858 results about "Distortion correction" patented technology

The invention relates to the technical field of visual positioning, and provides a visual positioning method and system for precise connector assembly. Performing multi-mode HDR fusion and distortion correction on the original connector image to obtain a multi-layer fusion image matrix; and performing feature region coarse positioning in combination with a composite convolutional neural network to obtain a connector coordinate set, performing geometric feature fine positioning according to the connector coordinate set and the multilayer fusion image matrix to obtain a key point coordinate set, and performing hand-eye calibration and dynamic compensation on the key point coordinate set to generate a pose instruction. And performing vision-force control hybrid assembly processing on the pose instruction according to the sensor feedback data to obtain assembly completion state data. Through multi-modal image fusion, deep learning coarse positioning, precise geometric registration and dynamic cooperation of vision and force control, the speed, precision and stability of precise connector assembly are improved.

The invention relates to the technical field of panoramic image construction, in particular to a panoramic image reconstruction method and system based on multi-angle imaging. The method comprises the following steps: collecting a multi-angle original image based on a distributed multi-camera array, carrying out adaptive filtering denoising and adaptive panoramic imaging adjustment, and constructing a multi-angle imaging geometric constraint network; performing multi-view semantic information deviation elimination based on a multi-angle imaging geometric constraint network, and performing global semantic feature fusion to obtain a unified semantic space representation framework; identifying illumination feature information of different visual angles, performing multi-angle illumination corresponding compensation on the multi-angle original image, performing image semantic distortion correction based on a unified semantic space representation framework, and constructing a multi-angle illumination compensation image; and performing multi-scale texture structure analysis on the multi-angle illumination compensation image to generate a high-fidelity texture fusion image. According to the invention, a natural and seamless panoramic image is provided, a panoramic scene is perfectly presented, and the immersive visual experience of a user is improved.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention discloses a field personnel positioning method cooperating with multi-view images and spatial vectors of an unmanned aerial vehicle, and the method comprises the steps: detecting a personnel target in real time through a pre-trained lightweight model, obtaining a surrounding radius in combination with a self-adaptive surrounding radius algorithm, and achieving the collection of a multi-view image sequence; carrying out quality quantitative analysis on the multi-view image sequence by adopting a multi-source fixed weight quality evaluation algorithm; a DBOF coordinate system is established, and coordinate conversion is completed; extracting high-frequency matching points based on a cross-image bidirectional matching mechanism, and performing composite distortion correction on the high-frequency matching points; carrying out weighted least square method solution on the multi-view observation equation set to generate a three-dimensional space coordinate of the target point in a DBOF coordinate system; and introducing a robust iterative optimization strategy to eliminate abnormal values, and outputting geographic coordinates. On the basis of low-cost monocular vision, the real-time performance and robustness of personnel positioning in a complex field environment are improved through a dynamic surround sampling and geometric resolving mechanism, and spatial data support is provided for field rescue.

The invention discloses a visible light and infrared fusion target detection method and system for an all-time unmanned aerial vehicle scene, and belongs to the technical field of unmanned aerial vehicle aviation and intelligent image processing. The method comprises the following steps: analyzing an original visible light image to calculate average brightness and root-mean-square contrast, and quantifying scene illumination conditions; distortion correction and registration are carried out on the original image; a parallel backbone network is adopted to extract visible light and infrared twinborn feature maps, the number of channels allocated to two modal features is dynamically adjusted through 1 * 1 convolution according to illumination condition parameters, and then a dual-light fusion network containing a gated convolution block is utilized to perform weighted fusion; and target prediction is carried out through the progressive feature pyramid network. Through an illumination adaptive fusion strategy and targeted data enhancement, the precision and robustness of target detection in a complex all-day scene are significantly improved, and the method is suitable for real-time target detection tasks of the unmanned aerial vehicle.

The invention relates to the technical field of artificial intelligence and computer vision, discloses a subway key component fault detection method and system based on an AI visual large model, and aims to solve the problems of low detection precision, weak generalization ability, insufficient multi-mode understanding, poor real-time performance and lack of state evolution modeling in the prior art. The method comprises the following steps: acquiring images of key components through a multi-view industrial camera array, and performing distortion correction, illumination normalization and noise suppression; a pre-trained visual large model is utilized to extract deep space features, and modeling is carried out on a continuous frame feature sequence through bidirectional LSTM to capture a time sequence change trend. By introducing the large-scale visual large model and spatio-temporal joint modeling, the identification capability of tiny defects is improved, the discrimination stability is enhanced, high-precision and low-delay automatic detection is realized, the false alarm rate and the omission ratio are remarkably reduced, and the detection efficiency and the system maintainability are improved.

