989 results about "Structure light" patented technology

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 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 three-dimensional reconstruction method and system based on polarization fringe projection structured light fusion, and the method comprises the steps: obtaining a light intensity image and a Gray code image, solving the solution phase of the light intensity image, and obtaining the absolute phase of an object; according to the mapping relation between the absolute phase and the three-dimensional space point cloud, three-dimensional coordinates of all pixel points of the object are calculated to obtain a point cloud, and adjacent points in the point cloud are selected for fitting calculation to obtain a normal; calculating according to the light intensity image and the Gray code image to obtain a full-white image, and splitting the full-white image into four images with different polarization angles; solving an azimuth angle and a zenith angle through the four images with different polarization angles, and calculating a target normal according to the azimuth angle and the zenith angle; and calculating an included angle between the normal and the target normal, removing inaccurate points in the point cloud by setting an included angle threshold, and generating a three-dimensional reconstruction result. According to the invention, a three-dimensional reconstruction result with low data redundancy can be generated, and the precision and robustness of three-dimensional reconstruction can be effectively improved.

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 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 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 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 three-dimensional measurement, and discloses a structured light three-dimensional detection system, method and device based on optical phased array coding, the structured light three-dimensional detection system comprises an optical phased array module, an acquisition module and a control module, the control module is connected with the optical phased array module and the acquisition module, and the optical phased array module is connected with the acquisition module and the control module. The optical coding module is used for performing phase regulation and amplitude regulation on the optical signal based on a preset regulation instruction to generate an optical coding pattern, and projecting the optical coding pattern to the surface of the to-be-measured object, so that the to-be-measured object reflects the optical coding pattern to generate reflection coding light; the acquisition module is used for acquiring the reflection coding light to obtain a reflection coding pattern; and the control module is used for generating a preset regulation and control instruction, decoding the reflection coding pattern, extracting a coding value and calculating feature information of the to-be-measured object based on the coding value. The problem that the structure light source cannot be adjusted can be solved, and the flexibility and controllability of three-dimensional detection are improved.

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 belongs to the technical field of stereoscopic vision, and discloses a depth estimation method based on adaptive structured light. The method comprises the following steps: acquiring a camera visual angle RGB image in a scene that a projector is turned off, acquiring a camera visual angle coarse disparity map through a semi-global matching algorithm, generating a projector visual angle RGB image and a projector visual angle coarse disparity map through projection transformation, and inputting the images into an adaptive structured light generation module to generate an adaptive structured light projection pattern; the adaptive structured light projection pattern generates a camera visual angle structured light image through a physical perception differentiable imaging module; the parallax attention depth estimation module receives the camera visual angle RGB image and the camera visual angle structured light image and outputs a depth estimation result; and taking the depth error as a loss function, jointly training and optimizing the adaptive structured light generation module and the parallax attention depth estimation module, and performing depth estimation after optimization. The optimal structured light mode is dynamically generated according to the real-time information of the scene, and the problem that traditional fixed-mode structured light cannot adapt to scene changes is solved.

The invention discloses a lead strain clamp three-dimensional pose identification method and system based on structured light, and the method comprises the steps: obtaining a multi-view structured light image, and carrying out the preprocessing of the structured light image, and obtaining a target structured light image; carrying out point cloud reconstruction according to the target structured light image, then carrying out denoising and abnormal value removal on the reconstructed three-dimensional point cloud, carrying out point cloud segmentation according to geometric features and spatial distribution of the point cloud, and obtaining a point cloud cluster after segmentation; selecting one point cloud in the point cloud cluster as a reference point cloud, and performing point cloud registration on other point clouds and the reference point cloud to obtain a registered point cloud cluster; and for the registered point cloud clusters, fitting a pre-established strain clamp three-dimensional model with the point cloud data by adopting a model-based pose estimation method, and determining a three-dimensional pose estimation result of each strain clamp. According to the invention, the problem of three-dimensional pose measurement of the strain clamp in a complex environment is effectively solved, and high-precision and high-robustness measurement effects are realized.

The invention relates to a plane structured light-based aircraft skin gap and jump detection method, which comprises the following steps of: acquiring a skin opposite seam image and an original skin opposite seam point cloud by using a binocular plane structured light method, and segmenting opposite seam features on the skin opposite seam image based on a light compensation fine-tuning large model deep learning network, mapping the opposite seam features on the skin opposite seam image into the point cloud data to obtain a skin opposite seam pixel region of the image, and then performing error verification; the method comprises the following steps of: firstly, carrying out surface structured light processing on a two-dimensional image, then, carrying out center line fitting and section line extraction to obtain point cloud data distribution of a section line, and finally, carrying out edge chamfer opposite seam gap and order difference analysis and calculation on the obtained point cloud data of the section line. And accurate detection of the opposite seam gap and the jump of the aircraft skin can be rapidly completed. The problems that a traditional method is low in laser detection efficiency, and edge chamfer opposite seam detection is difficult are solved.

