging equipment, carrying out multi-modal image acquisition on a surgical area, and carrying out standardization processing, space-time alignment and fusion on the acquired image. A multi-modal visual acquisition and preprocessing module, a three-dimensional tissue model construction and registration module, a visual-anatomical feature recognition and extraction module, an AI auxiliary positioning and path optimization module, an intraoperative dynamic perception and feedback control module and a target position confirmation and instruction output module are constructed. According to the method, high-precision identification and dynamic modeling of a brain tissue structure are realized, real-time identification, path planning and position correction can be carried out on a key anatomical structure in an operation process, the precision, the intelligent level and the intra-operation response capability of neurosurgery operation are remarkably improved, the operation risk is effectively reduced, and the positioning reliability and the automatic control efficiency are improved.

The invention belongs to the technical field of image processing and robots, and particularly relates to an image processing method and device and a robot, and the method comprises the steps: obtaining a structured light coding image, a polarization imaging image and a binocular depth image; constructing a multi-dimensional feature vector by fusing decoded contour phase, polarization analysis material characteristics and depth image data; dynamically calibrating the multi-dimensional feature vector according to the ambient light and the pose of the robot, correcting color deviation through an adaptive white balance algorithm, compensating a camera installation error, and obtaining a calibrated descriptor; based on the calibrated descriptor, utilizing an improved Hough transformation and template matching algorithm to identify the object category attitude, and calculating six-degree-of-freedom pose parameters through a triangulation method; and according to the six-degree-of-freedom pose parameters, a collision-free grabbing path is generated in combination with a robot kinematics model and obstacle information. Therefore, the problems of insufficient image analysis capability, single compensation mechanism, lack of path optimization capability and the like in the prior art are solved.

The invention provides a tunnel rock mass dynamic disaster monitoring system based on digital twinning, and relates to the technical field of tunnel rock mass dynamic disaster monitoring based on digital twinning. By integrating a multi-mode sensing network, the system comprises a self-adaptive infrared structured light scanner, a distributed micro-seismic sensing array and a fiber grating strain sensor; comprehensive and real-time monitoring of the rock mass state is realized; meanwhile, an intelligent analysis algorithm based on deep learning is introduced, abnormal conditions are automatically recognized, a rockburst risk comprehensive index is generated, in addition, a physical entity and a virtual model are combined through a dynamic digital twinborn modeling technology, and visual analysis of rock mass state changes is achieved; and finally, an edge-cloud cooperative communication technology is adopted to ensure low delay and reliability of data transmission and ensure that the monitoring system can quickly respond and timely take emergency measures.

The invention discloses a columnar transparent part defect detection method, and relates to the technical field of optical detection and machine vision, and the detection method comprises a detection preparation stage, a detection pose configuration stage, a structured light stripe excitation stage, a machine vision detection stage, a model optimization evaluation stage, and a detection post-processing stage. According to the defect detection method for the columnar transparent part, the comprehensive performance of defect detection of the columnar transparent part is remarkably improved through the synergistic effect of coaxial pose optimization, composite structure light source design and improved YOLOv8 deep learning model; various common surface and subsurface defects in production are covered; the real-time detection requirement of an industrial production line is met through relative movement of the part and the light source and high-speed image acquisition and processing; the system has strong anti-interference capability, and can adapt to detection scenes of different materials, sizes and environmental conditions.

The invention belongs to the field of three-dimensional measurement, and discloses a three-dimensional reconstruction method based on structured light image enhancement, which comprises the following steps of: generating a stripe coding pattern: generating four sine stripe patterns with different phase shift amounts by using MATLAB programming, and projecting the sine stripe patterns to the surface of an object to be measured; calibrating a camera; calibrating a projector; the method comprises the following steps: capturing four phase shift stripe images projected on the surface of an object by using a camera, and preprocessing the stripe images collected by the camera through a GF-CL image enhancement algorithm; acquiring a wrapped phase: extracting wrapped phase distribution through a four-step phase calculation formula; generating a Gray code; carrying out binarization conversion; unwrapping the phase by using a complementary Gray code; and performing three-dimensional reconstruction. According to the invention, the contrast is improved by using an image enhancement algorithm, and noise is effectively removed. In addition, three-dimensional reconstruction is carried out by combining a four-step phase shift method and a complementary Gray code, so that the error of edge hopping in the traditional Gray code reconstruction is eliminated, and no hopping and interruption are ensured when a real phase is obtained.