The invention belongs to the technical field of unmanned aerial vehicles, and provides an omni-directional vision positioning system and method and an unmanned aerial vehicle, the omni-directional vision positioning system is installed on a fuselage of the unmanned aerial vehicle, and the omni-directional vision positioning system comprises an image acquisition device used for acquiring a real-time scene image in an omni-directional range in the flight process of the unmanned aerial vehicle; the inertial measurement sensor is connected with the image acquisition equipment and is used for synchronously acquiring motion state data of the unmanned aerial vehicle; the navigation system is connected with the image acquisition equipment and the inertial measurement sensor and is configured to execute at least one of distortion correction processing and feature extraction processing on the real-time scene image to obtain a processed image; and according to the motion state data and the processed image, determining current pose information of the unmanned aerial vehicle in a space coordinate system constructed by taking a take-off point of the unmanned aerial vehicle as an original point. The positioning precision and robustness of the unmanned aerial vehicle in a complex environment are improved, and the requirement of high-precision autonomous flight is met.

The invention relates to the technical field of vehicle vision calibration, and discloses a DMS camera calibration method based on computer vision. The method comprises the following steps: acquiring a multi-view original image sequence in a target vehicle cockpit, and synchronously acquiring a camera mounting position, a view angle, an inclination angle and an environment illumination intensity parameter; after distortion correction is carried out on the original image sequence, edge features of a calibration reference object in each frame of image are extracted through image recognition, and an initial calibration matrix containing a mapping relation between pixel coordinates and physical coordinates is generated; constructing a three-dimensional space projection model based on the initial calibration matrix, and adjusting rotation and translation parameters of the model by using an iterative optimization algorithm to enable the error between a virtual imaging position and an actual edge feature to be minimum; and extracting an internal parameter matrix and an external parameter matrix of the optimized model, fusing the internal parameter matrix and the external parameter matrix into a final calibration parameter, and writing the final calibration parameter into a DMS camera firmware storage area. According to the method, the DMS camera calibration accuracy and environmental adaptability are improved, and stable technical support is provided for driver state monitoring.

The invention relates to the technical field of product detection, in particular to a discrete manufacturing product defect AI visual detection method based on multi-modal fusion, which comprises the following steps: acquiring a visible light image and a thermal infrared image of a target product in time alignment, carrying out photometric distortion correction on the visible light image, carrying out radiation calibration correction on the thermal infrared image, and obtaining a target product defect AI visual detection result. Performing spatial registration on the visible light image after the luminosity distortion correction and the thermal infrared image after the radiation calibration correction to obtain a registered visible light image and a registered thermal infrared image; feature level fusion based on multi-level DWT and decision level fusion based on the DS evidence theory are carried out on the registered visible light image and the registered thermal infrared image, and a fused image is obtained; and inputting the fused image into a visual detection model running on an embedded AI computing platform, and obtaining a detection result of the target product output by the visual detection model. According to the method, the defect detection precision is improved, and a more reliable guarantee is provided for the discrete manufacturing industry.

The invention provides a motion blurred bar code identification method and system based on multi-frame image fusion, and the method comprises the steps: obtaining a continuous image sequence containing a motion blurred bar code, carrying out the motion track feature extraction of the continuous image sequence, and obtaining a motion vector field and a pixel displacement track set of a bar code region in each frame image unit; performing multi-frame image fusion on the continuous image sequence based on the motion vector field and the pixel displacement track set to generate a candidate bar code image set; performing deblurring enhancement processing on the candidate bar code image set to obtain a clear bar code image unit after deblurring processing; and performing bar code area positioning and distortion correction processing on the clear bar code image unit to generate a standardized bar code image, and performing identification to obtain an identification result. According to the invention, the accuracy and reliability of bar code identification in a motion blurred scene can be obviously improved.

The invention discloses an electronic component early fault detection method based on infrared thermal imaging, and the method comprises the following steps: collecting an infrared thermal imaging image, synchronously collecting current and voltage data, and obtaining an infrared thermal imaging sequence; distortion correction, noise suppression and radiance correction are carried out on the sequence, and metal reflection false high-temperature points are removed; executing time sequence difference to extract a continuous heating area, and generating a time sequence temperature feature vector; inputting the feature vector into an improved Times Net model, and outputting time sequence feature representation; calculating a reconstruction error, a prediction residual error and a frequency spectrum deviation, and fusing into an abnormal score; comparing the abnormal score with a dynamic threshold value, and outputting an electronic component identifier, a potential fault type and an abnormal type; and an early fault trend report is generated by combining historical data comparison. The method realizes early detection and trend prediction of electronic component faults, and is suitable for state monitoring and reliability guarantee of a complex circuit system.

