2237 results about "Structured light" patented technology

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 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 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 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 discloses a quality detection method and system for vehicle safety belt production and a storage medium, and relates to the field of manufacturing quality control of automobile passive safety parts. The method aims at solving the problems that an existing detection means is weak in tiny defect recognition capacity, insufficient in defect evolution modeling and lack of intelligent feedback regulation and control. According to the method, multiple detection means such as medium-wave infrared thermal imaging, laser confocal microscopy, structured light three-dimensional reconstruction and capacitance sensing are fused, and multi-mode defect sensing of key parts such as a braid, an insertion area and an adhesive layer is achieved; deducing a defect evolution process and generating a high-credibility pseudo label by constructing a thermal-stress-rheology coupling finite element model; the graph neural network and the AI model are combined to complete defect type identification, risk quantification and grading, and automatic optimization of process parameters and production line control response are realized based on the quality grade, so that the detection precision, the response efficiency and the product yield are remarkably improved.

The invention discloses an unmanned aerial vehicle assisted three-dimensional scanning well measurement system and method. The method comprises the steps of firstly collecting shaft information, adjusting equipment, planning a measurement path, enabling an unmanned aerial vehicle to collect data according to the planned path, then preprocessing the data, intelligently identifying and classifying the data, constructing a three-dimensional model, analyzing errors, evaluating the data, and finally generating a measurement report. The system comprises an unmanned aerial vehicle, a laser radar, a structured light camera, a depth camera, an IMU + UWB and a cloud processor. Compared with a traditional measurement mode, the method has obvious advantages. Through laser radar SLAM + UWB positioning, the unmanned aerial vehicle can be accurately navigated, high-precision scanning and image acquisition are realized in combination with multi-sensor fusion, and measurement blind areas are reduced. The intelligent identification and classification technology can automatically detect and classify hoistway facilities, detect wall damage and support dynamic obstacle avoidance. Various algorithms are adopted for data processing to improve the model precision, the finally generated high-precision model can be directly used for various engineering scenes, and an efficient and intelligent solution is provided for well measurement.

The invention discloses a high-precision image detection method for a micro-drill blade surface based on deep learning, and the method comprises the following steps: S1, collecting a visible light image and a structured light image of the micro-drill blade surface, and completing the image preprocessing; s2, performing spatial alignment on the image, executing cross-modal fusion, and generating a feature fusion tensor; s3, inputting the feature fusion tensor into a multi-scale residual backbone network, and extracting a hierarchical semantic feature set; s4, inputting the semantic feature set into three task branches of defect detection, region segmentation and type classification, and outputting a corresponding prediction result; s5, calculating a multi-task loss function, dynamically adjusting task branch weights, and optimizing a feature sharing structure; and S6, generating a detection report according to a prediction result, and outputting defect coordinates, a boundary contour, a type label and a confidence value. According to the method, multi-modal fusion, high-precision identification and structured output of the micro-drill blade surface are realized, and the accuracy, efficiency and automation level of defect detection are remarkably improved.

The invention discloses a detection system and method for packaging box production and processing, and belongs to the technical field of packaging production quality detection. The invention discloses a detection system and method for packaging box production and processing. The system comprises a dual-polarization-angle imaging module, a structured light three-dimensional reconstruction module, a temperature compensation module, a defect judgment module and a sorting execution module. According to the method, dual-polarization images and three-dimensional point clouds are synchronously collected, qualified standard values are dynamically calibrated in combination with temperature, accurate recognition and grading judgment of surface and internal defects of the transparent plastic packaging boxes are achieved, and finally unqualified products are removed through a high-speed sorting mechanism. The method solves the problem of misjudgment caused by reflective interference and temperature drift of the transparent material, and is suitable for online detection of a high-speed production line.

