5258 results about "Exposure" patented technology

The invention discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

The invention relates to the technical field of tooth modeling, and discloses a three-dimensional tooth modeling system based on computer vision, computer equipment and a readable storage medium. According to the method, mirror reflection, diffuse reflection and subsurface scattering components in an original image are separated, mirror reflection intensity is normalized in combination with a dynamic truncation algorithm, pixel saturation is eliminated, groove and nest textures are reserved, a complete point cloud is obtained based on a two-dimensional texture image and cubic spline repair, and a multi-exposure point cloud sequence is obtained through bimodal calibration. The method comprises the following steps: solving the problem of data dislocation, carrying out weight assignment and data fusion on three-dimensional points in a plurality of exposure point cloud sequences to obtain three-dimensional fusion feature data, combining layered optical modeling and photon tracking compensation deviation, fusing clinical constraints, finally dynamically adjusting parameters, feeding back and optimizing, and outputting a micron-sized precision model. The modeling defect caused by difficulty in effectively coordinating feature contribution degrees under different exposure conditions is overcome, and high-precision modeling is realized.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention discloses a magnetic material defect detection system based on image recognition, which adopts a first acquisition module, a first determination module, a second determination module, a second acquisition module and a third acquisition module, and is characterized in that the first acquisition module is used for performing multi-angle illumination control on the surface of a magnetic material by adopting a polarized light imaging device; mirror reflection generated by the strong light reflection characteristic is inhibited by adjusting the angle of a polaroid and the incident angle of a light source, original image data with uniform illumination distribution are obtained, and if it is detected that an overexposure area exists in an image, exposure parameters and the polarization angle are automatically adjusted to obtain a high-quality image suitable for follow-up processing; and the first determination module is used for performing multi-scale wavelet transform decomposition according to the acquired high-quality image, identifying a surface defect area by analyzing texture features and edge information in different frequency components, and judging that the surface defect area is a potential defect area if a wavelet coefficient in the high-frequency component exceeds a preset threshold value. The detection performance is obviously improved.

The invention discloses a three-dimensional scene reconstruction method and device based on large model geometric prior, and a medium, and aims to solve the problems that a conventional 3DGS is liable to have artifacts and detail loss in geometric discontinuity, data redundancy and illumination variation scenes, and predicts a dense depth map and a normal map from a monocular image by using a pre-trained large model. The position and form of the Gaussian kernel are constrained as additional geometric priori; a primitive adjustment strategy based on kernel density estimation is introduced in the training stage, small Gaussian primitives with similar structures and adjacent spaces are combined into a large Gaussian primitive, the rendering quality is kept, redundancy is reduced, and the volume of the model is reduced; an exposure coefficient is adaptively estimated for each input image, an exposure compensation image loss function is constructed, and floating artifacts caused by illumination differences at shooting moments are eliminated. Experiments show that compared with the prior art, the method improves the three-dimensional reconstruction precision and real-time rendering quality of complex illumination and less-texture areas in a public data set and an unmanned aerial vehicle aerial photography scene.

The invention provides a processing method and system for reconstructing an LDI sub-pixel image through sparse compression and application, and the method comprises the steps: calculating a low-resolution to-be-exposed image corresponding to each phase structure in advance through a computer image compression algorithm; the low-resolution to-be-exposed images obtained through calculation are loaded to the digital micro-reflector according to the time sequence, and the display moment of each pattern is matched with the rotation position of the corresponding phase structure; coupling the pattern of the digital micro-mirror and the phase encoding mask in a frequency domain through a 4-f optical system, and reconstructing a high-resolution exposure pattern at a sub-pixel level by utilizing a diffraction effect; projecting a high-resolution light field of the reconstructed high-resolution exposure pattern to the surface of the photoresist, and forming a target circuit pattern by accumulating exposure dose; and the micro-nano structure with sub-pixel precision is obtained after development. The system comprises an image compression module, a pattern matching module and a pattern forming module. According to the invention, the manufacturing cost of the laser direct imaging equipment is reduced under the condition of the same precision.