The invention discloses a wide-angle camera distorted image correction method and device based on track visual detection. The method comprises the following steps: acquiring a track distorted image; the projection imaging intersection point between the sleeper and the steel rail is adopted to form a mark point; solving the distortion center of the camera by combining a distortion model according to the mapping relation between the three-dimensional world coordinate and the projection coordinate of the three-dimensional world coordinate in the distorted image; solving distortion parameters of a camera by combining a Levenberg-Marquardt algorithm according to the fact that blanking points formed by parallel steel rails in the image must be subjected to collinear constraint; re-projection correction is carried out on the mark points participating in calculation; screening out bad mark points and removing the bad mark points; the distortion center and the distortion parameters are calculated again until all the mark points meet the 3 sigma criterion; and according to the optimal distortion center and the distortion parameters, track image correction is carried out through a distortion model. According to the invention, high-efficiency and accurate distortion correction can be carried out on line without the participation of a special cooperative calibration object.

The invention relates to the technical field of landslide geological disaster hidden danger point identification, in particular to a landslide hidden danger point identification method based on an air-space remote sensing fusion neural network, which comprises the following steps: acquiring satellite SAR data, a digital elevation model, a vector boundary map and a precision orbit ephemeris of a research area; carrying out interference processing on the InSAR data, and carrying out deformation rate inversion, terrain residual error correction and atmospheric delay phase modeling and correction; preprocessing an original high-resolution remote sensing image, interpreting a high-resolution image, and outputting spatial distribution of suspected landslide hidden danger points in a research area; planning an aerial survey route of the unmanned aerial vehicle, and performing distortion correction, feature matching, dense point cloud generation and orthoimage generation processing; performing multi-dimensional feature analysis, and performing comprehensive interpretation in combination with an expert knowledge base; according to the method, the key effect of the multi-source remote sensing fusion technology in landslide hidden danger monitoring is displayed from data acquisition to hidden danger recognition.

The embodiment of the invention provides an optical measurement method and system based on camera calibration and a distortion model, and a medium. The method comprises the following steps: shooting a plurality of calibration images and distortion measurement images at different angles and positions based on a three-dimensional calibration workpiece; analyzing the calibration image and the distortion measurement image based on a calibration algorithm to obtain camera parameters and a distortion model; obtaining a to-be-measured object image based on the calibrated camera, and extracting features of the to-be-measured object image; performing distortion correction on the image features of the to-be-measured object based on a distortion model to obtain corrected image features of the to-be-measured object; performing three-dimensional reconstruction on the corrected image features of the to-be-measured object to obtain a measurement image, and analyzing parameter information of the to-be-measured object based on the measurement image to obtain a measurement result; parameter calibration and distortion correction are carried out on the camera, so that the distortion error is reduced, and the measurement precision is improved.

The invention discloses a railway comprehensive defect detection method and system based on multi-source sensor fusion. The method comprises the following steps: carrying out joint calibration on static external parameters by adopting a multi-modal calibration plate; a high-precision timestamp and vehicle multi-source motion parameters are utilized to construct a motion compensation model to carry out adaptive dynamic distortion correction calibration; synchronously acquiring multi-source sensor data, and based on static external parameters and adaptive dynamic distortion correction, obtaining a corrected visible light image, an infrared image and a laser radar three-dimensional point cloud; complementarity features are extracted from the data of the three modes, adaptive fusion and optimization are carried out through an attention mechanism, and multi-scale fusion features are output; segmenting different railway facility areas by using multi-scale fusion features, extracting corresponding area features, and calling detection algorithms of corresponding facilities to identify defects of different railway facilities; according to the method, high-precision calibration, feature-level fusion and railway facility special defect detection are fused, and the automation level and the detection accuracy of railway inspection are improved.