The invention discloses a wide-range weak feature scene measurement system based on multi-sensor fusion and structured light scanning, and relates to the technical field of wide-range scene scanning, in the system, an extended Kalman filtering algorithm is adopted, fusion is carried out based on positioning data provided by an inertial measurement module and a positioning sensing module, and a wide-range weak feature scene is obtained. And the distance measurement information provided by the laser distance measurement module is combined to correct the fused positioning data, so that the precision and robustness of subsequent point cloud registration are ensured. Then, according to position information and attitude information of the binocular structured light scanning camera during scene scanning, calculating a relative rotation matrix and a relative displacement matrix of each frame of scanning point cloud data relative to the first frame of scanning point cloud data, and performing coarse registration; in the coarse registration process, all frames of point clouds can be converted into a unified coordinate system by directly utilizing global pose information, and the efficiency of coarse registration is greatly improved. And finally, extracting laser marking point clouds projected by the non-contact laser marking device in each frame of scanning point cloud data, and performing iterative precise registration on each frame of scanning point cloud data by using color features and geometric distribution rules of the extracted laser marking point clouds. In conclusion, high-precision, high-efficiency and high-robustness point cloud registration in a large-scale weak feature scene is realized, and the method can be used for realizing high-precision non-contact measurement.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention provides a spatial heterogeneous vision positioning system and positioning method of a pineapple picking robot, relates to the technical field of picking robot positioning, and realizes high-precision spatial positioning and attitude estimation of the robot in a complex terrain environment by comprehensively utilizing multi-source information such as vision, inertial measurement, multispectrum and structured light. The positioning stability and the real-time performance are obviously improved; by embedding a multi-modal deep learning model, efficient non-destructive detection and intelligent classification of internal defects of pineapple fruits are realized, a mechanical arm is supported to dynamically adjust a picking strategy, fruits with better quality are preferentially selected for picking, and the picking quality and efficiency are improved; and meanwhile, the preset three-dimensional terrain model and the heuristic search algorithm are combined, the motion path of the robot is effectively planned, it is guaranteed that the robot safely and stably moves in the complex environment, and the mechanical damage risk is reduced.

The invention provides an oral surgery positioning and navigation method and system and a storage medium, and the method comprises the steps: calibrating an implanted mobile phone to obtain a unit vector of a rotating shaft of a drill bit and a three-dimensional coordinate value of the tip of the drill bit under an optical three-dimensional coordinate system, scanning to obtain a CT three-dimensional model of a patient, and segmenting to obtain a tooth CT model and an alveolar bone CT model; generating a tooth three-dimensional point cloud according to the real-time pattern of the speckle structured light on the teeth of the patient, acquiring a matrix transformation relation between a CT three-dimensional coordinate system and an optical three-dimensional coordinate system, and performing real-time navigation on the implanted mobile phone according to the real-time point position deviation and the real-time angle deviation of the drill bit. According to the technical scheme, the matrix transformation relation between the CT three-dimensional coordinate system and the optical three-dimensional coordinate system is calculated through the tooth CT feature points and the tooth point cloud feature points, and auxiliary navigation is carried out by comparing the target position of the implant with the real-time pose of the tip of the drill bit, the direction vector of the planting path and the unit vector of the rotating shaft of the drill bit. Operation is convenient and positioning precision is high.

The invention relates to the technical field of oral medical treatment, in particular to an oral disease analysis method and system based on AI intelligent recognition. Comprising the following steps that oral cavity three-dimensional surface data are obtained through an oral scanning module integrated with a dental chair, and the oral scanning module adopts a structured light coding and multi-view fusion algorithm; an oral soft tissue high-definition image is collected through an endoscope module, and an AI analysis unit is arranged in an endoscope to recognize mucous membrane lesion features in real time; calling CBCT image data of the patient, and aligning a three-dimensional image with oral scanning data through a spatial registration algorithm; the mouth scanning data, the endoscope image and the CBCT data are subjected to multi-modal fusion by adopting a dynamic weight fusion mechanism. According to the scheme, the problem of accurate registration of cross-modal data, the lack of a dynamic quality evaluation mechanism of heterogeneous data and the real-time collaboration bottleneck of dispersed equipment can be solved.

The invention discloses an additive manufacturing intelligent repairing and surface optimizing method for a complex special-shaped curved surface, and particularly relates to the technical field of additive manufacturing. A three-dimensional digital model is obtained, and a complex special-shaped curved surface defect area is recognized; extracting a local morphology parameter set; constructing a repair path generation model based on a graph neural network, and performing collaborative optimization of forming precision, residual stress and deposition stability on the path through a multi-objective optimization algorithm; additive manufacturing equipment is controlled to perform material deposition according to the optimized path; carrying out online error detection by adopting a structured light system, judging repair quality through a fuzzy decision tree model, and triggering path reconstruction if the repair quality exceeds a threshold value; after repairing is completed, surface micro texture factors are extracted, and functional texture regulation and control are conducted through the laser micromachining technology; according to the method, high-precision intelligent repair of the complex curved surface area and collaborative improvement of surface function performance are achieved, and the method is suitable for the fields of aerospace, energy, high-end equipment manufacturing and the like.