The invention relates to the technical field of optical lens defect detection, and particularly discloses an optical lens laser-induced damage on-line detection method and system, a linear polarizer and a narrow-band filter are connected in series in a detection laser light path, non-target polarized light is filtered through polarization direction matching, meanwhile, interference of the environment and scattered light is inhibited through the narrow-band filter, and the laser-induced damage on-line detection system is obtained. According to the method, external source noise such as ambient light and non-target polarized light is reduced through polarization matching and narrow-band filtering, randomness of speckle noise is offset through multi-angle collection and signal fusion in a visual detection mode, exposure is dynamically adjusted, an image is fused to improve the signal-to-noise ratio, noise and real signals are decoupled through self-supervised learning, and the real-time performance of the system is improved. Dust interference is removed in combination with morphological operation; a deep learning reasoning model enhances the damage identification capability in a noise environment, defocusing blur caused by mechanical vibration is eliminated through phase conjugate correction, and the influence of photoelectric conversion noise, dust interference and the like on the detection efficiency and accuracy is remarkably reduced through multi-link linkage.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

The invention discloses an optical performance detection system and method for a camouflage material, and relates to the technical field of camouflage material detection, and the system comprises a structure identification and illumination control module which is used for collecting a surface image of a camouflage material sample, identifying the surface structure of the sample, starting a full-spectrum light source, and simulating step-by-step illumination from weak light to strong light; carrying out RGB imaging based on the standard illumination; and the region division module is used for applying a superpixel segmentation algorithm to the image, automatically dividing sub-regions, performing hyperspectral reflectivity scanning on each region, synchronously acquiring a light intensity image in a polarization direction, calculating a Stokes vector and constructing a polarization enhanced image. According to the method, frequency domain quantitative analysis of microcosmic interference fringes and accurate mask extraction of polarization abnormal areas can be realized, optical exposure risks of camouflage materials under complex illumination and visual angle conditions are effectively quantified through multi-angle reflection sampling and scoring function modeling, and the detection precision, the area pertinence and the automation level are remarkably improved.

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention discloses a visual combined infrared precast beam appearance defect detection method, and relates to the technical field of civil engineering structure detection and nondestructive testing, and the method comprises the following steps: establishing an illumination time sequence observation layer, collecting multi-dimensional optical parameters aiming at the surface of a precast beam, and generating a dynamic reflection fingerprint baseline; and performing coherent phase decomposition based on the dynamic reflection fingerprint baseline, separating the specular reflection signal from the material texture signal, and calibrating light spot track and intensity evolution data in the unified baseline. According to the method, an illumination time sequence observation layer is constructed to generate reflection fingerprints, coherent phase decomposition and anti-fact playback are combined, reflection interference is stripped, and crack boundaries are recovered; time coordinates are reconstructed through double-mirror-image anchor points, and crack evolution is accurately recovered; phase conjugate projection and light field traction are combined, a dynamic threshold optical fence is established, the exposure rhythm is controlled in a closed-loop mode, and precise detection of the defects of the precast beam is achieved.

The invention discloses an event camera-assisted multi-modal vehicle-mounted three-dimensional occupancy prediction method, which comprises the following steps of: acquiring continuously acquired laser radar point cloud data, images, events and IMU measurement data, performing synchronization of spatial dimensions and time dimensions on various data, and determining the occupancy of a vehicle-mounted vehicle on the basis of the synchronized laser radar point cloud data and IMU measurement data, so as to predict the occupancy of the vehicle-mounted vehicle-mounted vehicle-mounted occupancy of the vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted image. Dense point cloud data is obtained; performing space-time alignment on the dense point cloud, the image and the event, performing semantic annotation on the image after the space-time alignment, performing dynamic and static separation on the dense point cloud after the space-time alignment through cross-modal semantic projection, and performing static scene optimization and dynamic scene fusion to obtain an occupied voxel true value; training an occupancy prediction model based on the laser radar point cloud data and the occupied voxel truth value; and acquiring laser radar point cloud data and image data of a scene and event data in image exposure time, and realizing three-dimensional occupation prediction by using the trained occupation prediction model.