The invention relates to the technical field of circuit board detection, in particular to a quality detection system of an integrated circuit board, which comprises an image acquisition module used for cooperatively acquiring a surface image and a temperature distribution image of the integrated circuit board through a camera array and a multispectral light source; the preprocessing module is used for carrying out noise suppression, distortion correction and multispectral fusion on the surface image and the temperature distribution image and generating a standardized detection image; the defect detection module is used for extracting welding spot contour features, wire connectivity features and dielectric layer thickness features from the standardized detection image, the welding spot image and the inner layer wiring image of the integrated circuit board can be separated, interlayer interference is eliminated, multi-dimensional more accurate evaluation of defect risks is achieved, and the defect detection accuracy is improved. Misjudgment caused by traditional isolated analysis is avoided, the irradiation angle and intensity of the multispectral light source can be dynamically adjusted, and the reliability and efficiency of quality detection of the integrated circuit board are improved.

The invention provides an optical and acoustic image registration method for underwater structure crack detection, and the method comprises the steps: collecting a multi-mode image through the vision field and time synchronization, building a one-to-one correspondence relation according to the pose and time metadata, and improving the image quality through the denoising, distortion correction and other means; optical and acoustic multi-scale features are respectively extracted through a dual-channel network, and cross-modal semantic alignment is realized in combination with a shared weight and a channel attention mechanism; generating a high-resolution scale offset field by using local cross-correlation and micro-upsampling, and introducing manifold regularization constraint to ensure that a vector field is smooth and continuous; according to the method, the local scale field and the affine parameters are combined, non-rigid space mapping is achieved through thin-plate spline interpolation, feedback iterative optimization based on edge structure consistency is assisted, the registration precision of a key structure is enhanced, the automatic registration effect of the underwater multi-modal image is improved, and the method has high robustness and practical application value.

The invention discloses an environment modeling method, system and equipment based on laser radar and vision fusion, and belongs to the technical field of mobile robot environment modeling. Through synchronous acquisition of three-dimensional point cloud data and image data, feature extraction and distortion correction are completed on the data acquired through the two different ways; accurate modeling features can be obtained, a geometric pose state quantity and a luminosity pose state quantity are calculated through an implicit moving least square iterative nearest point algorithm, the calculation precision of a curved surface in an environment is improved, and through point cloud splicing, a two-dimensional grid map and a three-dimensional point cloud map are generated. The two-dimensional grid map and the three-dimensional point cloud map are fused to complete environment modeling, the modeling speed is increased, the environment object information is aligned, information dislocation is avoided, and the environment modeling efficiency and accuracy are improved.

The invention relates to the technical field of inductor winding monitoring, and discloses an inductor winding equipment abnormity intelligent monitoring system based on image processing. The system comprises an industrial image acquisition module, a real-time feature processing module, a defect identification module, a dynamic decision-making module and a data tracing module. The industrial image acquisition module acquires a multi-angle dynamic image through a high-frame-rate industrial camera and completes distortion correction and timestamp marking; the real-time feature processing module extracts the edge gradient, surface texture and geometric morphology features of the wire turn, and integrates the features into a multi-dimensional feature vector. And the defect identification module analyzes the feature vector through a pre-trained deep convolutional network, and outputs a form anomaly probability, a spacing deviation index and a surface defect level. The dynamic decision-making module generates a judgment result according to the judgment result and triggers grading alarm if the threshold value is exceeded; and the data tracing module is used for storing time sequence associated data and supporting abnormal process backtracking. The system realizes real-time accurate monitoring of winding abnormity, and helps to improve inductor production quality and flow stability.

The invention belongs to the field of instrument identification, and particularly relates to a pressure gauge identification method and equipment in an industrial valve pressure testing process. The method comprises the following steps: extracting an instrument image from a collected video stream, and carrying out distortion correction and illumination normalization processing; feature extraction is carried out on the processed image, instrument types are identified based on a training model of multiple types of instruments, and an instrument image with angle deformation is corrected through a spatial transformation network; performing spatial sorting on the identified instrument images according to the coordinates of the central point of the detection frame, and generating a data array containing instrument information; and finally, performing enhancement processing on a single instrument image in the data array, positioning a dial plate area, performing perspective correction, determining geometric parameters of the dial plate, calculating an original reading based on the parameters, and outputting a final reading in combination with inclination compensation. Through the image processing and recognition steps, accurate recognition and reading of the pressure gauge in the industrial valve pressure testing process are achieved, and the recognition efficiency and accuracy are improved.