The invention relates to the technical field of detection, in particular to a complex shell detection method and system based on structured light scanning. The method comprises the following steps: calculating a wrapped phase value and a phase modulation amplitude of each pixel point under each frequency, calculating discrete probability distribution containing candidate stripe series and a corresponding probability for each pixel point based on a multi-frequency wrapped phase difference, generating an initial pixel confidence map, and constructing an energy function; the penalty weight of the smooth item is determined according to the phase modulation amplitude and the wrapped phase gradient local variance, obtaining an initial fringe order distribution diagram, updating discrete probability distribution by using an initial pixel confidence map, re-minimizing an energy function to obtain a corrected fringe order distribution diagram, and reconstructing a final three-dimensional shape. And iteratively optimizing a projector principal point and a radial distortion coefficient in system calibration parameters. According to the scheme, the geometric details of the surface can be reserved, and the processing effect and the measurement precision of the complex curved surface and the high-curvature area are improved.

The invention discloses a horizontal five-axis turning and milling composite center with a self-adaptive matching system. The horizontal five-axis turning and milling composite center comprises a tool turret, a boring tool mechanism, a tool changing area and a control system. A radio frequency identification reading device is arranged in a tool changing area of the tool turret and the boring tool mechanism, and an RFID chip tag is attached to the outer surface of each tool and used for storing tool serial numbers, specifications and length compensation parameters. And the control system realizes self-adaptive matching and sequential error correction of the cutter and the working procedure through radio frequency identification. The tool changing area is provided with a visual monitoring assembly which comprises a plurality of sets of industrial cameras, an adjustable light source and a structured light stripe illumination module and is used for detecting the clamping posture, the abrasion state and the type of a tool. The system has self-learning and digital twinning simulation functions, and can simulate the motion trail of the cutter and predict the interference risk before the cutter is changed. According to the method, integrated control over automatic tool recognition, posture correction, abrasion detection and path prediction is achieved, and the tool changing precision, the operation stability and the automation level of the horizontal five-axis turning and milling composite center are remarkably improved.

The invention relates to the technical field of defect diagnosis, in particular to a production defect diagnosis method and system for a quartz crystal resonator. The method comprises the following steps: extracting a micro-acoustic response signal of each processing stage based on an ultrasonic excitation and response monitoring device, and carrying out acoustic time-frequency structure analysis and response track trend analysis and extraction to obtain a multi-stage crystal voiceprint behavior track sequence; deploying a multi-angle structured light interferometer array to carry out acoustic monitoring and synchronous visual acquisition, carrying out light stripe form response deviation calculation, and extracting a light stripe form response deviation section; and sound-light behavior frequency response analysis is carried out on the multi-stage crystal voiceprint behavior track sequence and the light texture form response deviation section, acousto-optic mismatch index fitting is carried out, and an acousto-optic fusion feature deviation database is constructed. Through accurate and efficient defect diagnosis and positioning, the production yield of the quartz crystal resonator is improved, and waste and energy consumption of a production line are reduced.

The invention discloses a detection method and device for intelligent visual detection of parts and a storage medium, belongs to the technical field of industrial quality detection, and aims to solve the problems that surface and internal defects of complex parts are difficult to identify synchronously and the identification precision is low. The method comprises the following steps: acquiring multi-modal image data obtained through structured light three-dimensional imaging and laser ultrasonic scanning; performing geometric registration and scale normalization on the image to generate a fused image; carrying out image preprocessing and edge analysis, and extracting a region of interest; dividing the candidate region of interest into a plurality of image blocks, and inputting the image blocks into an anomaly detection network; calculating a reconstruction error between the original image block and the reconstructed image block, and generating an abnormal scoring graph; and finally, extracting a defect area through image post-processing, and outputting information such as a defect type, a spatial position, a geometric dimension and a severity level. According to the method, the robustness and accuracy of multi-modal defect identification are improved, and the method is suitable for an online visual inspection task of an industrial production line.