The invention discloses a plastic effect simulation display method and system based on a 3D simulation technology, and relates to the technical field of medical information, and the method comprises the steps: collecting the facial data of a patient, and constructing a multi-level facial structure initial model; constructing a hybrid deformation model; individual characteristic parameters of a patient are extracted, and an individual biomechanical parameter set is generated through the biomechanical parameter prediction network; applying the personalized biomechanical parameter set to the mixed deformation model; operation input parameters of plastic surgery are received, and the deformation result of each layer of structure is calculated and displayed in real time based on a GPU parallel computing framework; constructing a tissue healing and recovery kinetic model, and generating appearance prediction data at different postoperative time points in combination with the deformation result; and performing three-dimensional visualization processing on the appearance prediction data. According to the method, CT / MRI, 3D structured light scanning and expression dynamic video sequences are creatively integrated, the limitation of traditional single-mode data modeling is broken through, and the soft tissue boundary recognition precision is remarkably improved.

The embodiment of the invention provides a detection device, electronic equipment and a control method thereof, relates to the technical field of data detection, and aims to solve the problem that equipment with a health sign detection function is large in size. The detection device comprises a first circuit board, a light-emitting device, a photoelectric converter, a metal cover plate and a light-transmitting structure. The photoelectric converter and the light-emitting device are arranged on the same side of the first circuit board and are electrically connected with the first circuit board. The metal cover plate covers the first circuit board and is electrically connected with the first circuit board. The metal cover plate is provided with a first light through hole for exposing the light-emitting device and a second light through hole for exposing the photoelectric converter. The light-transmitting structure is filled in the first light-transmitting hole, the second light-transmitting hole and the accommodating cavity, wraps the light-emitting device and the photoelectric converter, and is connected with the light-emitting device, the photoelectric converter, the first circuit board and the metal cover plate to form an integrated structural member, so that the size of the detection device is reduced.

The invention discloses a multi-scale attention aggregation-based target point cloud segmentation method for a battery replacement robot, and the method comprises the steps: 1, collecting an RGB image, a depth image and three-dimensional point cloud data of a target fastener through a binocular structured light depth camera, and carrying out the fusion to generate a FastSeg3D data set; 2, a two-stage preprocessing method is provided, noise points are removed through radius filtering, and background point clusters far away from a target are removed through DBSCAN density clustering; 3, the network encoder uses a local feature aggregation module to extract geometric features, and the calculation complexity is reduced in combination with a random sampling strategy; 4, embedding a multi-scale attention aggregation module into the jump connection of the encoder and the decoder, fusing the features through a channel and a space attention unit, and achieving the self-adaptive weight weighting of the features of each layer of the encoder; and 5, recovering the resolution of the original point cloud by adopting nearest neighbor interpolation up-sampling, outputting a segmentation semantic tag, and obtaining a high-quality point cloud target. According to the method, the operation time of the battery replacement robot is shortened, and the balance problem of large-scale target point cloud segmentation speed and precision is solved.

The invention relates to a workpiece surface defect detection and laser repair path planning method and system based on surface structured light, and the method comprises the steps: obtaining the three-dimensional point cloud data of a workpiece based on the surface structured light, and completing the defect detection; on the basis of a defect detection result, coordinate points of the workpiece in a camera coordinate system of the mechanical arm are determined, point cloud data are updated, and curve reconstruction in the horizontal direction and curve reconstruction in the vertical direction of a repair path are completed; on the basis of the reconstructed curve, inner discrete points located on the same stripe are preprocessed through a gridding algorithm, an execution grid is constructed, and repair path planning is completed; the system comprises a defect detection module, a repair path design module and a repair path execution module, and workpiece surface defect detection and laser repair path planning are completed through a controller execution method. Defects are identified through data alignment, and the detection efficiency is greatly improved; a remodeled execution grid is quickly formed, the planning and execution time of a repair path is shortened, a defect detection result and path planning are integrated, and verification is facilitated.