The invention discloses a video transmission image stitching data enhancement method based on deep learning, and relates to the field of video image processing. The method comprises the following steps: S1, acquiring and screening images; s2, continuously screening structures and selecting key frames; s3, correcting image distortion; s4, splicing and fusing the images; s5, performing image enhancement output; firstly, a sliding time window mechanism is adopted, multi-dimensional image quality screening is combined, fuzzy, underexposure or severely-shielded inferior frames are accurately removed, and high quality of input key frames is ensured; through intelligent splicing and enhancement of a dynamic adaptive threshold strategy and semantic guidance, the image splicing precision and efficiency of the unmanned aerial vehicle and the multi-view camera in a complex environment are remarkably improved; besides, semantic segmentation guided feature extraction is combined with a multi-band fusion technology, seamless splicing is realized, the quality of an output image is improved, and the reliability of automatic analysis and decision making is remarkably improved.

The invention discloses an optical lens coating defect detection method based on image detection, and relates to the technical field of image processing, and the method comprises the steps: collecting a multi-illumination multi-band reflection image of a lens, carrying out dark white field and distortion correction to form a correction image, carrying out the segmentation and pose solving of the correction image, then carrying out the curved surface expansion remapping, and carrying out the image processing. A reflection observation sequence is formed according to channel stacking, a judgment threshold value is calculated based on the white field image and the dark field image, and pixel positions meeting the judgment threshold value are marked as low credibility and converged to generate a quality mask; coating reflection representation fitting is carried out on the reflection observation sequence, binary weighting is carried out according to a quality mask, range and continuity checking is carried out on a fitting result to obtain a credible mark, and an ideal reflection response is synthesized according to the credible mark. According to the method, ideal reflection response and residual error construction are performed on the reflection observation sequence, so that separation characterization of imaging disturbance and real coating abnormality is realized.

The invention discloses a structure three-dimensional displacement monitoring method and device based on a monocular camera and a concentric annulus, and relates to the field of computer vision and structure health monitoring, a concentric annulus target is fixed at a to-be-monitored structure monitoring point, and the monocular camera carries out positioning and lens parameter adjustment; shooting a checkerboard image and calibrating the checkerboard image by adopting a camera calibration algorithm to obtain a distortion parameter and an internal reference matrix; collecting a video sequence containing a structure displacement process of the target; image distortion correction is carried out based on the distortion parameters and the internal reference matrix, a target is identified through a target detection algorithm, and target parameters are calculated through least square circle fitting; realizing multi-target tracking by a target point topology identification algorithm based on Y-X threshold sorting; calculating scale factors according to target parameters and actual physical sizes, and calculating in-plane orthogonal direction displacement and out-of-plane displacement to form three-dimensional displacement information of the structure; according to the invention, the monocular camera is adopted to realize the accurate measurement of the three-dimensional displacement of the structure and reduce the complexity and calibration difficulty of the system.

The invention belongs to the field of quantum computing, and particularly relates to a distortion correction optimization method and device for a quantum measurement and control link, and the method comprises the steps: applying a frequency control signal for loading a target pre-distortion waveform on a to-be-corrected quantum measurement and control Z link; setting a first logic gate for quantum state operation on an XY control line of the selected quantum bit; obtaining the fidelity of the quantum state output by the selected quantum bit; in response to the fact that the fidelity does not reach the preset standard, updating the pre-distortion waveform, and returning to execute a frequency control signal for applying and loading the target pre-distortion waveform signal on the quantum measurement and control Z link to be corrected; and setting a first logic gate for quantum state operation on an XY control line of the selected quantum bit until the fidelity reaches a preset standard. According to the invention, distortion correction can be rapidly and effectively carried out.

The invention relates to a visual guidance robot automatic control method, system and device and a medium. The method comprises the following steps: firstly, acquiring a target image sequence and extracting initial visual features, and processing the initial visual features through a deep learning algorithm to output a high-resolution image; calculating a distance switching value based on the high-resolution image, and if the distance switching value exceeds a preset threshold value, performing scale distortion correction on the high-resolution image to obtain a target image; multiple frames of time sequence data are fused through a Kalman filtering algorithm, image distortion real-time compensation and feature point track stability judgment are synchronously completed, and a target positioning precision estimated value is output; and finally, according to the target positioning precision estimated value, determining the position of a grabbing point through a posture mapping rule of mapping preset grabbing feature points in a target coordinate system to a robot-based coordinate system, and based on the position of the grabbing point, optimizing and fusing track parameters through a Kalman filtering algorithm to generate adaptive track parameters. The method improves the positioning precision and track following effect of automatic control of the robot.