The invention provides an eccentric butterfly valve performance detection method and system based on optical detection. The method comprises the steps that a first image of light transmission intensity distribution of a sealing pair structure of a bidirectional all-metal hard sealing four-eccentric butterfly valve is obtained; acquiring a reflection interference image of the structured light on the sealing pair structure according to the phase modulation structure grating light field, and performing conjoint analysis in combination with the first image to generate a contact pressure distribution matrix; based on the first image and the contact pressure distribution matrix, extracting corresponding multi-dimensional image features to generate a leakage early warning area map; space comparison is carried out on the leakage early warning area map and the floating response track of the sealing pair structure in the opening and closing process, and the corresponding floating response characteristics of the leakage early warning area in the opening and closing process are determined; and generating a butterfly valve sealing performance detection result according to the abnormal degree of the floating response characteristics. Through the implementation of the scheme, the leakage early warning area and the corresponding dynamic abnormal characteristics can be identified in advance, and the safety and the operation stability of the system are improved.

The invention discloses a multi-modal face recognition method and system based on deep learning, and relates to the technical field of face recognition, 3D structured light, near-infrared images and PPG blood flow signals are synchronously fused, occlusion completion is achieved through a double-branch generation network, PPG spectrum constraints generate blood vessel distribution, and biological feature authenticity is ensured; low-confidence sub-regions are divided based on a structural similarity algorithm, the weight of a loss function is dynamically adjusted, only problem regions are iteratively generated, and calculation redundancy is reduced; the depth feature of the complemented image and the blood vessel frequency spectrum main frequency band overlapping rate are extracted, the identity authenticity is doubly verified, and 3D mask and photo attacks are resisted; the performance bottleneck of traditional single-mode identification in a shielding scene is broken through, closed-loop optimization is driven through data, precision, efficiency and safety are considered, and the method is particularly suitable for high-requirement scenes such as medical treatment and finance.

The invention relates to the technical field of three-dimensional model design of molds, and provides a mold three-dimensional high-precision modeling method and system based on multi-modal data fusion, and the method comprises the steps: collecting an RGB image, a high dynamic range image and a structured light depth image of an entity mold, and carrying out the reconstruction to obtain a globally consistent dense point cloud; denoising and semantic segmentation are carried out on the point cloud, and a cavity function area, a core function area and a runner function area are recognized; performing differential parametric modeling based on an identification result: performing geometric primitive fitting on regular geometric features, performing NURBS curved surface reconstruction on a free-form surface, and performing swept volume or rotator fitting on a flow channel to obtain a parameterized geometry, a parameterized curved surface and a parameterized channel model; and applying boundary continuity constraint to fuse the parameterized geometry, the parameterized curved surface and the parameterized channel model, and constructing a hybrid parameterized three-dimensional model. According to the method, rapid reconstruction of a high-precision, high-semantic and full-parameterized three-dimensional model of an entity mold can be realized.

The invention discloses a straight gear shape-free measurement method based on single-group line structured light. The method comprises the following steps: constructing an optical platform coordinate system, carrying out line structured light coordinate system calibration through a spherical calibration body, and calibrating the rotation center of a precision turntable; installing a measured straight gear, establishing a straight gear coordinate system, and adjusting the position of the linear structure optical measuring head, so that the measuring field of view of the linear structure optical measuring head covers a root circle to an addendum circle of the straight gear; the straight gear is installed on a precision rotary table, the straight gear is driven to rotate at a constant speed, a linear structure optical probe scans and collects straight gear contour point cloud data and circular grating angle information, and tooth surface three-dimensional point cloud data is obtained; based on the three-dimensional point cloud data of the tooth surface, extracting an addendum circle, a dedendum circle and an involute tooth profile, and calculating spur gear parameters; performing reverse three-dimensional shape modification tooth surface identification on the shape modification straight gear to generate a three-dimensional deviation diagram; and outputting a measurement result and printing a report. The problems of shape-free rapid measurement, modification tooth surface identification and tooth surface three-dimensional deviation characterization of the straight gear with unknown basic parameters are solved.