The invention relates to the technical field of welding seam positioning, and discloses a structured light and line laser fused welding seam thickness grading three-dimensional positioning method and system, and the key point of the technical scheme is that the method comprises the following steps: S1, shooting a workpiece through a structured light camera, and obtaining a workpiece image; s2, based on the workpiece image, through a channel space attention mechanism and a Center Net convolutional neural network, welding seam position coarse identification is carried out, and the welding seam position is determined; s3, according to the identified position of the welding seam, scanning the position of the welding seam through a line laser camera to obtain three-dimensional point cloud data of the welding seam; and S4, based on the three-dimensional point cloud data, through a Marr-Hildreth edge detection algorithm, identifying to obtain three-dimensional coordinates of the welding seam, and rapidly and accurately positioning the three-dimensional data of the welding seam of the workpiece.

The invention discloses a charger shell screw hole rapid positioning method based on three-dimensional point cloud identification, and the method comprises the steps: collecting a polarization structured light three-dimensional point cloud, carrying out the preprocessing, building a workpiece coordinate system, and obtaining a steady point cloud; calculating a homology bar chart in the neighborhood of the point cloud, screening candidate areas according to a threshold value, and generating candidate masks and topology confidence; performing principal component analysis and polar coordinate projection on the candidate region, and outputting a local point cloud; projecting and correcting a local point cloud to obtain a complemented point cloud and implicit field features; solving a hole axis by adopting random sampling consistency, and carrying out Gaussian mixture fitting on an output center and a hole diameter; fusing topology and geometric scores as joint confidence, and judging whether to enter execution or not; and mapping the center and the axis to a robot coordinate system, and compensating and updating a parameter output result during assembly. According to the invention, through three-dimensional point cloud identification and multi-stage geometric topology analysis, rapid and accurate positioning and assembly adaptive correction of the charger shell screw holes are realized.

The invention relates to the technical field of model construction, in particular to a three-dimensional model generation method and system based on a lightweight glass container. The method comprises the following steps: performing information acquisition on a lightweight glass container by using a preset multi-mode scanning sensor, acquiring a multi-view visible light image, depth information and structured light projection data, and constructing a standard multi-view point cloud data set; performing multi-modal fusion based on the standard multi-view point cloud data set to obtain multi-modal fusion data of the glass container; performing thin-wall judgment on the multi-mode fusion data of the glass container, and repairing a thin-wall structure to obtain a high-integrity three-dimensional point cloud model; therefore, through multi-modal data fusion, geometric and optical joint optimization and a multi-scale path evaluation mechanism, the problems of low structure reconstruction precision, large transparent material error and process evaluation deficiency in traditional glass container modeling are solved, and the geometric fidelity, the material reduction degree and the construction whole process controllability of the three-dimensional model are improved.

The invention provides a self-adaptive teaching-free welding robot intelligent gas saving monitoring system, which relates to the technical field of industrial robots, and comprises a three-dimensional sensing module used for identifying the workpiece material category through a multispectral sensor integrated with a welding gun of a welding robot, meanwhile, a stereoscopic vision camera and a line structure light scanning camera are used for obtaining three-dimensional point cloud data of the welding seam in real time; the parameter adjusting module is used for calling a heat conductivity coefficient database based on the three-dimensional point cloud data, and correcting the initial flow of the welding current, the arc voltage and the protective gas in real time; and the matching control module is used for calculating the arc length variable quantity based on the arc voltage, controlling the opening degree of the gas flow valve through a dynamic regulator, realizing dynamic matching of the gas flow and the changed arc length, and generating gas dynamic matching control parameters. Through cooperative operation of self-adaptive attitude compensation and real-time gas consumption monitoring, teaching-free accurate operation in the welding process is achieved, and intelligent control over gas consumption is improved.