The invention provides a multi-sensor weld penetration state recognition method for improving an attention mechanism, relates to the technical field of weld recognition, and solves the technical problem of inaccurate weld quality judgment caused by insufficient recognition precision of a single signal on a penetration state in the prior art. According to the technical scheme, a sound signal in the welding process and a molten pool image in the welding process are collected; effective sound signals are extracted, and then the sound signals are visualized by using a Gramer angle field method; performing de-exposure processing on the overexposure area through a decoupling polymerization exposure correction model; a channel attention and space attention mechanism is introduced into sound and image feature extraction, deformable convolution self-adaptive sampling is utilized, a double-cross attention fusion strategy is combined to fuse multi-source information, fusion penetration state classification is completed, and therefore the quality of a welding seam is judged. According to the method, the penetration state can be accurately classified through the sound signal extracted in the welding process and the molten pool image, and a certain thought is provided for weld quality detection.

The invention discloses a transformer substation non-uniform exposure image enhancement method and system based on adaptive fusion, and the method comprises the following steps: obtaining non-uniform exposure video frame images in a transformer substation monitoring video, and carrying out the successive preprocessing of the video frame images; processing the brightness channel graph V by adopting a two-branch parallel step; for the H and S parts of the color channel graph, the saturation of the pre-channel graph is enhanced on the basis of the hue difference; combining the enhanced images of the brightness channel and the color channel with an original channel image, respectively designing a self-adaptive channel weight method for fusion, and converting H, S and V channels after fusion into RGB enhanced images for result output; and establishing an evaluation mechanism to evaluate the quality of the enhanced image, and adjusting parameters of an enhancement algorithm according to an evaluation result. According to the invention, the problem of non-uniform image exposure under the complex illumination condition of the substation is effectively solved, and powerful support is provided for accurately monitoring the state of substation equipment.

The invention provides a manufacturing and processing photoetching method and system of a display panel, and relates to the technical field of photoetching processing. According to the method, the display panel substrate is subjected to cleaning, prime coating and partition division to form an imageable micro-mark covering all exposure partitions, and the thickness and the uniformity are measured after lower-layer supporting glue and upper-layer imaging glue are sequentially formed; carrying out in-situ acquisition and modeling by taking the micro-mark as an imaging object, and generating an exposure correction instruction and an overlapping energy table; performing partition exposure and real-time correction by using a digital micromirror, and completing development to obtain an anti-etching window with a steep side wall; and setting an etching window according to the exposure record and the window quality data, and executing low-damage etching and defect recovery to form stability evaluation data. According to the method, comprehensive improvement of high-precision exposure, pattern consistency and electrical stability is realized.

The invention discloses a metal processing detection system and method based on computer vision, and relates to the technical field of defect detection.Rotatable polarization filters are installed in front of a lens of a vision sensor, the polarization filters are arranged at four angles respectively, and four sets of polarization images are collected; calculating the polarization degree of each pixel point on the surface during metal processing by using the gray values of the four groups of polarization images, setting a polarization region interval, and dividing a metal surface reflection region into a specular reflection region and a diffuse reflection region; acquiring images of different wavebands by using a multispectral vision sensor, and constructing a multispectral image set; for the same wave band, the images of different exposure times are synthesized to obtain an HDR high dynamic range image; setting a weight for the HDR image of each wave band, and combining the weighting of the HDR image and the suppression of the polarization degree to carry out multispectral image fusion so as to obtain a final image; and a gray threshold value and an area threshold value are respectively calculated through the average value and the standard deviation, and the metal processing defects are judged by using the two threshold values.