The invention relates to the technical field of wave-absorbing material quality detection, and discloses a pyramid wave-absorbing material coating quality online detection system based on machine vision, which comprises a conveying unit, a vision acquisition unit, an image preprocessing unit, a coating quality analysis unit, a result output unit and a feedback control unit. The coating quality analysis unit is internally provided with a pyramid surface image geometric correction core model, a coating thickness quantitative calculation core model and a coating uniformity and defect judgment core model, cooperation is achieved through a feedforward correction and feedback optimization linkage mechanism, and the visual collection unit achieves full-view coverage through three sets of industrial cameras. The image preprocessing unit reduces distortion correction errors through conical surface constraint iterative correction; the thickness calculation model is combined with quadratic polynomial fitting and temperature compensation; the defect discrimination model fuses multiple parameters and comprehensive deviation values, and accurately recognizes three types of defects. According to the system, production parameters can be dynamically adjusted, the detection real-time performance and the quality traceability are both considered, and the large-scale production quality control requirement is met.

The invention provides a method and a device for mapping image coordinates to desktop coordinates, equipment and a storage medium, which are used for improving the positioning precision of a projection touch system in a real environment. The method comprises the steps of performing distortion correction on original gesture video data according to internal parameters of a camera and a lens distortion coefficient to obtain a corrected image frame sequence, and performing interaction space calibration according to the corrected image frame sequence to obtain a distortionless interaction plane, performing perspective change matrix solving on the distortionless interaction plane according to the target vertex coordinate set to obtain orthogonal mapping data, and when gesture key points are detected in the distortionless interaction plane, performing perspective transformation and normalization processing on gesture coordinates of the gesture key points according to the orthogonal mapping data to generate initial desktop control coordinates; and performing display mapping processing on the initial desktop control coordinates according to the physical attribute parameters of the target display to obtain desktop control coordinates.

The invention relates to the technical field of optical precision interference measurement, in particular to a correction method for projection distortion detection of a primary mirror of a dynamic interferometer and a related product, and the method comprises the steps: obtaining a first coordinate and a second coordinate; determining a zero distortion point; obtaining an ideal imaging point set and an actual mapping point set after system projection through ray tracing; fitting to obtain a distortion function representing the distortion of the optical system; performing point-by-point correction on a phase diagram obtained by interference measurement, mapping pixel coordinates of the phase diagram into workpiece coordinates, obtaining interference measurement data corresponding to the actual surface of the primary mirror, and completing distortion correction; according to the method for correcting the surface shape projection distortion during interference measurement of the high-precision primary mirror of the large-aperture dynamic interferometer, the problem that in the process of assembling, repairing and developing the large-aperture Twyman type dynamic interferometer, the projection distortion effect of a detection system causes the loss of an accurate matching relation between a workpiece coordinate system and a detection coordinate system is solved.

The invention relates to the technical field of computer vision and industrial automation, and particularly discloses a MARK point high-precision PCB positioning and mechanical mapping method based on multi-coordinate system fusion, which is composed of PCB positioning and mechanical mapping in a template stage and PCB positioning and mechanical mapping in a production stage. Wherein the PCB positioning and mechanical mapping in the template stage comprises the steps of coordinate system establishment and image coordinate distortion correction; the PCB positioning and mechanical mapping in the production stage comprises Mark point real-time detection and offset calculation, real-time attitude solution and dynamic matrix calculation, and mechanical coordinate calculation of any PCB point. According to the method, a three-coordinate system collaborative modeling mechanism of an image coordinate system, a PCB coordinate system and a mechanical coordinate system is introduced, so that board attitude change and equipment fixing errors are separated, and the system robustness and the mapping precision are greatly improved; rigid body affine transformation is constructed by adopting double Mark points, the rotation angle, the proportion and the translation amount are solved in real time through two-point geometric constraint, and self-adaptive compensation of the posture change of the PCB is achieved.

The invention relates to the technical field of augmented reality glasses optical correction, and specifically provides an eye movement distortion detection method. The method comprises the following steps: acquiring an AR glasses playing image and identifying a visual saliency region to establish an attention priority order; detecting an eyeball movement track direction vector and an instantaneous speed in real time; determining a predicted attention target according to the motion direction and the attention priority; generating a distortion correction parameter set in combination with eyeball arrival time and spatial distribution information; performing pre-distortion compensation rendering on the candidate area in a pre-generation period before eyeballs reach the actual attention area to form a pre-corrected image buffer area; and finally, outputting a pre-corrected image matched with the actual stay area. Space-time synchronization of eyeball movement and image compensation is achieved through predictive rendering, the image distortion and color separation phenomena in the glancing process are eliminated while the low-delay characteristic is maintained, and the visual definition and comfort in a dynamic scene are remarkably improved.