The invention relates to the technical field of weld joint alignment tolerance detection of special equipment, spaceflight, bridges, ships, wind power and the like, such as containers, pipelines, boilers, recreation facilities, hoisting machinery, passenger ropeways and the like. The invention discloses an automatic weld joint alignment tolerance detection method fusing point cloud and image segmentation, which can quickly and accurately measure the alignment tolerance of a weld joint by combining binocular stereoscopic vision, AI and point cloud analysis technologies, and make up for the defects of the traditional detection method in efficiency and accuracy. The method comprises the following specific steps: shooting through a binocular structured light camera to obtain a grey-scale image and a point cloud image which are aligned; segmenting a welding seam on the grey-scale map, generating a position area mask according to the segmented welding seam, and dividing the actual object into an upper welding seam area, a welding seam area and a lower welding seam area; and according to the segmented welding seam, a welding seam boundary mask is extracted, the position mask and the boundary mask are mapped to a point cloud picture, and regional point cloud data and boundaries are obtained.

An intraoral scanning system comprises an elongate handheld wand with a probe at a distal end, a structured light projector configured to project a uniform structured light pattern onto an object, a plurality of cameras configured to capture points of the uniform structured light pattern projected onto the object by the structured light projector, and one or more processors. The one or more processors are configured to determine a correspondence between projected points in the uniform structured light pattern generated by the structured light projector and captured points of the uniform structured light pattern captured by the plurality of cameras viewing the uniform structured light pattern projected onto the object, and to use triangulation and the determined correspondence to determine three-dimensional points in space associated with the captured points of the uniform structured light pattern captured by the plurality of cameras.

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 discloses a turntable-based dual confocal image scanning microscope and an imaging method, and relates to the technical field of optical elements, systems, instruments and imaging. The imaging light source assembly, the optical fiber transmission assembly, the light source collimation assembly, the structured light generation assembly, the turntable assembly, the microscopic amplification assembly and the camera detection assembly are arranged along a light path; the structured light generation module realizes structured light illumination on a sample surface by loading a specific mask sequence, super-resolution imaging is realized, pinholes in the turntable assembly are equidistantly arranged according to an Archimedes spiral, and the turntable assembly rotates at a high speed to realize multi-point parallel scanning and de-scanning; the detection module performs detection by using an area array detector, so that the resolution improvement of double diffraction limit is realized; according to the dynamic pinhole array-pixel redistribution method, super-resolution reconstruction is carried out based on an image scanning microscopic system principle, and Stokes shift, optical aberration and other non-ideal conditions can be effectively corrected.

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 belongs to the technical field of image analysis, and particularly relates to an electronic component defect detection method and system based on machine vision, and the method comprises the following steps: S1, obtaining visible light, X-ray and three-dimensional structured light images of an electronic component to be detected, and generating respective multi-scale feature pyramids through independent feature extraction networks; s2, generating a multi-modal fusion feature pyramid; s3, applying a candidate region generation network to propose a candidate defect region; s4, processing the spatial topological relation graph through a graph neural network, and embedding spatial context information into candidate defect region features; and S5, if the minimum distance between the candidate region features and all the pre-stored prototypes is greater than a set discrimination threshold, determining that the candidate region is a potential defect of an unknown type. According to the method, the detection range is widened, the robustness of feature expression is improved, the limitation of insufficient information of a single data source is overcome, and the accuracy, comprehensiveness and perspectiveness of detection are improved.

The invention belongs to the technical field of structured light three-dimensional measurement, and particularly relates to a super-density stripe structured light three-dimensional surface scanning method and system, and the method comprises the steps: obtaining camera parameters; projecting a super-density stripe structure light pattern formed by alternating wide bright and dark stripes to an object; acquiring an image by using a camera, and extracting a central point pixel coordinate of the stripe-structured light pattern by using a sub-pixel precision positioning method; calibrating a light plane equation of each stripe structure light pattern under a camera coordinate system by means of a plane target; reversely projecting the pixel coordinate of the central point of the stripe-structured light pattern into a ray and intersecting the ray with a corresponding light plane to obtain the three-dimensional coordinate of the ray and form an initial three-dimensional point cloud; and finally, carrying out gray scale estimation and filling on missing data caused by signal saturation or difficult positioning based on a radiation brightness physical model to obtain complete and accurate three-dimensional surface shape data. According to the invention, high-precision, high-density and high-integrity three-dimensional surface scanning is realized, and the reliability and applicability of a measurement result are remarkably improved.