The invention belongs to the technical field of signal synchronization, and discloses a multi-camera anti-shake time sequence synchronization control system and method based on an FPGA. The method comprises the following steps: taking an FPGA as a core, generating an FSYNC signal as a camera exposure reference, and obtaining a delay value through a timestamp and differential measurement; acquiring I MU data and obtaining a global motion vector through a gradient descent algorithm; analyzing by combining the two to obtain phase deviation and feeding back the phase deviation to the FPGA, and adjusting the FSYNC signal by combining the global motion vector by the FPGA according to the phase deviation; according to the method, nanosecond timestamp recording is realized through a homologous clock reference and hardware-level signal binding mechanism, and the limitation of dependence on static time difference compensation in the prior art is broken through in combination with global motion vector analysis and dynamic phase offset adjustment.

The invention relates to the technical field of camera control, and discloses a megapixel camera control method based on multifunctional fusion. The method comprises the following steps: acquiring original image data of an image sensor, and preprocessing to generate an initial brightness distribution diagram; and calculating a scene dynamic range to determine a target exposure parameter set containing exposure time, a gain coefficient and an aperture value, synchronously analyzing color temperature information, matching a preset white balance model to output a correction coefficient, and combining the two to generate a first control instruction to be sent to the image sensor. Receiving a touch screen focusing signal, and extracting a focus area coordinate to adjust a stroke parameter of a focusing motor; ambient light intensity is monitored in real time, and when the ambient light intensity is lower than a preset threshold, a flash lamp compensation module is activated and a compensation light quantity value is calculated. In the photographing mode, the output power of the flash lamp is adjusted according to the compensation light quantity value, the target exposure parameter set is recalibrated based on the focus area coordinates, and the camera complex scene adaptation and imaging quality are improved.

The invention provides an image scene understanding method and system applied to vehicle networking road condition analysis, and the method comprises the steps: firstly obtaining a multi-source road condition image set which is collected by a vehicle networking roadside sensing node in a continuous time period and comprises road scene image sequences in different shooting angles and different exposure modes, and carrying out the time-space consistency calibration processing of the multi-source road condition image set; the method comprises the following steps: generating a standardized image sequence, performing hierarchical feature analysis on the standardized image sequence to form a feature hierarchical chain from a bottom layer to a high layer, inputting the feature hierarchical chain into a pre-training scene semantic understanding model for cross-layer association reasoning, and generating scene semantic description containing road element type labels and dynamic relationships among elements; and finally, generating road condition analysis data containing road condition element positioning information and interaction trend prediction based on the scene semantic description, and transmitting the road condition analysis data to an Internet of Vehicles communication terminal to support driving decision optimization, thereby effectively improving the accuracy and decision support capability of Internet of Vehicles road condition analysis.

The invention relates to an experimental method for manufacturing a through hole by using gray exposure, and aims to realize a through hole structure with a specific inclination angle and depth-to-width ratio through collaborative design of photoetching and etching processes. The method comprises the following steps: determining a through hole angle and a depth-to-width ratio, selecting an etching condition, designing a gray mask structure based on an etching rate selection ratio, implementing gray exposure to obtain a mask pattern, etching, removing residual photoresist and finally forming a target through hole. And by regulating and controlling the selection ratio k of the mask to the substrate material, the inclination angle beta of the through hole and the depth H, designing the gray-scale mask morphology conforming to the target structure. The gray mask can be constructed by adopting gray photoresist or an optical modulation mask, and the inclination morphology parameter, the etching time and the etching rate of the gray mask meet a specific relation, so that high-precision structure control is realized. The method is suitable for the fields of MEMS, micro-optical devices and the like with high requirements on the morphology precision of the through hole, and has the effects of good structure consistency and high process repeatability.

A method for predicting risk exposure includes receiving a particular set of data acquired via a sensor. The method for predicting risk exposure further can include analyzing, via a machine learning (ML) model, the particular set of data. The analyzing can include determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern. The ML model can be trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure also can include predicting a risk exposure for a user based on the particular set of data. The method for predicting risk exposure also can include providing a notice indicating the risk exposure.