The invention discloses a high-reflective surface defect detection method based on structured light and phase deflection, which belongs to the technical field of visual inspection, and comprises the following steps: generating a multi-frequency sinusoidal grating pattern by using a structured light module, and projecting the multi-frequency sinusoidal grating pattern to the surface of a detected object; multi-frequency phase shift coding is introduced, an absolute phase is calculated through a pilot frequency unwrapping method, a depth map obtained by the structured light is calculated, and a structured light point cloud is reconstructed; a unit normal vector is calculated, a Poisson reconstruction model is adopted to obtain a depth map obtained by phase deflection, and deflection point cloud is obtained; the structured light point cloud data and the deflection point cloud data are registered through an iterative nearest point algorithm and then fused to form a fused depth map; and determining a defect area by adopting the maximum principal curvature and the minimum principal curvature combined mask. By means of the mode, the limitation of a traditional three-dimensional measurement means in high-reflection and complex-curvature surface defect recognition is solved, and submicron-level three-dimensional deformation detection precision is achieved.

The invention provides a high-dynamic deformation measurement system and method based on laser section contour differential scanning. The measurement system is composed of a section contour measurement unit, a calibration unit, an error compensation unit and a data processing unit, wherein the section contour measurement unit comprises a multi-sight-line structured light sensor module, a speed sensing module and a distance measurement module; the calibration unit comprises a synchronous control module, an adjustment module and a common tooth-shaped calibration target; the error compensation unit comprises a vibration detection module, a posture detection module and a temperature detection module; the data processing unit is a device with programming and computing functions. The measuring method comprises the following steps: (1) calibrating a laser plane and an image plane; (2) unifying a global coordinate system; (3) obtaining point cloud data; (4) error compensation; (5) constructing a deformation time-history model; and (6) carrying out maximum deformation global optimization. According to the measurement system and method, accurate expression and maximum value global optimization of a pavement micro-deformation three-dimensional space under the load effect can be realized.

The system comprises a projection module, an imaging module and a phase resolving module, the projection module comprises at least three light source units and light modulation units corresponding to the light source units, an emergent light path of at least one light source unit is provided with an optical coupling assembly, and the emergent light path of at least one light source unit is provided with an optical coupling assembly. The light modulation unit is used for carrying out gradient phase modulation on a light beam, and the optical coupling assembly couples a light path modulated by the light modulation unit to a coaxial light path of the imaging module; the imaging module is arranged on a path sharing the same optical axis with the optical coupling assembly and is used for acquiring a stripe image modulated on the surface of the measured object; the phase resolving module is used for solving a wrapping phase of the fringe image output by the imaging module and unwrapping the wrapping phase based on a quality image guided space phase unwrapping algorithm; while the high-frequency sine fringe generation capability is ensured, collaborative optimization of phase distribution and light intensity characteristics is realized, and the contradiction between device miniaturization and optical performance is effectively reconciled.

The invention relates to the technical field of quality detection, in particular to an AI-based liquid crystal glass plate defect detection method and system, comprising multi-modal data acquisition, adaptive preprocessing, dynamic intelligent detection, defect cognition decision, closed-loop feedback execution, a safety guardrail mechanism and digital twin support. Compared with the prior art which adopts a single imaging mode for surface detection and has the defects that the omission ratio of tiny flaws is high and metal and PR defects are difficult to distinguish, the scheme innovatively introduces a multi-mode collaborative acquisition mechanism, and the detection accuracy is improved through millisecond-level synchronization and space registration of four sets of bright field, dark field, ultraviolet and structured light imaging systems. A comprehensive data cube containing textures, metal features, fluorescence response and three-dimensional morphology is constructed, full-dimensional capture and accurate characterization of micron-sized flaws are achieved, and the detection rate and classification accuracy of complex flaws are remarkably improved.

The invention relates to the technical field of workpiece detection, and discloses an intelligent visual detection method and system for the surface of a workpiece, and the method comprises the steps: projecting a polarization structure light pattern to the surface of the workpiece, and synchronously collecting an image of the polarization structure light pattern after deformation on the surface of the workpiece; separating a structured light component and a polarized light component from the image, reconstructing a surface three-dimensional shape according to the structured light component, and generating surface polarization degree distribution according to the polarized light component; performing multi-scale gradient scanning on the surface three-dimensional morphology, extracting edge change features, performing texture consistency analysis on the surface polarization degree distribution, and identifying an abnormal texture mode; identifying a mirror reflection area based on the surface polarization degree distribution, performing height compensation on a corresponding area in the surface three-dimensional morphology, generating a corrected surface three-dimensional morphology, and combining edge change features and an abnormal texture mode; according to the invention, the accuracy and robustness of defect identification can be improved, and the reliability and efficiency of the system in industrial field application are enhanced.