The invention discloses a binocular structured light three-dimensional reconstruction and dynamic rotation matrix-based large rotation equipment rotor multistage stacking concentricity accurate measurement method, and belongs to the technical field of non-contact optical measurement, three-dimensional reconstruction and assembly precision measurement. The method comprises the following steps: 1, binocular structured light three-dimensional reconstruction; step 2, dynamic rotation matrix correction: correcting the space attitude of the surface of the spigot, and converting all measured point clouds to a unified coordinate system for concentricity calculation; 3, multi-stage stacking eccentricity error evaluation under the polar coordinate system is carried out, the concentricity is rapidly calculated on the basis of a three-dimensional reconstruction result, and visual distribution of eccentricity errors is formed for subsequent online compensation; and 4, constructing a feed-forward + feedback compensation closed-loop system based on the measurement and error transfer model in the step 3, and correcting the eccentric error in real time in the assembly process. The defects that existing contact measurement is low in efficiency, contact damage exists, and multi-stage assembly accumulative errors cannot be evaluated and compensated in real time are overcome.

The invention discloses a method for regulating and controlling a cosine multi-Gaussian correlation structure light field, and the method comprises the steps: introducing a hyperbolic cosine function and a multi-Gaussian function, and constructing a weighting function and a kernel function based on the hyperbolic cosine function and the multi-Gaussian function; introducing a partially coherent light construction formula, and constructing a cross spectral density function of a cosine multi-Gaussian correlation structure light field at the source plane by combining a weighting function and a kernel function; introducing a free space transmission formula, and constructing a light intensity expression of a cosine multi-Gaussian correlation structure light field at any transmission distance according to a cross spectral density function, namely, an expression of the cosine multi-Gaussian correlation structure light field; adjusting the transmission distance and parameters of the cosine multi-Gaussian correlation structure light field to obtain light intensity distribution regulation and control of the cosine multi-Gaussian correlation structure light field, and further obtaining an array form in which sub-beams are flat-topped beams according to the light intensity distribution regulation and control; according to the invention, controllable transmission of the cosine multi-Gaussian correlation structure light field can be realized, and effective regulation and control of the array form in which the sub-beams are flat-topped beams can be realized.

The invention belongs to the technical field of new view angle synthesis, and discloses a new view angle synthesis method based on structured light and 3D Gaussian sputtering joint optimization. Under the sparse view angle, the new view angle is synthesized through combined optimization based on structured light and 3D Gaussian sputtering, the rendering precision of the model in an indoor scene with a weak texture area is improved, and the performance of new view angle synthesis in a complex scene is improved. Through combination of high-precision depth information provided by a binocular structured light technology and Gaussian distribution optimization of 3D Gaussian sputtering, the method aims to solve the problem of insufficient geometric detail representation of new view angle synthesis under a sparse view angle, so that a more accurate new view angle synthesis result and richer detail representation are realized, and improvement of the precision of new view angle synthesis is promoted.

The invention discloses a precast beam reinforcement cage quality detection method and system based on depth vision. The method comprises the following steps: acquiring a depth map and an RGB map of a reinforcement cage based on a binocular structured light camera; the method comprises the following steps: collecting steel reinforcement framework picture data, preprocessing original data, and completing data labeling; segmenting the labeled data set into a training set, a verification set and a test set; the ResNet-FPN is used as a backbone network of the Mask R-CNN and is used for extracting multi-scale features of the image; an edge detection model is constructed based on ResNet-FPN; taking the rest part of the edge detection model except the backbone network as edge detection branches and integrating the edge detection branches into the original Mask R-CNN model; introducing a feature fusion module into the mask branch, wherein the module combines shape and position features in the edge detection branch; and according to a reinforcement instance segmentation result, calculating the actual diameter of the reinforcement and the distance between adjacent reinforcements, and performing evaluation according to a design standard. The problems that the method is only suitable for a single-layer reinforcing mesh and the calculation efficiency is low are solved.

The invention belongs to the technical field of projection light field regulation and control, and particularly relates to a method and a system for enhancing manufacturability of a projection type structure photomask pattern. Determining an initial mask pattern according to the target structured light field and the magnification of the projection system; calculating an initially generated structured light field by adopting a vector diffraction imaging model, calculating a numerical value of a target function, and judging whether the numerical value meets a preset threshold value or not; if the target function does not meet the preset threshold value, carrying out iterative optimization on the mask pattern by adopting an optimal gradient method until the preset threshold value of the target function is met, and outputting a continuous numerical value mask; pixel splitting or fusion is carried out on the continuous numerical mask according to the required mask manufacturing precision; local integral approximation operation is adopted, binarization processing is carried out on the mask, and the manufacturability of the mask is enhanced. According to the method, the projection type structure light field which is high in degree of freedom, high in efficiency, low in calculation power requirement and robust can be optimized, the mask pattern which can be manufactured can be obtained, and the manufacturing precision of the mask can be adjusted at will so as to adapt to different mask pattern preparation processes.