The invention discloses a photoetching technology implementation method for multi-chip high-density connection, a chip-level chip interconnection method, a mask plate line manufacturing method and photoetching equipment, and the photoetching technology implementation method comprises the following steps: selecting a first mask plate, and aligning and exposing the first mask plate and a first original pattern unit through a second alignment mark, transferring the first mask pattern to a first exposure area; longitudinally moving the substrate by M times of the longitudinal size of the original pattern for alignment exposure, and transversely moving by 1 time of the transverse size of the original pattern for alignment exposure to complete exposure of other M * 1 chipset exposure areas; a second mask is selected to be aligned with the first original pattern unit for exposure through a second alignment mark, and a second mask pattern is transferred to a second exposure area; and transversely moving the substrate by N times of the transverse size of the original pattern for alignment exposure, and longitudinally moving by 1 time of the longitudinal size of the original pattern for alignment exposure, thereby completing exposure of other 1 * N chipset exposure areas. According to the scheme, high-density interconnection of multiple chips is realized.

The invention discloses an LED chip appearance defect detection method based on image processing, and relates to the technical field of image processing, and the method comprises the following steps: S1, carrying out the multi-angle and multi-wavelength scanning of a defect-free reference LED chip under a stable exposure condition, extracting the dominant frequency vector and direction distribution information of the surface microstructure of the LED chip, and carrying out the detection of the appearance defect of the LED chip; an interference risk map is generated based on the dominant frequency vector and the direction distribution information. According to the method, dynamic adjustable optical imaging parameters and image processing results are deeply coupled, a closed-loop detection mechanism for adaptively inhibiting interference fringes is constructed, and accurate defect and interference distinguishing is realized through an interference risk map, a phase consistency index, discrimination subspace backstepping and optical adjustment, frequency domain notch and spatial domain detail recovery, so that the detection accuracy is improved. And in combination with confidence feedback and encryption sampling, the missing detection and false detection rate is remarkably reduced, and the detection stability and reliability are improved.

An apparatus comprising an infrared (IR) illuminator, an interface and a processor. The IR illuminator may comprise an array of emitters divided into a plurality of segments each configured to generate an amount of an IR light. The interface may be configured to receive pixel data comprising the IR light. The processor may be configured to process the pixel data arranged as video frames, extract IR illumination data for each of a plurality of zones of the video frames, perform a comparison of the IR illumination data in each zone to an exposure threshold, and generate a control signal in response to the comparison. Each zone may correspond to a subsection of the video frames associated with a respective one of the segments. The control signal may be configured to independently adjust the amount of the IR light generated by each of the segments for each of the zones.

The invention relates to the technical field of unmanned aerial vehicle autonomous obstacle avoidance, and discloses an unmanned aerial vehicle obstacle avoidance method fusing vision and laser in a weak light environment, and the method comprises the following steps: obtaining a visible light image, depth information, sparse laser point cloud and real-time illumination intensity; processing the image and the point cloud data, and fusing to generate the position, size and category of the obstacle; based on the obstacle information, constructing a risk assessment model, and assessing a flight situation risk level; according to the risk level, generating an obstacle avoidance behavior sequence including speed, height and scanning adjustment; based on the obstacle avoidance behavior sequence, the flight path of the unmanned aerial vehicle is updated and flight is controlled; during flight, the visual depth and the laser point cloud are utilized to iterate and self-calibrate the external parameters of the sensor online. According to the method, the exposure time and the gain are adaptively adjusted, the sparse point cloud is acquired in combination with the laser radar, and deep processing and fusion are performed on the image and the point cloud data in the multi-scale cross-modal fusion network, so that the perception performance of a single sensor in a weak light environment is effectively overcome.