The invention provides an intelligent road crack detection method, and belongs to the technical field of electrical digital data processing.The intelligent road crack detection method comprises the steps that firstly, a line structured light scanner is adopted to obtain road surface point cloud data, and attitude compensation processing is conducted in combination with an inertial navigation system; then noise is eliminated through bidirectional moving average filtering, and the depth matrix is converted into an image and segmented; gaussian background modeling is used to screen abnormal points, a connected graph is constructed through a minimum spanning tree algorithm, and pruning is carried out to obtain a crack skeleton; using a feature enhancement function to optimize the crack saliency; and finally, inputting a pre-trained multi-scale pavement damage identification network, and outputting a crack identification result and classification. According to the method, three-dimensional point cloud data, geometric feature analysis and a deep learning technology are fused, the technical problem of poor adaptability of a complex pavement environment is solved, and accurate identification of various crack types is realized.

The invention relates to the technical field of regulating valve monitoring, in particular to a regulating valve opening degree monitoring method and system based on image recognition, and the method comprises the following steps: S1, constructing a composite optical mark: machining annular grooves and longitudinal convex ridges which are distributed in a staggered manner on the surface of a valve rod to form a composite mark belt; s2, anti-interference stereo imaging: collecting visible light and short wave infrared images to generate a fusion point cloud set; s3, dynamic shielding compensation: generating a three-dimensional point cloud model of a complete composite marking band; s4, thermal expansion decoupling calculation: visual displacement errors caused by thermal expansion are eliminated; and S5, time-space domain closed-loop calibration: reversely reckoning the opening value by using the structured light three-dimensional coordinate to carry out cross validation. According to the invention, high-precision, high-robustness and verifiability monitoring of the opening degree of the regulating valve is realized, and the measurement stability and engineering applicability under complex working conditions are effectively improved.

The invention discloses an oral tooth lesion AI auxiliary diagnosis system, which belongs to the field of artificial intelligence and comprises an image acquisition module, a three-dimensional modeling module, a lesion marking module, a dual-channel feature extraction module, a cross-modal diagnosis reasoning module, a lesion evolution trend prediction module and a dynamic risk level generation module. The image acquisition module utilizes multi-frequency structured light and a polarization camera to cooperatively acquire oral images; the three-dimensional modeling module is used for reconstructing an upper and lower jaw three-dimensional structure based on edge constraint splicing point clouds and registering images to form double-view fusion data; the lesion labeling module fuses expert labeling and weak supervision pseudo labels to generate joint labels; the dual-channel module extracts skeleton and texture features; the reasoning module realizes cross-modal semantic coupling through an image-text co-occurrence graph; the evolution prediction module models a lesion change path based on the time reversal causal network; and the risk module outputs a five-level risk and re-injects the embedded vector to strengthen prediction. The beneficial effects are that diagnosis intelligence and clinical decision support level are obviously improved.

The invention relates to the technical field of industrial automatic detection and computer vision, in particular to a complex workpiece high-precision defect detection method based on a dynamic light source and multi-dimensional detection, which comprises the following steps: performing multi-dimensional data acquisition on a complex workpiece by using high-precision laser scanning equipment; working parameters of the workpiece are adjusted according to characteristics of the workpiece and environmental changes, and the image acquisition process is optimized; performing data preprocessing on three-dimensional coordinate data obtained by laser scanning and texture information obtained by structured light projection; respectively extracting geometric features, texture features and depth defect features of the workpiece from the preprocessed data; acquiring sample data from the database, selecting and optimizing a proper deep learning model, training the model by using the sample data, and constructing a defect detection model; and inputting the data subjected to preprocessing and feature extraction into a defect detection model, and detecting the defect degree of the workpiece.