The invention provides a light beam demodulation detector based on a multi-layer metasurface and a construction method of the light beam demodulation detector based on the multi-layer metasurface, and the light beam demodulation detector comprises an image processing chip and the multi-layer metasurface. The structured light beam to be identified generates a plurality of diffraction beams after passing through the diffraction neural network formed by the multi-layer metasurface; the diffracted light beams generate different light intensity distributions on the upper surface of the image processing chip, so that the image processing chip determines the type of the structured light beams to be recognized based on the light intensity distributions, and demodulation recognition of the light beams is achieved. According to the light beam demodulation detector based on the multi-layer metasurface and the construction method of the light beam demodulation detector, a demodulation component is constructed by utilizing diffraction neural network simulation and the multi-layer metasurface so as to realize demodulation transmission of structured light beams, and recognition of the types of the light beams is realized in combination with an image processing chip; and a compact light beam demodulation detection device with a miniaturized structure is optimally designed.

The invention discloses an infrared photothermal imaging system and method in structured light illumination fluorescence detection, and belongs to the field of super-resolution imaging. The system comprises a structured light generation module, a fluorescence excitation module, a fluorescence collection module and a mid-infrared light thermal module. The structured light generation module generates structured light with adjustable phase and direction; the fluorescence excitation module inputs the structured light to a to-be-analyzed sample and excites the sample to emit fluorescence; meanwhile, infrared pulse laser can be input into a sample by utilizing a mid-infrared photothermal module, and the fluorescence intensity is modulated by utilizing a photothermal effect; finally, fluorescent light emitted by the sample is collected by a fluorescent light collecting module, and an infrared photothermal image in structured light illumination is obtained. The system breaks through the diffraction limit, the resolution ratio is increased by two times at most, the intrinsic infrared spectrum of the sample is obtained through mid-infrared photothermal imaging, and the chemical components and the structure of the sample are reflected. According to the invention, two technologies are combined, so that the intrinsic infrared spectrum information of the sample is provided under the sub-diffraction limit.

A structured light microscopic system relates to the technical field of optical detection equipment and comprises a light source system, a double telecentric projection system, a relay system and an image detection assembly. The light source system is configured to provide structured light for the double telecentric projection system; the double telecentric projection system is configured to receive the structured light output by the light source system and project the structured light to the relay system according to a preset zoom ratio; the relay system is configured to receive the structured light output by the double telecentric projection system, perform relay transmission and output parallel structured light used for irradiating a preset field angle of the sample to be detected; and the image detection assembly is configured to receive a detection light beam formed by the structured light reflected by the sample to be detected and output corresponding imaging information. According to the invention, higher light energy transmission efficiency can be realized, and higher detection resolution can be provided.

The embodiment of the invention discloses a clothing care flatness monitoring method, electronic equipment and fabric cleaning equipment, and belongs to the technical field of clothing care and monitoring. The clothing care flatness monitoring method comprises the following steps: acquiring a multi-angle RGB image and laser projection reflection data; according to the multi-angle RGB image and the laser projection reflection data, obtaining multi-modal fusion data of the surface of the to-be-nursed clothes; determining two-dimensional texture features and three-dimensional deformation features of the surface of the to-be-nursed clothes based on the multi-modal fusion data; determining a three-dimensional wrinkle strength index according to the two-dimensional texture features and the three-dimensional deformation features; and determining the flatness of the to-be-nursed clothes based on the three-dimensional wrinkle strength index. According to the method, the optical image and the laser structured light data are combined, so that the spatial accuracy of wrinkle recognition is remarkably improved, meanwhile, real-time decision support is provided for intelligent nursing equipment, the situation of excessive nursing or insufficient nursing is effectively avoided, and therefore the nursing effect is improved, and meanwhile energy consumption is reduced.

The invention discloses a grabbing control method and system of a 3D sorting machine, and relates to the related field of material sorting, and the grabbing control method comprises the steps that multi-angle image data of a target material is acquired through a binocular structured light camera, three-dimensional reconstruction is carried out, and a material three-dimensional model is obtained; performing matching analysis based on the material three-dimensional model and the material order data to obtain material sorting grid positions and material property information; based on the working characteristic data of the 3D sorting machine, grabbing analysis is conducted on the material three-dimensional model and the material property information, and a target material grabbing point and target material grabbing force are determined; constructing a material grabbing path space; and a grabbing path is planned, a target material grabbing path is generated, and the 3D sorting machine executes material grabbing and compensation closed-loop control. The technical problem that grabbing control of an existing sorting machine is low in control precision and reliability is solved, and the technical effect of improving the grabbing control precision and reliability of the sorting machine is achieved.