The invention discloses a light field surface defect identification system and method based on multi-angle scattering, and relates to the field of optical image analysis. The system comprises a multi-angle scattered light field acquisition module and a defect intelligent identification module. The acquisition module obtains a hemispherical space scattered light field image of an optical element to be detected through one-time exposure of a collimation laser light source, a hemispherical dome and the like. The recognition module integrates a data preprocessing unit and an SFD-YOLO network, and on the basis of YOLOv11, the network replaces CBS in a shallow layer with a PSiam attention module to strengthen local significant feature perception, and replaces C3K2 in a deep layer with an OfficitMambaCSP module to strengthen global semantic modeling. The method comprises the steps of image acquisition, data set generation preprocessing, network training and defect identification. The problems that in the prior art, single-view-angle information is limited, and scattering data is insufficient in utilization are solved, Top1ACC reaches 95.6%, the parameter quantity is only 1.25 M, and the method is suitable for high-precision real-time detection of optical element defects.

The invention discloses a combined denoising and demosaicing method for a low-exposure original image under low light, and the method is divided into two stages: in the first stage, introducing a high and low frequency fusion module based on wavelet transform, and generating relatively clean pseudo long exposure data to reduce noise; and in the second stage, through a color space attention subnet, image reconstruction is carried out by using the pseudo long exposure data generated in the first stage, the color is recovered, and texture details are enhanced. In addition, the invention also provides a wavelet loss function to balance detail recovery and noise suppression, and discusses the influence of three different training strategies on the performance of the model. Experimental results show that the method provided by the invention is superior to most advanced algorithms in the aspects of quantitative indexes, color accuracy, detail retention and the like.

The invention discloses a nanosecond particle velocity measurement method based on orthogonal line polarization beam splitting, and belongs to the technical field of particle image velocity measurement. The method comprises the following steps: providing two linear polarization state orthogonal double-pulse lasers, and respectively outputting S polarization pulse light beams and P polarization pulse light beams; the four pulse light beams are combined through the double-path laser beam combining mirror set and output to the sheet light mirror set, and a plane illumination jet flow field with the thickness smaller than or equal to 1 mm is formed; scattered light is separated according to a polarization state by using a polarization beam-splitting cubic prism, and particle images under S polarization and P polarization are respectively captured by two double-exposure cameras which are orthogonally arranged; five kinds of time sequence control are triggered through a multi-channel synchronous controller; processing the view field deviation of the two cameras by adopting a block polynomial alignment method; and calculating a particle velocity field based on a particle tracking velocity measurement algorithm. According to the invention, the polarization debugging error is reduced, the homogeneity error after beam combination is obviously reduced, the coincidence degree of the four beams of planar light is improved, and a high-precision universal alignment scheme is provided for the multi-camera PTV technology.

The invention discloses a hand-eye-force integration-based catenary cantilever robot assembly method, which comprises the following steps of: synthesizing image sequences of different exposures into a high-quality image based on a self-adaptive multi-exposure fusion technology, identifying a workpiece contour in the image based on a deep learning framework, and calculating a pose of an identified part in a camera coordinate system; the two-dimensional camera and the three-dimensional camera are matched to carry out accurate scanning, an accurate pose deviation matrix of an actual workpiece relative to a theoretical model is obtained through a nearest point cloud registration algorithm, and a corrected moving target pose is calculated; and the robot moves towards the position and posture of the moving target, when the robot is about to make contact with the assembly workpiece, the impedance control mode is switched to conduct cantilever installation, and moment data are continuously monitored in the whole process till installation is completed. According to the method, the robot can intelligently cope with uncertainty in the cantilever assembly process, and the assembly success rate and the assembly efficiency are remarkably improved.

A low-illuminance image enhancement method based on virtual exposure and dual fusion, which relates to the technical field of image processing. The method comprises the following steps: step 1, receiving an original image; step 2, performing different levels of exposure on the original image to acquire a plurality of virtual images, and generating a virtual-image sequence; step 3, calculating a weight map for each virtual image in the virtual-image sequence, and obtaining a brightness-enhanced image after fusion; and step 4, extracting detail information from the brightness-enhanced image and fusing same with details of the original image, so as to obtain an enhanced image. The method has a relatively small brightness distortion and contrast distortion, and can effectively preserve visual information of an image itself, making it more compatible with the visual enjoyment of human eyes.